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Obesity Stigma, Evolution, andDevelopment

PAUL A. KLACZYNSKI

Department of Psychological Sciences, University of

Northern Colorado, Greeley, CO, USA

SynonymsCorpulent; Fat; Flabby; Heavyweight

DefinitionObesity is excessive body fat (percent weight due to

fat), indicated in women by�32% body fat and in men

by �25% body fat. A body mass index score �30 is

often used to operationally define obesity.

Stigmas are physical (e.g., mephitic smelling, phys-

ical abnormalities) or verbal (e.g., convict, addict,

“untouchable”) signifiers of physical or psychological

defects and signify that individuals should be avoided

or shunned.

Stereotypes are beliefs about the behaviors and per-

sonalities of individuals who belong to social groups

(e.g., racial, religious, gangs) and provide a basis for

categorizing, forming impressions of, and making

inferences about individual group members.

Theoretical BackgroundThe obesity stigma is distinctive for several reasons.

First, children and adults explicitly tease, marginalize,

dislike, and discriminate against the obese more than

most other social groups; unlike other physical condi-

tions, obesity does not typically elicit empathy. Second,

obesity biases are evident by 3 years of age and, unlike

other appearance-based stereotypes (e.g., racial), obe-

sity stereotypes strengthen from childhood through

adolescence. Third, despite modest cultural and ethnic

differences in obesity biases, children and adults in

most cultures stigmatize the obese. Finally, obese

N. Seel (ed.), Encyclopedia of the Sciences of Learning, DOI 10.1007/978-1-441# Springer Science+Business Media, LLC 2012

women are treated more negatively and more fre-

quently experience the psychosocial consequences of

obesity than obese men (Brownell et al. 2005).

Psychosocial correlates of obesity. Obesity biases

include negative emotional responses, derogatory per-

sonality assumptions, disparaging comments, and dis-

criminatory behaviors. Obese children and adults are

disliked, teased, shamed, and marginalized more, and

have fewer friends than, their average-weight counter-

parts. Obese adolescents and adults date less often, have

less satisfactory sexual relationships, receive less social

support, and face more discrimination than thinner

people. The sense of estrangement, alienation, isola-

tion, and dehumanization that characterizes the “psy-

chosocial climate” of obesity places the obese at risk for

numerous psychological difficulties. Relative to aver-

age-weight individuals, obese adolescents and adults

experience more anxiety, loneliness, and body dissatis-

faction, and are more prone to low self-esteem depres-

sion, poor academic performance, suicidal ideations,

and pessimistic perceptions of social and occupational

opportunities (Schwartz and Puhl 2003).

These findings, coupled with worldwide increases

in obesity, highlight the need to understand why obese

children and adults are treated as social outcasts. The

majority of relevant research is based on attribution

theories or “pathogen avoidance” theories of obesity

stigmatization. However, neither theoretic perspective

has the scope or precision necessary to fully explain the

obesity stigma and its development. Although

a comprehensive explanation will likely require ele-

ments of both theories, current formulations of these

theories rest on incompatible assumptions. Conse-

quently, theoretical integration will depend on

revisiting and revising these assumptions.

Attribution TheoryAttribution theory assumes that people construct intu-

itive or “folk theories” to explain and predict the

actions of individuals and groups. Adults in Western

9-1428-6,

2488 O Obesity Stigma, Evolution, and Development

societies recognize that both situational and disposi-

tional variables affect behavior, but typically emphasize

personal qualities and traits as the predominant causes

of others’ actions. Evidence that attributions are biased

by outcomes (positive vs. negative), affect (like/dislike

for actors), and group membership (e.g., in- vs. out-

group membership) highlights the effects of attribu-

tions of stereotype formation, social inferences, and

impression formation. For example, stereotypes can

be formed and maintained by attributing an individ-

ual’s actions to internal qualities and generalizing those

qualities to a group associated with the individual.

Group prototypes often provide a basis for determin-

ing whether to attribute a member’s behaviors to dis-

positions or situational factors.

Numerous theorists have relied on attribution the-

ory to explain the obesity stereotype. Indeed, attribu-

tion theory has long been the preeminent explanation

for the negative perceptions of the obese. A key tenet of

attribution theories of the obesity stereotype is that, if

individuals’ appearance deviates from cultural proto-

types and those deviations can be attributed to uncon-

trollable (i.e., environmental, hormonal, genetic)

causes, those individuals will not be stereotyped as

negatively as individuals who can presumably control

their appearance. Unlike physical disabilities and cer-

tain characteristics of ethnic minorities (e.g., nose

shape, hair texture, skin color), obesity is a deviation

from the dominant culture’s attractiveness ideals that

can be attributed to characterological flaws.

An ancillary hypothesis of attribution theory is that

the obese should be more negatively stereotyped in

individualistic than in collectivistic cultures. Western

cultures emphasize independence, personal responsi-

bility, and autonomy: Across life domains (e.g., mar-

riage, vocation), success results from personal effort,

skills, fortitude, and persistence. Failure is attributed to

the absence or underutilization of these qualities. Just

as individuals are responsible for occupational fail-

ures, obese people are personally accountable for

their “appearance failures.” The obese are perceived

as capable of controlling their appearance, but lack

the requisite characteristics (e.g., drive to achieve, per-

sistence) to enact change. Because the dispositions

apparently lacking in obese individuals are in direct

opposition to those most valued in individualistic

societies and because thinness (especially for girls

and women) is linked more closely to beauty and

success than in the past, the obese are particularly

vulnerable to stigmatization.

Attribution theory has been supported by experi-

mental, cross-cultural, and developmental research. In

both collectivistic and individualistic cultures, “lay”

dispositional theorists perceive the obese more nega-

tively than situational theorists. However, as antici-

pated by attribution theory, the relationship between

dispositional attributions and obesity stereotypes is

especially pronounced in cultures that value individu-

alism and glorify individuals whose achievements can

be attributed to hard work and persistence. Correla-

tional research also indicates that preschooler and ado-

lescent “dispositional theorists” express more obesity

biases than same-aged situational theorists. Conversely,

manipulations that focus attention on uncontrollable

causes (e.g., hormonal imbalances) reduce the negativ-

ity of obesity stereotypes in children and adults.

Despite this supportive evidence, attribution theory

does not address findings that (1) adults maintainmore

physical distance from obese individuals than average-

weight individuals, (2) obese women and girls are per-

ceived and treated more negatively than obese men and

boys, (3) across several ethnic groups, obesity stigma-

tization increases with age, and (4) to varying degrees,

preschoolers, older children, and adults in individual-

istic and collectivistic cultures stigmatize the obese and

avoid individuals and objects associated with obese

people (Klaczynski 2008).

Evolutionary TheoryThe “pathogen avoidance” explanation of stigmatiza-

tion assumes that individuals whose appearance or

behavior deviates from species-typical norms activate

a preconscious parasite detection system. Pathogen

avoidance theories hinge on the assumption, derived

from evolutionary psychology, that survival is partially

dependent on detecting cues that objects and organ-

isms carry contagious pathogens (Oaten et al. 2009).

Although pathogens are not visible, pathogens can

cause unusual behaviors (e.g., ticks) and significant

deviations (e.g., lesions) from prototypically healthy

appearance. The “pathogen detection” system is acti-

vated by symptoms that correlate with infectious ill-

nesses and triggers negative emotional responses (e.g.,

anxiety, disgust). Individuals manifesting such symp-

toms should elicit these emotions from others who, in

turn, should attempt to avoid the apparent carrier.

Obesity Stigma, Evolution, and Development O 2489

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A cognitive-perceptual pathogen detection system

would decrease the probability of contamination, ill-

ness, and death. An appreciation for the conditions that

shaped the evolution of this system may help under-

stand why obesity is stigmatized. Early humans under-

stood neither the nature nor the origins of illnesses;

consequently, a pathogen detection system that erred

on the side of caution would be adaptive. This system-

atic bias toward false positives – disease-free people

who have appearance-related characteristics that cor-

relate with the symptoms of illnesses – would involve

reacting to these individuals as though they were car-

riers. The costs of false positive biases would outweigh

the cost of false negatives (failures to detect real path-

ogen carriers). Early social groups would benefit from

a cautious system because contracting illnesses, even

those considered minor today, could lead to the deaths

of individuals and entire groups. A system that detects

pathogenic conditions and covariates of these condi-

tions would elicit emotions (e.g., fear, disgust, revul-

sion) that motivate behaviors intended to increase the

physical distance from potential carriers.

The price of false positives is stigmatizing individ-

uals who pose no more pathogen threats than morpho-

logically typical individuals. By excluding these

individuals, early groups reduced numbers of potential

mating partners and their capacity to ward off other

threats (e.g., predators, other groups). An overly sensi-

tive system could also lead to phobias, chronic anxiety,

fear of novelty/exploration, and obsessive behaviors

(e.g., obsessive-compulsive disorder is, in fact, related

to atypical disgust sensitivity). Nonetheless, such costs

were small relative to those of false positives, particu-

larly if an additional cost of chronic exposure to dis-

gust-eliciting stimuli and potential disease carriers was

depletion of emotional and physical energy.

In recent years, support for pathogen-avoidance the-

ories of stigmatization has increased (Oaten et al. 2009).

Animate and inanimate disease carriers, and objects

and individuals associated with potential disease car-

riers, elicit disgust and avoidance (a) from children and

adults in different cultures and (b) even after potential

carriers have been disinfected. False positives have been

reported for a several appearance-related conditions

(e.g., psoriasis, facial asymmetries), behaviors (e.g.,

indicative of mental illness), and diseases (e.g., cancer).

However, because the relevance of pathogen-avoidance

theories to obesity has been questioned (Kurzban and

Leary 2001), advocates of this approach to explaining

the obesity stigma should clarify why obesity activates

the pathogen-detection system.

This question can be answered with arguments

derived from evolutionary psychology and by drawing

on empirical evidence. First, humans evolved in a food-

scarce environment of human evolution. Obesity, if it

occurred at all, would have been a rare, but obvious,

deviation from morphological norms. Second, obese

individuals are prone to illness, detrimental physical

conditions (e.g., orthopedic problems, Type II diabe-

tes), and reproductive difficulties (e.g., miscarriages).

Third, the “symptoms” of obesity parallel symptoms

associated with some contagious illnesses. Compared

to average-weight people, the obese are more prone to

lethargy, skin rashes, diarrhea, bad breath, facial red-

dening, breathlessness, profuse sweating, unpleasant

body odor, and disproportionate facial features.

Research with children, adolescents, and adults

supports pathogen avoidance theory. For example,

children have “illness theories” theories from which

they generate hypotheses and explanations for sickness.

Preschoolers have a rudimentary understanding that

illnesses can be contagious, contracted by contact with

and proximity to contaminated objects, and potential

pathogen carriers should be avoided. Children and

adults often overgeneralize correct conceptions of ill-

nesses to people and situations to which they do not

apply. Because the potential benefits of false alarms

outweigh those of false negatives, overgeneralizations

based on feature-based similarities between unknown

people and known carriers have been labeled, “pseudo-

rational” (Klaczynski 2008).

Priming studies provide stronger, more direct sup-

port for a pathogen avoidance mechanism. Adults per-

ceive obese individuals more negatively when

contagion beliefs are primed and implicitly associate

signifiers of diseases with obesity (Park et al. 2007).

Adults, preschoolers, and school-age children dislike

and rate negatively individuals who have been in the

physical proximity of obese individuals. Particularly

when primed link ingestion to contagious illnesses,

Chinese and American children believe they are more

likely to get sick from, and have better memory for,

ingestible items associated with obese children than

identical items associated with amputee, physically dis-

abled, and average-weight children. Only items linked

to children with contagious diseases elicit similar

2490 O Object Memory

reactions (Klaczynski 2008). Extant data thus indicate

that obese people, and people with whom and objects

with which obese individuals have been associated,

elicit responses indicative of disgust and avoidance

(Oaten et al. 2009).

Important Scientific Research andOpen QuestionsIn different ways, extant evidence supports both attri-

bution theory and pathogen-avoidance theories of obe-

sity stigmatization. However, several methodological

issues must be addressed before competing predictions

can be examined. First, the variety of methods – inter-

views, questionnaires, drawings, and photographs – used

to assess reactions to obesitymake between-investigation

comparisons difficult. Second, whether age, culture,

ethnic, or gender differences exist in categorizations

of (objectively) obese individuals has not been

investigated. Individual/group differences in categori-

zations of, for instance, obese individuals as over-

weight (or vice versa) or subjective perceptions of

obesity that deviate from objective criteria would

necessitate reexamination of numerous findings.

Third, although implicit measures of obesity stereo-

types and attitudes are theoretically appealing, reports

that implicit and explicit measures sometimes corre-

late and that implicit measures predict behavior

inconsistently have raised questions about the validity

of the most common implicit tests.

Finally, theorists need to detail more precisely

findings that would support attribution theory, patho-

gen-avoidance theory, or both theories. Whereas

pathogen-avoidance theory may provide a stronger

basis for explaining how individuals initially respond

to obese people and why the obese are stigmatized,

attribution theory may better explain the maintenance

of and developments in the obesity stereotype and

stigma. Nonetheless, both theories must more carefully

explicate developmental hypotheses and, critically,

hypotheses regarding the age at which children first

evince obesity aversion. Findings that children react

to obesity with disgust before they can make disposi-

tional attributions to others would pose considerable

challenges to attribution theories.

Cross-References▶Attitude(s) – Formation and Change

▶Biological and Evolutionary Constraints of Learning

▶Dual Process Models of Information Processing

▶ Folk Psychology About Others’ Mind and Learning

▶ Learning (and Evolution) of Social Norms

▶ Learning the Affective Value of Others

▶ Social-Cultural Learning

ReferencesBrownell, K. D., Puhl, R. M., Schwartz, M. B., & Rudd, L. (Eds.).

(2005). Weight bias: Nature, consequences, and remedies.

New York: Guilford.

Klaczynski, P. A. (2008). There’s something about obesity: Culture,

contagion, rationality, and children’s responses to drinks “cre-

ated” by obese children. Journal of Experimental Child Psychology,

99, 58–74.

Kurzban, R., & Leary, M. R. (2001). Evolutionary origins of stigma-

tization: The functions of social exclusion. Psychological Bulletin,

127, 187–208.

Oaten, M., Stevenson, R. J., & Case, T. I. (2009). Disgust as a disease-

avoidance mechanism. Psychological Bulletin, 135, 303–321.

Park, J. H., Schaller, M., & Crandall, C. S. (2007). Pathogen-avoidance

mechanisms and the stigmatization of obese people. Evolution

and Human Behavior, 28, 410–414.

Schwartz, M. B., & Puhl, R. (2003). Childhood obesity: A societal

problem to solve. Obesity Reviews, 4, 57–71.

Object Memory

▶Memory for “What,” “Where,” and “When” Infor-

mation in Animals

Object Quality Learning Set

▶ Learning Set Formation and Conceptualization

Object-Based Learning

▶Context-Based Learning

Obligatory Education

▶Compulsory Education and Learning

Observational Learning of Complex Motor Skills: Dance O 2491

Obliterative Subsumption

Meaningfully learned material cannot be recalled in the

precise form in which it was originally learned due to

the subsumption of the new material under a broader

class or concept.

Observation of Animals andHuman Learning

▶ Learning from Animals

Observational Inference

▶Observational Learning: The Sound of Silence

O

Observational Learning

▶ Field Research on Learning

▶ Imitation: Definitions, Evidence, and Mechanisms

▶ Imitative Learning in Humans and Animals

▶Observational Learning of Complex Motor Skills:

Dance

▶ Social Cognitive Learning

▶ Social Learning

▶ Social Learning in Animals

▶ Social Learning Theory

Observational Learning ofComplex Motor Skills: Dance

EMILY S. CROSS

Department of Psychology, Max Planck Institute for

Human Cognitive & Brain Sciences, Leipzig, Germany

Department of Social & Cultural Psychology, Radboud

University Nijmegen, Nijmegen, The Netherlands

SynonymsObservational learning

DefinitionThe ability to learn new actions through observation is

a ubiquitous feature of human behavior. Whether one

is attempting to learn how to put a saddle on a horse or

to dance the Irish Jig, observation, along with physical

practice, is key for effective task learning. Decades of

behavioral research have provided clues that observa-

tional learning shares common cognitive and neuro-

physiological underpinnings with physical learning.

Recent advances in human neuroimaging techniques

are enabling scientists to directly quantify the brain

systems underlying skill learning. Research paradigms

investigating dance learning via physical practice or

observation reveal common neural processes underly-

ing both types of learning.

Theoretical BackgroundWhen we learn to walk, use a fork, or drive a car, we

learn by first observing others do the task, and then

practicing it ourselves. A wealth of research has dem-

onstrated that not only is observation helpful for learn-

ing, but that physical practice is more beneficial when

paired with observation of new movements (Hodges

et al. 2007). Behavioral research on action learning

suggests simultaneously observing and reproducing

the correct pattern of movements, results in the

quickest and most accurate learning (e.g., Bandura

1977). Nevertheless, the ability to learn or improve

task performance by observation alone, without con-

current physical practice, is a powerful capacity of

humans.

Early behavioral investigations of observational

learning by Sheffield (1961) led to the proposal that

observation of a motor sequence improved learning by

means of providing a “perceptual blueprint,” or

a standard of reference for how the task should be

performed. Behavioral studies comparing observa-

tional and physical learning support the value of

a perceptual blueprint. While the bulk of observational

learning research has focused on learning from an

expert human model, the use of a human actor

performing the target behavior is not a requirement

for forming a perceptual blueprint (Hodges et al. 2007).

A more inclusive conceptualization of observational

learning encompasses encoding any instruction,

whether physical or symbolic, that can provide

a sufficient model of the to-be-performed actions.

The key distinction of what defines observational

2492 O Observational Learning of Complex Motor Skills: Dance

learning is not the type of instruction, per se. Rather,

pure observational learning is defined as the subject not

concurrently performing physical practice at the time

the observational instructions are provided.

One of the primary theories why observational and

physical learning have so much overlap is that they

both engage similar cognitive processes (Bandura

1977; Hodges et al. 2007). However, as researchers in

this field are quick to point out, such findings do not

mean that physical and observational learning are iden-

tical cognitive processes; particular features remain

unique to each kind of learning, but the common

ground shared by these two types of learning might

provide insights into how we are able to learn from

both kinds of instruction. Contemporary research on

observational learning thus attempts to more fully

characterize points of overlap and divergence between

physical and observational learning.

While the wealth of behavioral research on obser-

vational learning provides a foundation for exploring

the comparative effectiveness and mechanisms under-

lying this kind of learning, it is difficult to determine

from behavioral procedures alone the degree of corre-

spondence between cognitive and neural processes

serving these two types of learning. With the advent

of functional neuroimaging techniques, including

functional magnetic resonance imaging (fMRI), scien-

tists are making significant advances in understanding

how both types of learning imprint the brain and

behavior by determining whether observational and

physical learning modify similar or distinct neural

regions. If both types of learning engage the same

areas of the brain, then it seems plausible that both

observational and physical learning engage comparable

cognitive processes. Conversely, the emergence of dif-

ferent areas of neural activity based on learning would

be more indicative of distinct cognitive processes

underpinning these two types of learning.

Important Scientific Research andOpen QuestionsRecently, several research laboratories have endeavored

to evaluate and compare brain and behavioral

responses during observational and physical learning

of complex motor skills, such as those required to

dance. These experiments identify a distinct set of

brain regions that are active both when observing and

when performing actions, including bilateral premotor

and parietal cortices, collectively referred to as the

“mirror neuron system” (MNS). The MNS encom-

passes a network of neural regions involved in visual

analysis of action as well as areas involved in visuo-

motor and action sequence performance. As such, this

system is thought to be a plausible mechanism

supporting new action learning, as it is responsive to

physical and observational experience for a broad range

of actions, spanning from simple keypress sequences to

far more complex skills such as playing basketball,

juggling, and dancing (for a review, please see Rizzolatti

and Sinigaglia 2010).

Two studies in particular have demonstrated the

feasibility of using dance learning and observation as

a paradigm for investigating observational compared

to physical learning of complex action sequences. In

one study, the authors evaluated the influence of obser-

vational compared to physical experience on neural

responses engaged while watching ballet movements

(Calvo-Merino et al. 2006). In order to parse visual

familiarity from physical experience, expert men and

women ballet dancers observed videos of movements

learned only by their sex, only by the opposite sex, or

moves that are performed by all dancers. The motiva-

tion behind this procedure was to determine whether

equally robust action resonance processes may be

elicited by observation of movements that are equally

visually familiar (because men and women dancers

train together), but unequal in terms of physical expe-

rience. The authors reported that when effects of visual

familiarity were controlled for, evidence for action res-

onance based on pure motor experience was found in

MNS regions (inferior parietal and premotor cortices),

as well as cerebellar cortices. This study provided initial

evidence that observational and physical experience

can imprint the brain differently, but this evidence is

tempered by the fact that dancers’ observational expe-

rience was not precisely controlled, and no measures of

physical competence for observed movements were

recorded.

A subsequent study addressed some of these issues

by investigating whether, under certain situations,

observational learning alone can lead to changes within

the MNS, thus broadening the capacity of this partic-

ular brain system to include learning from pure obser-

vation (Cross et al. 2009). In this study, novice dancers

physically rehearsed one group of simple dance

sequences in a video game context, and passively

Observational Learning: The Sound of Silence O 2493

O

observed a distinct set of simple dance sequences. fMRI

measures taken immediately before and after a week of

training revealed that a subset of MNS regions showed

comparable neural responses after physical and obser-

vational experience. The neural responses to physical

and observational experience were more robust than

responses measured while novice dancers observed

comparable untrained dance sequences whilst under-

going fMRI. Moreover, participants performed physi-

cally practiced and observed dance sequences more

accurately than untrained sequences. Considered

together, the imaging analyses from this study suggest

that among this sample of novice dancers, physical and

observational learning share more commonalities than

differences at a neural level. The converging evidence

from the behavioral and neural measures serves to link

the rich history of behavioral research on observational

learning with the burgeoning field of neuroimaging

inquiry into action cognition.

A tentative conclusion that can be drawn from this

experiment on observational learning of dance is that

we can learn to dance through observation using the

same brain systems that are involved when physically

practicing dance. In the (Cross et al. 2009) study, it is

noteworthy that such clear evidence emerged for obser-

vational learning in light of the fact that participants

were never explicitly told to try and learn the sequences

they watched each training day. Evidence from other

studies suggests that the amount of observational

learning can be markedly increased if participants are

explicitly instructed to try and learn the information

they observe during the training procedures (e.g.,

Hodges et al. 2007). At present, a great need exists for

future research to explore the different parameters that

might influence observational learning at brain and

behavioral levels, including motivation to learn,

which part of the model provides the most information

for learning a new skill, and how different kinds of

instructions might influence observational learning.

Such research should shed light on how educators

and those involved in rehabilitating individuals recov-

ering from neurological or physical injury might be

able to capitalize upon the brain and body’s inbuilt

mechanisms for learning effectively from observation.

Cross-References▶Action Learning

▶ Imitation: Definitions, Evidence and Mechanisms

▶ Learning as a Side Effect

▶Motor Learning

▶Neurophysiological Correlates of Learning to Dance

▶Robot Learning from Demonstration

ReferencesBandura, A. (1977). Social learning theory. Englewood Cliffs: Prentice-

Hall.

Calvo-Merino, B., Grezes, J., Glaser, D. E., Passingham, R. E., & Hag-

gard, P. (2006). Seeing or doing? Influence of visual and motor

familiarity in action observation. Current Biology, 16(19), 1905–

1910.

Cross, E. S., Kraemer, D. J., Hamilton, A. F., Kelley, W. M., & Grafton,

S. T. (2009). Sensitivity of the action observation network to

physical and observational learning. Cerebral Cortex, 19(3),

315–326.

Hodges, N. J., Williams, A. M., Hayes, S. J., & Breslin, G. (2007).What

is modelled during observational learning? Journal of Sports

Sciences, 25(5), 531–545.

Rizzolatti, G., & Sinigaglia, C. (2010). The functional role of the

parieto-frontal mirror circuit: Interpretations and misinterpre-

tations. Nature Reviews Neuroscience, 11, 264–274.

Sheffield, F. D. (1961). Theoretical consideration in the learning of

complex sequential task from demonstration and practice. In

A. A. Lumsdaine (Ed.), Student response in programmed instruc-

tion. Washington, DC: National Academy of Sciences – National

Research Council.

Observational Learning:The Sound of Silence

JUANJUAN ZHANG

Sloan School of Management, Massachusetts Institute

of Technology, Cambridge, MA, USA

SynonymsObservational inference; Social learning

DefinitionObservational learning refers to the process by which

decision-makers learn about the quality of available

choice options by observing the choices made by others

who have faced the same decision. The basic premise of

observational learning is that different people have

different private information which is revealed by

their actual choices. The observation of others’ choices

thus allows an observational learner to update her

2494 O Observational Learning: The Sound of Silence

knowledge about the choice options through rational

Bayesian updating. The Sound of Silence, the title of

a 1960s song by Paul Simon and Art Garfunkel, figura-

tively describes the impact on observational learners’

quality inferences if previous decision-makers choose

not to select a particular option, thus keeping the

option “silent.” This impact tends to be self-reinforcing

should decision-makers share the same preferences

regarding quality.

Theoretical BackgroundHuman decisions are often made in a social environ-

ment. The mere observation of others’ choices can

influence a person’s decision, even if these decision-

makers remain total strangers. For instance, people

tend to associate the sight of a long line waiting outside

a restaurant with high-quality food or service, even

without knowing the people in the line, or directly

soliciting these people’s opinions of the restaurant.

Social scientists have long recognized the power of

social influence on human behavior (see Chamley 2004

for a review). This influence has traditionally been

interpreted as an irrational tendency exhibited by

human beings, in a manner similar to how animals

swarm popular territories. It is seen as irrational partly

because mass behaviors are often erroneous. However,

the 1990s witnessed the rise of the view that social

influence can be decomposed into a series of micro-

processes by which individuals draw rational inferences

from others’ behaviors. The foundational theoretical

analyses of Banerjee (1992) and Bikhchandani,

Hirshleifer, and Welch (1992) demonstrate that indi-

vidually rational observational learning might produce

uniform social behaviors that are irrational – humans

imitate because others’ behaviors are genuinely infor-

mative; however, the act of imitation itself might sub-

sequently result in a loss of information for people who

only observe, and rationally look to learn, from these

imitators (an effect called herding externality).

Over the past 2 decades, observational learning has

attracted extensive research in a variety of social science

disciplines, including economics, finance, and market-

ing. Theoretical models of observational learning have

been extended to capture the complexity of the human

decision process, the decision environment, and the

incentives governing these decisions. Empirical evi-

dence of observational learning has been widely

documented through laboratory experiments with

human subjects, field experiments with uninformed

human participants, and natural experiments in

which subjects are naturally exposed to different

regimes of observational learning.

One recent empirical study explicitly examines the

sound of silence effect of observational learning (Zhang

2010). The empirical setting parallels the classic envi-

ronment for observational learning to occur: In the

United States, renal-disease patients who need kidney

transplantation enter a national waiting list. Once

a kidney is procured, compatible patients sequentially

decide whether to accept it for surgery. Imagine that the

first patient has made her decision, yet the kidney

“remains silent” without being adopted. The second

patient is likely to evaluate the kidney more negatively,

reasoning that the first patient (and perhaps her doc-

tor) might have found the quality of the organ unsat-

isfactory. The second patient’s added reservation

further aggravates the silence and worsens the third

patient’s doubt – the collective silence could be so

compelling that the third patient decides against the

kidney even though her private inspection is favorable.

Eventually, as the silence grows along the queue,

even medically viable kidneys might be repeatedly

turned down.

The sound of silence effect prevails in various

aspects of life. For example, real estate properties with

a long “time on market” are often hard to sell, workers

with an episode of unemployment tend to experience

difficulties landing a new job, and movies opening on

a quiet weekend are likely to dwindle in further obscu-

rity. Even though home buyers, employers, and mov-

iegoers are individually doing the right thing by

interpreting the silence as lower real estate value,

worker capability, andmovie quality, the herding exter-

nality may lead to overinterpretation of the silence,

potentially causing good homes, qualified workers,

and excellent movies to suffer from an initial lack

of luck.

Because of the importance of the initial luck, to

“break the silence,” conventional wisdom has empha-

sized first impression management. For example, it has

been a recommended business strategy to accelerate

product adoption by offering low introductory prices.

However, recent research in observational learning

advocates a seemingly opposite demarekting theory

(Miklos-Thal and Zhang 2010). This theory draws

attention to the visibility of first impression

Observational Learning: The Sound of Silence O 2495

management activities, such as product marketing

efforts. If these efforts are visible to consumers who

engage in observational learning, there are two

countervailing effects: although intensive marketing

might enhance sales, any lukewarm market response

in spite of heavy marketing will signal low product

quality. It is worth noting that the latter effect would

have been inconsequential if consumers were irratio-

nally herding – they would have simply chased popu-

larity without questioning whether popularity had

been driven by intrinsic quality or external marketing

efforts. This distinction exemplifies the need to under-

stand the precise mechanism by which humans make

decisions.

Observational Learning: The Sound of Silence. Table 1

Mechanisms underlying correlated social behaviors and

potential managerial priorities

Mechanism Potential managerial priorities

Correlatedpreferences

Change consumer preferences(e.g., through persuasive advertising);segment the market to targetconsumers who exhibit morefavorable preferences

Correlatedknowledge

Improve the intrinsic quality of theproduct

Correlatedcontexts

Increase demand-enhancingmarketing efforts (e.g., awarenessadvertising)

Correlatedpayoffs

Incentivize early adoption

Preference forconformity

Expand market share; improve brandimage

Irrationalherding

Incentive early adoption; enhanceproduct salience

Observationallearning

Incentive early adoption (with the“demarketing” caveat); managemarginal consumers

O

Important Scientific Research andOpen QuestionsKnowing what market forces drive observed market

outcomes, or mechanism identification, is an impor-

tant topic for empirical studies of observational learn-

ing. Although observational learning often implies

socially correlated choices, establishing the reverse

causal relationship requires more detailed analysis.

For example, a group of individuals may remain col-

lectively silent about a product for the following

reasons:

● Correlated preferences: The silence could simply

represent a common distaste for the product.

For instance, if all individuals are tradition bound,

they may be uniformly reluctant to adopt new

products, independent of what they know about

product quality.

● Correlated knowledge: There may be product char-

acteristics (e.g., defects) that all individuals are

commonly aware of.

● Correlated contexts: There may be contextual factors

that compel a set of individuals to make the same

choices, such as the lack of promotional efforts

within the same geographic area.

● Correlated payoffs: The silence could be an equilib-

rium market outcome if the value of the product to

one individual depends on how many others have

adopted the same product – one example being the

adoption of telephones.

● Preference for conformity: Individuals may derive

psychological utilities from conformist behaviors;

alternatively, they may want to identify with a social

group and convey this social identity by taking the

same action as member of the desired social group.

● Irrational herding: Individuals may simply mimic

others’ choices as a decision heuristic, or gravitate

toward popular and salient choices.

● Observational learning: Individuals infer from the

silence of a product that others are privately aware

of some product defects.

Mechanism identification is important for funda-

mental inquiries of human behaviors, for policy

makers who wish to improve the welfare consequences

of societal choices, and for businesses that aim to guide

consumer decisions into amanagerially desirable direc-

tion. Going back to the case of product adoption,

a profit-oriented manufacturer’s priorities depend on

a precise understanding of the mechanism underlying

buyer behaviors, as summarized in Table 1.

An area that needs further exploration is the empir-

ical study of observational learning using historical

data. A major challenge is the usual coexistence of

observational learning and the aforementioned list of

alternative behavioral mechanisms. One recent study

2496 O Observation-Based Learning Rather Than Experienced Individual Learning

has proposed that researchers can distinguish between

irrational herding and observational learning from

panel data, a data format widely available in

a number of industries which include longitudinal

records of choices among a set of products (Zhang

and Liu 2010). The idea is that the dynamic evolution

of choices helps to reveal the impact of social influ-

ences, and that the cross-sectional variations in the

evolution paths help to isolate the nature of social

influences – while irrational herders simply follow

popularity, rational observational learners would mod-

ify the inferences they draw from popularity based on

contextual factors. Empirical research in observational

learning will benefit from development of other effi-

cient, scalable methods that identify observational

learning without imposing stringent data requirements.

Cross-References▶Bayesian Learning

▶ Imitation and Social Learning

▶ Imitative Learning in Humans and Animals

▶ Learning in the Social Context

▶ Social Interactions and Effects on Learning

▶ Social Learning

▶Theory of Conformist Social Learning

ReferencesBanerjee, A. V. (1992). A simple model of herd behavior. Quarterly

Journal of Economics, 107(3), 797–818.

Bikhchandani, S., Hirshleifer, D., &Welch, I. (1992). A theory of fads,

fashion, custom, and cultural change as informational cascades.

Journal of Political Economy, 100(5), 992–1026.

Chamley, C. (2004). Rational herds: Economic models of social learn-

ing. Cambridge: Cambridge University Press.

Miklos-Thal, J., & Zhang, J. (2010). Demarketing. Mimeo, University

of Rochester, and Massachusetts Institute of Technology.

Zhang, J. (2010). The sound of silence: Observational learning in the

U.S. kidney market. Marketing Science, 29(2), 315–335.

Zhang, J., & Liu, P. (2010). Herding in Microloan Markets. Mimeo,

Massachusetts Institute of Technology, and Cornell University.

Observation-Based LearningRather Than ExperiencedIndividual Learning

▶ Selective Attention in Social Learning of Vervet

Monkeys

Occasion Setting

A phenomenon in which stimuli come to modulate

responding to conditioned stimuli without eliciting

responding on their own. Typically, occasion setting

occurs when a relatively long duration stimulus pre-

cedes and co-terminates with a shorter stimulus. Pos-

itive occasion setting results when a conditioned

stimulus is reinforced with the occasion setter and

nonreinforced alone. Negative occasion setting results

when a conditioned stimulus is nonreinforced with the

occasion setter and reinforced alone. Occasion setters

differ from conditioned stimuli in that they do not

directly elicit conditioned responding and in that

their modulatory properties are not impaired by coun-

terconditioning. It has been proposed that occasion

setters act by facilitating or inhibiting CS-US

associations.

Ockham’s Razor

The heuristic that we should prefer the simplest

hypotheses which fit the observations to date.

Oddity

▶Matching to Sample Experimental Paradigm

Offending

▶Delinquency and Learning Disabilities

Offline Learning

▶ Sequence Skill Consolidation in Normal Aging

Online Collaborative Learning O 2497

Offline Memory Consolidation

▶Reactivation and Consolidation of Memory During

Sleep

Older People and HealthQuestions

▶ Empowering Health Learning for the Elderly (EHLE)

On-Demand Learning

▶Microlearning

O

Online Collaborative Learning

EUGENIA M. W. NG

Department of Mathematics and Information

Technology, The Hong Kong Institute of Education,

Hong Kong SAR, China

SynonymsCollaborative learning environment; Computer-based

collaborative learning; Computer-mediated communi-

cation; Computer-supported collaborative learning;

Computer-supported cooperative work; Computer-

supported intentional learning environment; Online

learning communities; Virtual learning communities

DefinitionThere is no agreed definition on online collaborative

learning. Collaborative learning means learners work

together in small groups toward a common goal. Some-

times cooperative and collaborative learning are used

interchangeably but cooperative work usually involves

dividing work among the team members, whilst col-

laborative work means all the team members tackle the

problems together in a coordinated effort (Lehtinen

et al. 2007). Online collaborative learning means

participants learning together in teams using informa-

tion communication technologies, in particular, the

Internet, as the mediating tools.

Theoretical BackgroundVygotsky (1978) has suggested a constructivist

approach to learning, which emphasized learning

through social interaction with social artifacts to con-

struct their own knowledge. Knowledge has to be dis-

covered, constructed, practiced and validated by each

learner and learners construct new ideas of concepts

based on associating current knowledge with prior

knowledge rather than memorizing facts and proce-

dures. Of the learning activities imbued with construc-

tivist theory, cooperative learning has been widely

practiced. Indeed, Johnson and Johnson (1996)

documented hundreds of successful studies on cooper-

ative learning.

Scardamalia and Bereiter were the pioneers in

adopting networked computers as collaborative learn-

ing tools in mid-1900s. The personal computers were

mainly used as a standalone machine before they were

connected for communication in the early 1990s. Spe-

cial software and communal database was developed,

namely, computer-supported intentional learning

environments (CSILE), to allow primary school chil-

dren in different locations to do projects in mathemat-

ics and in social studies collaboratively. It was thought

that CSILE was able to promote active learning, which

was the basic principle of constructivist approach of

learning.

Apart from some tailor-made software to allow and

encourage online collaborative learning, there are

many free communication channels such as chats,

e-mail, video conferencing, and discussion forums

that provide flexible and convenient arena for single

or multiple users, to discuss a range of topics, synchro-

nously or asynchronously. There are also different

terms for online collaborative learning such as com-

puter-supported collaborative learning, community of

inquiry, and virtual community and they are com-

monly used to describe interactive activities among

students working on computers. Members of the com-

munities exchange information, help each other to

develop skills and expertise and solve problems in an

innovative way. They develop a community identity

around shared knowledge, common approaches and

established practices, and also create a shared directory

2498 O Online Collaborative Learning

of common resources. Online collaborative learning is

frequently used as learner-centered for blended learn-

ing or e-learning mode of study. There are a number of

learning communities in education and noneducational

settings. With the introduction of Web 2.0, which is

broadly defined as a second generation, or more

personalized communicative form of the World Wide

Web, people communicate socially using various

media formats such as pictures, videos, andmultimedia.

Creators not only create and own data but also mix,

amend, and recombine contents of their or other’s

resources. Furthermore, creators also welcome com-

ments and even ratings from viewers. Thus, creators

and viewers learn from one another informally.

There are many factors that affect online collabora-

tive learning. Factors include hardware, software, com-

munication line, tasks, duration, information

competency, and perceptions (Fjermestad et al. 2005).

Learning activities should be embedded within the

learning environment, and that the operation of the

mediating tools should be designed to facilitate group

coherence and to promote social interaction among

learners. In other words, teaching and learning needs

to become shared experiences. The academic becomes

the facilitators and students have to assume responsi-

bility for their own learning. They have to assume the

roles of initiators and co-participants in online collab-

orative learning processes (Collis and Moonen 2001).

Indeed, the relationships and interactions among edu-

cators and students are thought to be more significant

than other factors, which affect online collaborative

learning. However, we cannot assume that students

will automatically tune in to this new approach of

learning. They need to perceive online collaborative

learning as useful and be motivated. On the other

hand, good e-mentors also need to know when and

how to provide expert input, to act as a learning peer

and to when to remain silent. The following techniques

are useful to motivate participants to engage in online

discussions (Lim and Cheah 2003):

1. Setting meaningful task

2. Provide clear guidelines to help online learners

prepare on-topic responses

3. Keeping the discussion focused

4. Participating actively by providing constructive

feedback, answering queries, and challenging par-

ticipants’ thoughts

5. Rephrase the original question when responses are

not in the right direction

6. Drawing conclusions and providing expert

knowledge

7. Recommending resources to extend learning

The postings by the participants can provide hard

evidences on their learning processes. Tracked statistics

provided by the hostingWeb sites or learning platforms

can provide quantitative data such as the frequency and

duration of communications. The Web 2.0 environ-

ment can provide extra information such as the editing

history and also enable creators to subscribe to the

changes so that all the updates are sent to the creator

via e-mails (Ng 2010). Apart from the quantitative data

analysis, content analysis is frequently used to analyze

the level of engagement of the participants. Based on

Johnson and Johnson’s theoretical framework, Curtis

and Lawson (2001) suggested analyzing dialog

exchanges under five behavior categories, which are

planning, contributing, seeking input, reflection, and

social interaction. However, Henri (1992) has devel-

oped one of the most sophisticated cognitive analysis

models for online interaction. She delineated five

dimensions related to the quality of the messages:

1. The number of postings by the participants

2. Content that shows the social dimension of confer-

ence interchanges

3. Content that is related to the interactive dimension

of the conference

4. Content that indicates the application of cognitive

skills

5. Content that reflects metacognitive skills

Numerous researchers’ studies (Lehtinen et al. 2007)

have found that online collaborative learning can

1. Enable students with diverse backgrounds and from

differing locations to communicate so that multiple

perspectives and solutions to problems could be

obtained.

2. Facilitate student-centered learning in authentic

and collaborative learning settings.

3. Foster active and independent learning.

4. Enable learners to discuss their subject matter in

greater depth and thus considerably enhance their

critical thinking skills.

5. Increase the level of learner involvement and pro-

vide incentives to learn, which can lead to a wider

Online Learning O 2499

and more complete understanding of the subject

knowledge.

Important Scientific Research andOpen QuestionsComprehensive review of a 100 empirical studies, on

comparing face-to-face versus asynchronous learning

networks (combining self-study with substantial,

rapid, asynchronous interactivity), up to November

2002, found that most results are mixed but student

learning was established to be significantly better. The

most frequently cited disciplines were information

technology-related disciplines followed by business

and management. It is unclear if online collaborative

learning is successful for learners of other disciplines or

not. Similarly, most of the studies have reported posi-

tive feedback from participants on online collaborative

learning and yet there is a lack of well-controlled exper-

iments in the studies, which hampered the internal

validity of their results (Lehtinen, et al. 2007). Further-

more, Alavi and Leidner (2001) did not find conclusive

evidence of the value of technology after reviewing the

pertinent literature, and suggested that a better under-

standing of the role of technology is required.

O

Cross-References▶Asynchronous Learning

▶Asynchronous Learning Environments

▶Constructivist Learning

▶Cooperative Learning

▶ eLearning and Digital Learning

▶Online Learning

ReferencesAlavi, M., & Leidner, D. E. (2001). Research commentary: Technol-

ogy-mediated learning – a call for greater depth and breadth of

research. Information Systems Research, 12(1), 1–10.

Collis, B., & Moonen, J. (2001). Flexible learning in a digital world.

London: Kogan Page.

Curtis, D. D., & Lawson, M. J. (2001). Exploring collaborative online

learning. Journal of Asynchronous Learning Networks, 5(1), 21–34.

Fjermestad, J., Hiltz, S. R., & Zhang, Y. (2005). Effectiveness for

students: Comparisons of “In-Seat” and ALN courses. In S. R.

Hiltz & R. Goldman (Eds.), Learning together online: Research on

asynchronous learning networks (pp. 39–80). Mahwah, NJ:

Erlbaum.

Henri, F. (1992). Computer conferencing and content analysis. In

A. R. Kaye (Ed.), Collaborative learning through computer confer-

encing: The Najaden papers (pp. 115–136). New York: Springer.

Johnson, D. W., & Johnson, R. T. (Eds.). (1996). Cooperation and the

use of technology. London: Macmillan.

Lehtinen, E., Hakkarainen, K., Lipponen, L., Rahikainen, M., &

Muukkonen, H. (2007). Computer supported collaborative learn-

ing: A review. Retrieved 27 March 2007, from http://www.

comlab.hut.fi/opetus/205/etatehtava1.pdf.

Lim, C. P., & Cheah, P. T. (2003). The role of the tutor in asynchro-

nous discussion boards: A case study of a pre-service teacher

course. Educational Media International, 40(1), 33–48.

doi:10.1080/0952398032000092107.

Ng, E. M. W. (2010). Learning to interacting with multiple partici-

pants inmultipleWeb 2.0 learning communities. Journal of Issues

in Informing Science and Information Technology, 7, 11–23.

Vygotsky, L. S. (1978). Mind in society: The development of higher

psychological processes. Cambridge: Harvard University Press.

Online Experiments onLearning

▶Web-Based Experiment Control for Research on

Human Learning

Online Learning

CYNTHIA S. SUNAL, VIVIAN H. WRIGHT

College of Education, The University of Alabama,

Tuscaloosa, AL, USA

SynonymsDistance education; E-learning; Virtual classrooms

DefinitionOnline learning, broadly defined, uses electronic tech-

nologies via the Internet to engage learners and facili-

tate their learning. Multiple Internet tools exist and are

used to create a diverse online learning environment.

Such an environment is complex, providing multiple

opportunities for collaboration, interaction, and com-

munication with instructors, other students, and con-

tent experts from locations around the world at any

time throughout a 24-h day. These opportunities are

supported by a wide range of technologies including,

but not limited to, text, video, audio, and multi-media

2500 O Online Learning

presentations that may take place synchronously or

asynchronously. Online learning typically involves

learning communities in which participants are

engaged with other students and the instructor by

voice over Internet (VOIP), Web 2.0 tools (e.g., blogs,

wikis, and digital shared media), video conferencing,

three-dimensional virtual environments, social net-

working tools, digital drop boxes, and other technolo-

gies often packaged in classroom management systems

(e.g., discussion boards and e-mail). While variation in

the style of delivery differs depending upon the venue,

the term online learning often is used interchangeably

with distance education as the student is in one location

while facilitators of content, or instructors, are at

another location. Early forms of distance education

did not have access to multiple and accessible online

modes of presentation and interaction.

Theoretical BackgroundOnline learning environments often are influenced by

constructivism, and focus on utilizing tools and design

principles that encourage authentic learning, are stu-

dent-centered versus instructor-centered, and engage

learning through a range of pedagogic strategies.

An emphasis exists on supporting meaningful learning

through a design that involves learners in interpreting,

creating, and acquiring knowledge through their active

participation in the knowledge construction process.

The online environment provides unique opportuni-

ties to engage learners in a deep range of experiences.

John Dewey (1933), a strong proponent of active stu-

dent participation, described a problem-solving face-

to-face learning environment with elements that today,

provide guides for instructors designing online learn-

ing environments. Constructivist instructors apply

many of Dewey’s ideas as they seek to design an online

environment enabling students to build on their prior

knowledge while challenging students to reconstruct

existing ideas. The aim is to enable students to expand

their ability to apply new knowledge to a range of

relevant issues and problems (Piaget 2001). To accom-

plish this aim, instructors carefully scaffold the learning

process (Bruner 1992). They also build a learning com-

munity using Vygotsky’s (1978) constructs, in which

learners interact while collaborating, challenging sup-

positions, and negotiating via social interaction (Marsh

and Ketterer 2005).

Multiple online tools and teaching methods can

enhance the dimensions that encourage learners’

knowledge construction. Creativity, innovation, com-

munication, collaboration, critical decision-making,

and problem solving occur through instructors’ devel-

opment of authentic learning experiences for the

online environment. Such experiences are relevant to

learners, inquiry-oriented, and broad and deep in

scope (Gasersfeld 1995). Online tools and resources

work to maximize content learning by involving stu-

dents in making connections between the content and

its’ real world applications. Such applications enable

students to understand the purpose of learning the

content making it relevant to their needs and providing

opportunities, for learners to apply the content in

settings different from that in which it was first learned

(Fosnot 2005; Knowles 1984;). An online environment

is educative when it is constructivist, using tools and

inquiry-oriented pedagogies in distributed environ-

ments that address the multiple learning styles and

experiences students bring to their learning

(Gueldenzop 2003; Huang 2002). When online learn-

ing environments are constructivist in conception, they

enable learners to work at higher levels of Bloom’s

taxonomy of cognitive objectives (Bloom 1956),

because their real-life applications involve them in

working with comprehension, application, synthesis,

and evaluation.

Important Scientific Research andOpen QuestionsOnline learning has evolved from early models that

were flat and used online technologies as a different

delivery model to present the same material found in

face-to-face presentations. While instructional design

has continued to progress, taking advantage of today’s

multiple online tools, developing deep consistent inter-

actions between students, instructors, and other con-

tent providers continues to be a challenge. Instructors

often are required, or even pushed, by administrators

to provide online courses versus the traditional face-to-

face class, because they are seen as an inexpensive

revenue source. The design of an online course, how-

ever, is not easily achieved; creation is complex and

time consuming, and acquiring an understanding of

how and when to use specific tools takes time and

evaluation. Implementing pedagogic strategies online

requires more thought and effort than does the

Online Learning O 2501

O

development of didactic instruction. Instructors need

time to prepare to make the shift to an online learning

environment as several factors affecting that environ-

ment must be addressed. A key question, then, is “How

much time and training are needed prior to completing

an effective design for an online learning course?” This

question has no single answer. The response can fall

along a continuum depending on how many tools

will be used, the specific models needed within

a particular content, and how experienced the instruc-

tor is with establishing and maintaining active learning

communities.

Social presence is a major factor in the effectiveness

of an online learning environment. Participants may

not experience the immediacy of understanding that

often is afforded through face-to-face contact, such as

eye contact and immediate personal responses to ques-

tions and concerns. There is a lack of research clarifying

how social presence impacts online learning particu-

larly in regard to class size, the subject matter being

presented, course structure, and the responses of

instructor and other students in online interactions.

Key questions for which limited research evidence has

been gathered are “What characteristics define social

presence in an online learning environment?” and

“How do each of the characteristics of social presence

function independently and together, to support effec-

tive learning in an online environment?”

Instructor presence is another major factor in the

effectiveness of an online learning environment and

includes feedback to and from students, course struc-

ture, design of the class, assessment, and instructional

style. Instructors have multiple roles in identifying key

information and applications, designing how students

will share information and conjectures, deciding how

much presence and influence the instructor will have

on the direction of discussions, and determining how

much access students will have to the instructor and

when such access will be available. The instructor can

take on a more or less formal role. How the instructor’s

role is structured impacts the potential for students’

inquiry within the course. Students are more satisfied

and demonstrate greater learning when the teacher is

perceived as present and responsive and encourages

multiple views. Instructor presence is important to

online learning and to the structure of an online learn-

ing community. The effects of that presence can be

positive, but the design must allow for students’

involvement in a manner they perceive as respectful

and emotionally safe. A key question for which limited

research exists is “How can instructor presence be most

effectively implemented so that it scaffolds students’

online learning and provides frequency of feedback and

assessment, while giving students significant control

over the direction of discussions, course projects, and

other major course components?”

The use of three-dimensional virtual environments

as an online learning delivery model is gaining atten-

tion. These environments share the aim and character-

istics identified as significant in other constructivist

online learning environments. They appear to present

instructors with many of the same issues as well, rang-

ing from the time needed to learn new tools, to insuring

the scaffolding of an environment students perceive as

a relevant and safe learning community, to determining

how to assess and to provide feedback on students’

work and performance in the environment. Another

key question, then, is “How can educators utilize three-

dimensional virtual environments to deepen opportu-

nities for active, participatory, and relevant online

learning?”

Cross-References▶Actor Network Theory and Learning

▶Asynchronous Learning

▶Computer-Based Learning

▶Computer-Enhanced Learning and Learning

Environments

▶Constructivist Learning

▶Distance Learning

▶Distributed Learning Environments

▶ E-Learning

▶ Learning Strategies for Digital Media

▶Open Learning

▶Technology-Based Learning

▶Virtual Learning Environments

ReferencesBloom, B. S. (1956). Taxonomy of educational objectives, handbook I:

The cognitive domain. New York: David McKay.

Bruner, J. (1992). Acts of meaning. London: Harvard University Press.

Dewey, J. (1933). How we think. New York: Heath 1933.

Fosnot, C. T. (2005). Constructivism: Theory, perspectives and practice.

New York: Teachers College Press.

Gasersfeld, E. (1995). Radical constructivism: A way of knowing and

learning. London: Routledge Falmer.

2502 O Online Learning Communities

Gueldenzop, L. (2003). The integration of constructivist theory and

socialization to distance (online) learning. The Delta Pi Epsilon

Journal, 45(3), 173–182.

Huang, H. M. (2002). Toward constructivism for adult learners in

online learning environments. British Journal of Educational

technology, 33(1), 27–37.

Knowles, M. (1984). Andragogy in action: Applying modern principles

of adult education. San Francisco: Jossey Bass.

Marsh, G., & Ketterer, J. (2005). Situating the zone of proximal

development. Online Journal of Distance Learning Administra-

tion, 8(2), 1–11.

Piaget, J. (2001). The psychology of intelligence. London: Routledge.

Vygotsky, L. (1978). Mind in society: The development of higher psy-

chological processes. Cambridge, MA: Harvard University Press.

Online Learning Communities

▶Online Collaborative Learning

Online Methods for LearningResearch

▶Web-Based Experiment Control for Research on

Human Learning

Online Role Scripts

▶Role-Taking for Knowledge Building

Online Role-Taking

▶Role-Taking for Knowledge Building

On-Task Behavior

▶Academic Learning Time

▶Alertness and Learning of Individuals with PIMD

On-the-Job Learning

▶Workplace Learning

On-the-Job Training

▶ Learning in Practice and by Experience

▶Workplace Learning

Ontogenesis

▶Development and Learning (Overview Article)

Ontogenetic Robotics

▶Developmental Robotics

Ontogeny of Memory andLearning

MAHENDRENATH MOTAH

School of Business, Management and Finance, UTM,

Pointe aux Sables, Mauritius

SynonymsDevelopment, emergence, and maturation of memory

DefinitionOntogeny : from Greek on (ontos), being + genes, born

of – defined as the history of the development of an

individual (as opposed to Phylogeny from Greek

phulon, race, phule, tribe + geneia, birth, origin –

defined as the history of the development of a species

of related organisms). These two concepts are used to

distinguish the development of the individual as

opposed to the species.

Ontogeny of Memory and Learning O 2503

O

Learning and memory are two processes which are

so intertwined that it becomes difficult to think about

one of them without making an abstraction of the

other. Memory is the most important dimension of

human experience and the illustration and manifesta-

tion of its quintessence can be viewed as learning.

Memory retains all that we perceive in the form of

sensory information from the outside world through

our five senses – visual, olfactory, tactile, gustatory, and

auditory. This sensory information is integrated and

interpreted by the brain and stored in the memory to

be retrieved later to help the individual in the learning

process. Knowledge about the world, acquired through

the five senses, is accumulated as the individual organ-

ism grows and will constitute what we call experience.

Hence, knowledge acquired through experience will be

stored in the memory, and retrieved as and when

required to learn new ways to adjust to changing

environmental conditions. Memory therefore is

viewed as the store of all the information received

from the outside world, and can be retrieved at any

time during the life of the organism to ensure its

survival.

Theoretical Background

IntroductionThe history of mankind is tributary of two important

characteristics shared by all human beings: learning

and memory – the first one is believed to be the process

of putting information received from the outside world

in the specific parts of the brain; the second one is

defined as the store where the information received is

located. Memory is considered to be the sum of the life

experiences of humanity and the basis of the life history

of man since the beginning of time. Learning is there-

fore the process that has facilitated the evolution of

man from the initial primitive state to its present state

in the modern world; but man, still living in a primitive

state, uses the same processes of memory and learning

to adjust to the environment to create and maintain his

own identity, history, and evolution. We are exposed to

a body of knowledge on the evolution of the human

species through the oral tradition of the primitive

tribes, as well as the sophisticated communication

tools of the modern, scientific world through the

same processes of memory and learning irrespective

of the era. Research is still being carried out and theo-

ries are exposed to explain the mystery of memory: why

dowe store certain information and not all? How dowe

remember things and forget others? Why do some

people have better memories of events than others?

How do some people have vivid memories of the past

and can relate events in detail from their early years till

today? How can the medicine man, the primitive sha-

man of the South American tribes, the yogis of India,

the Australian Aborigines, and other primitive tribes-

men remember and relate the unwritten stories about

life of their ancestors and the myths and origins of

mankind from their own perspective without any

material support except their memory and what they

have learned from the elders through the oral tradition.

The theories on learning are confronted to almost the

same situation. Research on learning theories; types of

learners, learning styles, and learning to learn are the

major areas of investigation for many scientists who are

intrigued by the issues on learning: why do some indi-

vidual learn faster than others? Why do some individ-

uals have specific ways of learning? Why learning

becomes difficult for some people? Why do children

and adult learning differ? Why are there different types

of learners? In trying to find the answer to these ques-

tions and to find solutions to these issues, researchers

have had to face important theoretical controversies

related to learning, memory, and access and retrieval

of information from the store. All human beings are

made and born with the same genetic material, but

with pronounced individual differences.

An insight in the ontogeny of memory and learning

will lead us to appreciate the importance of individual

differences and help us to find answers to the above

questions in relation to memory and learning.

Theories on memory that have evolved over time,

without successfully explaining the process are: the

theory based on the changes in the structure of the

brain cells under the influence of experience and

exposed as the neural theories; the study of the electrical

activities of the brain has given birth to the electrical

theories; and the realignment of the chemical molecular

structure of the brain cells induced by the stored infor-

mation is believed to be the basis of the biochemical

theories.

Learning, on the other hand, is the process which

enables the organism to place the information received

2504 O Ontogeny of Memory and Learning

in the appropriate area of the brain – in relation to the

type of information received through the relevant sense

organs. The information thus acquired and stored can

be retrieved by the organism to modify its behavior

through another process called remembering.

We can state that: memory is the store of informa-

tion; learning is the process of putting the information

in the store; remembering is the process of retrieving

the information from the store for inspection and use.

Learning is a broad area of study which encom-

passes various aspects. It is defined as “a process leading

to a relatively permanent change in the way people feel,

think, and act.” There is increasing interest in the learn-

ing process as issues related to: the theory of learning;

types of learning and learning styles are investigated by

many researchers. The appraisal of the meaning of each

of the issues related to learning can lead us to under-

stand the process.

It is generally accepted that memory comprises the

encoding stage, the storage stage, and the retrieval stage

(Santrock 2000) as exposed earlier. Because our per-

ception (integration and interpretation of sensory

information by the brain) of the world around us is

unique, so is our memory. Information encoded and

stored in memory by two individuals within the same

environment and about the same event is unique. As

each individual has lived different experiences during

their lifetime, their memory of life events will therefore

be different, and their behavior will be influenced by

their memory.

Human behavior has proved to be a real puzzle to

researchers as the causes are imputed to both internal

and external factors. The interactions of numerous

determinants of human behavior (among which mem-

ory has been proved to be the most important) have

brought scientists like Schacter (1996, 1999), Craik and

Lockhart (1972), Santrock (2000), Craik and Tulving

(1975), Paivio (1971/1986), Shepard (1996), and

Bruning et al. (1999) to work on various aspects of

memory and share their findings on memory systems,

shallow and deep memory, duration of memory, mem-

ory processing, memory storage (iconic – visual and

echoic-auditory registers), organization of memory,

and explicit and implicit memory, respectively. Others

have worked on theories based on how the memory

functions and proposed the Atkinson-Shiffrin Theory:

sensory memory, short-term or primary memory, and

long-term or secondary memory; Tulving (1972): epi-

sodic memory and semantic memory; Bartlett (1932):

Schema Theory of memory; Bower (1981), Blaney

(1986): The Network Theories. Several other mem-

ory-related concepts have been proposed.

The confusion does not only lie in the understand-

ing of the functions of memory, but also in the location

of memory in the brain. Scientists who have tried to

solve this mystery of memory are among others:

Haberlant (1999) – the brain’s role in the retention of

memory; Lashley (1950) – location of memories in the

brain; Squire (1990) – memories cluster in groups and

memory is distributed throughout the brain; Lynch

(1990) – limited number of brain systems and path-

ways involved in memory. The role of the brain in

remembering and forgetting experiences, Neergaard

(1998) have brought other scientists to investigate the

role of the brain in the way information is encoded. The

retrieval and forgetting of information including other

memory-related aspects: tip-of-the-tongue phenome-

non, serial-position effect, the primacy and the recency

effect, recall, recognition, and forgetting have been

studied by Ebbinghaus (1850–1909). An incursion in

the literature and research on the other types of mem-

ory could lead to a better understanding of the intrica-

cies between memory and learning - Conway and

Rubin (1993), Schacter (1999); emotional memories

and flashbulb memories –Rubin and Kozin (1984);

personal trauma – Langer (1991), Schacter (1996);

repressed memories, mood-congruent memory –

Mineka and Nugent (1995); implicit and explicit mem-

ory and learning – Kihlstrom et al. (2007).

LearningThe unified information processing system (Goldstein

1999), comprising of sensation, (concerned with the

detection and encoding of light, sound and heat from

the environment through the senses) and perception

(organizing and interpreting the information received

by the brain, to give it meaning) is at the basis of

attention, the first of the four main processes compris-

ing retention, reproduction, and reinforcement

described by Bandura (1986, 1994) as Observational

Learning (the capacity to learn behavior patterns by

observation). Apart from systems and processes, there

are, on the other hand, set of factors which facilitate

and influence learning. Learning and cognition

Ontogeny of Memory and Learning O 2505

O

(acquisition and use of knowledge) emphasize the role

of cognitive factors in learning – Tolman

(1948) described an organism’s representation of the

structure of physical space as cognitive maps; Kohler

(1925) used the concept of insight learning to expose

a form of problem solving in which the organism

develops a sudden insight or understanding of

a problem’s solution. Other factors which influence

learning are: biological factors, Chance (1999); pre-

paredness, Seligman (1990); cultural factors, Cole and

Cole (1996).

Beside learning processes and systems, there are

several theories of learning which have been

propounded by several renowned scientists: Reinforce-

ment theory – Skinner (1938, 1953); Action Learning –

Reg Revans (1940); Facilitation theory – Rogers (1961);

Cognitive Gestalt Approaches – Burns (1978); Experi-

ential Learning –Kolb (1984); Holistic Learning theory –

Laird (1985); Sensory Stimulation theory – Laird

(1985); Adult Learning theory – Knowles (1973).

Debate on other aspects of learning such as perceptual

and conceptual learning, implicit and explicit learning –

is still going on among researchers. The ideas and

theories on learning exposed by earlier researchers dur-

ing the late nineteenth century and the early years of

the twentieth century are still being either challenged or

adopted by the modern scientists. Moreover, the find-

ings of earlier scientists are helping either to consoli-

date or to confirm the ideas of modern researchers on

the learning process and its ramifications in the human

brain.

Views on Memory and LearningSeveral other views have been exposed on memory and

learning in spheres remote from our modern world

such as the “Homeric tales: Odyssey and Iliad,” the

“Bhagwat Gita,” the “Ramayana,” and the “Bible,”

which are important references for the modern man.

These result from a totally oral culture – these have

been orally composed, recited, received and retained,

and transmitted with all the beauty of what the oral

tradition is capable of transmitting without the help of

information technology. Memory and learning have yet

other mysteries which the modern world is still trying

to uncover.

The mystery of memory and learning is made even

more complex if we consider them from the point of

view of rebirth or reincarnation among the Hindus,

Buddhists, and other believers. The concept of “soul”

which is considered as containing the database of life’s

learning and actions has a permanence, and is carried

from one’s previous life into a new body and so on. This

is depicted in the “Bhagwat Gita.”

Chapter 15 verse 8 – “the living entity (individual

human soul) in the material world carries his different

conceptions of life (individual tastes and actions) from

one body to another as air carries aromas from one place

to another. Thus he takes one kind of body and again

quits it to take another.”

Chapter 15 verse 9: “the living entity (individual

human soul), thus taking another gross body obtains

a certain type of ear, tongue, nose and sense of touch,

which are grouped about the mind. He thus enjoys

a particular set of sense objects through another body.”

Therefore, memory and learning are never lost.

Depending on the memories and life experiences,

each individual human being will have specific memo-

ries of events and different experiences, and thus can

only share what he or she has accumulated during their

successive lives.

Important Scientific Research andOpen QuestionsThe mastery over the ontogeny of memory and learn-

ing is still being sought by many scientists. The impor-

tance of memory and learning in humans from

conception to birth is also being investigated: Does

the fetal behavior indicate some form of memory and

learning during the intrauterine period? Chamberlain

(1995).

Several studies are still being carried on aspects of

sensory development among new born babies, lan-

guage development, and effects of traumatic events on

the emotional states of mothers and babies. These

researches indicate that the way ahead is still long and

encloses many unexpected aspects, but the fact remains

that memory and learning are major components of

the overall development of humans and guarantee the

preservation and progress of human life.

The neural, electrical, and biochemical theories are

the ones that are receiving the most attention, as the

electronic means provided by new technologies have

provided a wider scope of investigation and experimen-

tation. The latest scientific research finding is attributed

2506 O Ontogeny of Memory and Learning

to British Scientists at University College London. Dr.

Eleanor Maguire, (2010) and team have been able to

“differentiate brain activity linked to different memo-

ries and identify thought patterns by using function

Magnetic Resonance Imaging. They have been able to

look at brain activity for a specific episodic memory

and found that memories are represented in the hip-

pocampus. Through their research, they had the

opportunity to understand how memories are stored

and how theymay change through time” (Arts Journal –

March 12, 2010).

Cross-References▶Adult Learning Theory

▶Autoassiociative Memory and Learning

▶Biological and Evolutionary Constraints of Learning

▶Development and Learning

▶Developmental Cognitive Neuroscience and Learning

▶ Episodic Learning

▶ Learning Styles

▶Memory Codes

▶Memory Consolidation and Reconsolidation

▶Observational Learning: The Sound of Silence

▶ Sensory Memory

▶ Social Learning

ReferencesBandura, A. (1986). Social foundation of thoughts and actions – A

social cognitive theory. Englewood Cliffs: Prentice-Hall.

Bartlett, J. (1932). Remembering. Cambridge: The University Press.

Blaney, P. H. (1986). Affect and memory: A review. Psychological

Bulletin, 99, 229–246.

Bower, G. H. (1981). Theories of learning (5th ed.). Englewood Cliffs:

Prentice-Hall.

Bruning, R. H., et al. (1999). Cognitive psychology and instruction

(3rd ed.). Columbus: Prentice Hall.

Burns, J. M. (1978). Leadership. New York: Harper & Row.

Chamberlain, D. B. (1995). Prenatal memory and learning in life before

birth.

Chance, P. (1999). Learning and behaviour (4th ed.). Belmont:

Wadsworth.

Cole, M., & Cole, S. R. (1996). The development of children (3rd ed.).

New York: Freeman.

Conway, M. A., & Rubin, D. C. (1993). The structure of autobiographic

memory – theories of memory (pp. 103–137) Hove: Lawrence

Erlbaum.

Craik, F. I. M., & Lockhart, R. S. (1972). Levels of processing: A

framework for memory research. Journal of Verbal Learning and

Verbal Behaviour, 11, 671–684.

Craik, F. I. M., & Tulving, E. (1975). Depth of processing and reten-

tion of words in episodic memory. Journal of Experimental Psy-

chology; General, 104, 268–294.

Ebbinghauss, H. (1850–1909) Wikipedia.

Goldstein, E. B. (1999). Sensation and perception (5th ed.). Pacific

Grove: Brooks/Cole.

Haberlant, K. (1999). Human memory. Boston: Allyn & Bacon.

Kihlstrom, J. F., Dorfman, J., & Park, L. (2007). Implicit and explicit

learning and memory. In Velmans, M., & Schneider, S. (Eds.),

A companion to consciousness (pp. 525–539). Oxford: Blackwell.

Knowles, M. (1973). The adult learner: A neglected species. Houston:

Gulf Publishing.

Kohler, W. (1925). The mentality of apes. New York: Harcourt, Brace

and World.

Kolb, D. A. (1984). Experiential learning. Englewood Cliffs: Prentice-

Hall.

Laird, D. A. (1985). Learning styles. www.dlrn.org/library/dl/guide5.

html.

Langer, L. L. (1991). Holocaust testimonies: The ruins of memory.

New Haven: Yale University Press.

Lashley, K. (1950). In search of the anagram. New York: Cambridge

University Press.

Lynch, G. (1990). The many shapes of memory and the several forms of

synaptic plasticity. Dallas.

Maguire, E., et al. (2010). Online edition of current biology. London:

UCL.

Mineka, S., & Nugent, K. (1995). Memory distortions. Cambridge:

Harvard University Press.

Motah, M. (2006). In Proceedings of the 2006 Informing Science and IT

Education Joint Conference. InSITE.org.

Motah, M. (2007). In Proceedings of the 2007 Computer Science and IT

Education Conference. InSITE. org.

Neergaard, L. (1998). Scientists get insight on memory by watching

brain activity.

Paivio, A. (1971/1986). Imagery and verbal processes. New York: Holt,

Rinehart & Winston.

Reg Revans, (1940). Action learning, www.jtiltd.com.

Rogers, C. (1961).On becoming a person. Boston: Houghton Miifflin.

Rubin, D. C., & Kozin, M. (1984). Vivid memories. Cognition, 16,

81–95.

Santrock, J. W. (2000). Psychology (6th ed.). Boston: McGraw-Hill.

Schacter, D. L. (1996). Searching for memory. New York: Basic Books.

Schacter, D. L. (1999).Consciousness in the new cognitive neurosciences

(2nd ed.). Cambridge: MIT Press.

Seligman, M. E. P. (1990). Learned optimism. New York: Pocket

Books.

Shepard, R. N. (1996). The eye’s mind and the mind’s eye. Paper,

Toronto.

Skinner, B. F. (1953). Science & human behaviour. New York:

McMillan.

Squire,L.(1990).Memory and brain. MyiLibrary/Ebooks Corporation.

Swami Prabhupada, A. C. B. (1972). Bhagwat Gita as it is (2nd ed.).

New Delhi: The Bhaktivedanta Book Trust. India.

Tolman, E. C. (1948).Cognitivemaps in rats andman, OpenOffice.org.

Tulving, E. (1972). Episodic and semantic memory. In origins of mem-

ory. San Diego: Academic.

Ontology and Semantic Web O 2507

Ontology and Semantic Web

PABLO N. PIRNAY-DUMMER

Department of Education, University Freiburg,

Freiburg, Germany

O

SynonymsExpert systems

DefinitionOntology comes from the Greek word οntος (on’-toce)that stands for “of being,” for all that is, the onte.

Semantic web and the corresponding web ontologies

map this idea to the largest repository of semantic data

currently available: the content of Internet. The seman-

tic web is a class of tools, technologies, and algorithmic

methods to associate meaning with a vast amount of

partial standardized data, i.e., to base some of machine

performance on meaning. The web ontology is the

product (as data) from the semantic web technologies

and serves at the same time as a reasoning basis to

support knowledge and decision making within

humans and machines. It contains things, their

descriptions, their properties, and their relations. The

ontology describes all that is, the semantic web is

a technological set of tools to access the ontology of

the Internet, and the web ontology is the structured

data that results from this method.

Theoretical BackgroundThe transfer of Plato’s and other philosophers’ objec-

tivist ideas about “all that is” into the domain of web

data is nonetheless empirical. Thus web ontologies do

not require an objectivist position per se, even if their

assumptions are inspired by the underlying philoso-

phies. However, web ontologies inherit some of the

problems of objectivism (see Important Scientific

Research and Open Questions). A web ontology con-

stitutes a network of meaning usually from multiple

sources to resemble a certain field of knowledge and

expertise and to make meaning formally interpretable

to both machines and humans.

Aweb ontology usually has a given purpose or field

(e.g., geophysics) and consists of concepts, types, rela-

tions, and instances. Depending on the goal of an

ontology, a village could be a topological instance of

the type “point” whereas a street would in this context

be a topological instance of the type “line.” Concepts

have different central (prototypical) identifiers. Identi-

fiers are object properties that are likely to identify

a certain entity and are at the same type generic to

a type. They fulfill the necessary and sufficient condi-

tion to identify an instance within a type. Instances of

the same type may be dependent on different contexts.

Relations provide properties of the connection between

them, e.g., the village “Riedichen” is located next to the

town “Zell.” Properties of concept structures, relations,

and types can be inherited from subsuming elements:

Elements may pass down part of their structure. If an

ontology focuses on concepts, their taxonomy, and

relations only then is it referred to as lightweight ontol-

ogy. If it incorporates axioms and constraints (e.g.,

a specific scopus or range of validity) it becomes

a heavyweight ontology: Like every theoretical model

structure, these ontologies have axioms that always are

considered to be true in the context of the ontology in

order for the ontology to work. In order to work

properly for humans and machines alike, a specific

structure has been developed that tries to fit both

formal and usability needs.

Usually, methods of the semantic web follow

a multilayer subsuming architecture going bottom-up

from simple text character sets and document identi-

fiers (URI) up to the user interface. In between are

layers that process the syntax, the data structure

(specific web ontology languages), taxonomy- and

query structures, ontology documents, and rules for

data interchange. Beyond the basic structure and the

descriptive layers, there are the inferential layers that

contain inductive logics, theory (model) proof, and

evaluation modules (trust layers) that connect the

model to the user interface.

The most frequently used languages are RDF

(Resource Description Framework), OWL (Web

Ontology Language), andWSML (Web Service Model-

ing Language). OWL and WSML developed as lan-

guage classes and thus have different dialects as well.

As a basic fallback, XML (Extensible Markup Lan-

guage) could also be used to very basically create

a fitting data structure. In fact, XML is already the

basis for all web ontology languages. To help users

with the construction and maintenance of ontologies,

several software editors are available. Moreover, the

structure and the instances of a web ontology can also

2508 O Ontology and Semantic Web

be visualized graphically. The nodes of a graph then

represent the concepts and the links their relations.

Figure 1 shows how some concepts frommusic may

be related. Real web ontologies have of course a larger

scope.

The ontology from Fig. 1 will have multiple

instances to be of use to anybody. An example is given

in Fig. 2. The ontology is not completely filled for the

instances, e.g., composers inherit some properties from

artisans and humans while symphony inherits a property

ofmusic. Once filledwith content, the small ontology and

its data may thus answer queries on concerts, composers,

events, or on symphonies and their themes.

Once a language is established that fits the needs

and goals for a given ontology, a reasoner is needed that

works on the structure to derive more or less complex

outputs. A reasoner contains a set of evaluation

functions and options for deduction and induction –

it is the part of the software that interprets the ontol-

ogies and handles requests and outputs. Reasoners are

not necessary for web ontologies per se, but they are

mandatory for the methods of the semantic web that

operate onweb ontologies. Reasoners also help with the

Orchestra

Musician

Composer

Symph

Artisan

Human

is a

is ais a

writes

is part

performsis group

of

perfoon

Ontology and Semantic Web. Fig. 1 A structure for a simple

automated construction of web ontologies if they are

interfaced by semantic web learning algorithms.

With a given language at hand, ontologies can be

constructed manually. If the scopus of the ontology is

larger, which makes it more useful at the same time,

manual encoding is too time consuming to be feasible

for most applications. Thus, methods of automated

content construction, more commonly referred to as

ontology learning, are one of the key research fields for

web ontologies. Learning algorithms are usually heu-

ristics that use a combination of metadata analysis,

tagging, parsing, semantic clustering, and inductive

reasoning methods from already existing content

(within or outside the current ontology). In general,

all available compatible sources are used and processed,

e.g., documents from a search query of search engines,

existing neighboring web ontologies, content-specific

files within the company or institution for which the

web ontology is built. When learning algorithms nav-

igate through web documents, they also rely highly on

metadata that are attached to the documents. Stan-

dards for such metadata are available, e.g., by following

the Dublin Core standard (dublincore.org).

Stage

Event

ony

Theme

Music

is a

is part of

of

rms

performs on

ontology

Ludwig

van Beethoven

Sergei

Rachmaninov

Piotr IIyich

Tschaikovsky

First name

First name

First name

Last name

Last name

Last name

writes

writes

writes

Fate theme has

Composer:708

Composer:1209

Composer:913

based onplays

plays

plays at

plays at

plays

Symphony:5

Symphony:1

Symphony:4

Name

NameName

Stage:3

Event:789291

ChicagoSymphonyOrchestra

ChicagoSymphony

Center

ThursdayNight

Concert

Orchestra:7

Ontology and Semantic Web. Fig. 2 Instances for the ontology in Fig. 1 with several relations

Ontology and Semantic Web O 2509

O

As to learning, web ontologies and the methods of

semantic web can be utilized in different ways. In

machine learning they provide a flexible data matrix

for decisionmaking while themethods of semantic web

make use of machine learning algorithms. Web ontol-

ogies can be used as base models within learning soft-

ware to help the learner to navigate a content. There are

also hopes, that the methods may be used to individu-

ally create and heuristically track the learner in his or

her learning process. Up to now, technologies of this

kind are however still quite fragmentary.

Important Scientific Research andOpen QuestionsThe World Wide Web Consortium (W3C) has largely

promoted research and development of semantic web

languages, algorithms, and tools (Koivunen and Miller

2001). Also, a semantic web search engine (swoogle.

umbc.edu) has been established in 2004 by UMBC that

helps researchers and developers to find free accessible

ontologies on all kinds of common subjects: “Swoogle”

finds ontologies in all kinds of different semantic web

languages. Most of the recent years in web ontology

were spent on the automated learning algorithms (e.g.,

Madche 2002). Moreover, also the languages and the

reasoners were improved (e.g., Martin et al. 2006). The

construction of ontologies requires inductive logics

(see Angluin 1980), e.g., types of non-monotonic rea-

soning and the model theoretic principles of unifying

logic (e.g., Fanizzi et al. 2008; Martin et al. 2006).

Herein lies one of the most important research potential

for the semantic web at the moment – it will presumably

improve both the construction processes for automated

ontology learning and the reasoners. As mentioned

before, most of the technologies rely on metadata

attached to certain entities (e.g., entries, documents,

parts). The fact that metadata can be easily forged (and

will be once the methods play a meaningful role in

someone’s life) is a methodological downside of the

semantic web approach. Herein web ontologies inherit

a main objectivist problem: How is there to know

whether something is true before a given background

(e.g., possible world)?Most semantic web algorithms are

completely blind toward identifying a lie, forgery or

erroneous content, and very few of them deal with deceit

at all. The same holds true for incomplete and inconcise

data (vagueness, inconsistency) and nonmetrical devia-

tions (uncertainty). Although it is the human side that

plants the information on the web, the semantic web

methods still have too little means to control for that

aspect. Another problem that is still open is vastness (of

the web): At least 24 billion pages of information are not

processable within reasonable amounts of time even

with the fastest algorithms available.

Cross-References▶ Inferential Learning and Reasoning

▶ Learning Algorithms

2510 O Ontology of Learning Objects Repository for Knowledge Sharing

▶Ontology of Learning Objects Repository for

Knowledge Sharing

▶ Semantic Networks

▶Web Science

ReferencesAngluin, D. (1980). Inductive inference of formal languages from

positive data. Information and Control, 45, 117–135.

Davies, J., Studer, R., & Warren, P. (Eds.). (2006). Semantic web

technologies: Trends and research in ontology-based systems.

Chichester: Wiley.

Fanizzi, N., d‘Amato, C., & Esposito, F. (2008). Induction of classifiers

through non-parametric methods for approximate classification

and retrieval with ontologies. International Journal of Semantic

Computing, 2(3), 403–423.

Koivunen, M.-R., & Miller, E. (2001). W3C semantic web activity.

Retrieved August 16, 2010, from http://www.w3.org/2001/12/

semweb-fin/w3csw

Madche, A. (2002). Ontology learning for the semantic web. Dor-

drecht: Kluwer.

Martin, E., Sharma, A., & Stephan, F. (2006). Unifying logic, topology

and learning in parametric logic. Theoretical Computer Science,

350(1), 103–124.

Ontology of Learning ObjectsRepository for KnowledgeSharing

SHOUHONG WANG1, HAI WANG

2

1Charlton College of Business, University of

Massachusetts Dartmouth, Dartmouth, MA, USA2Saint Mary’s University, Halifax, NS, Canada

DefinitionA learning object is a unit of digital resource that can be

shared to support teaching and learning. An ontology

for a learning objects repository is a conceptual net-

work of all related learning objects that shows the

semantic relationships between the learning objects for

the learning subject domain and allows people to share

knowledge of the domain.

Theoretical BackgroundAlong with the increasing use of online and blended

teaching/learning systems, learning objects become

increasingly valuable and, at the same time, the man-

agement of learning objects repository becomes

complicated. There have been metadata standards for

learning objects, such as those proposed by IMS Guide

(IMS 2006). These standards are used to represent

individual learning objects at the collection level,

which is similar to library catalogue systems. However,

to use learning objects to support teaching and learning

at the knowledge sharing level for a specific field,

knowledge schema must be applied to the learning

objects repository for the domain. This is because

learning objects can be organized in a variety of ways

depending upon complex intra-context and inter-

context (Wiley and Edwards 2002). When a virtual

learning objects repository is huge and is distributed

on the Internet, the use of metadata and keywords only

to search the needed learning objects is inefficient and

ineffective since much potential associations with var-

ious learning aspects are bypassed. This has lead to

approaches to Semantic Web applications that model

the relationships between learning objects using formal

ontologies.

Ontology in the Context of LearningObjectsIn the general philosophical term, an ontology is

a specification of a conceptualization (Gruber 1995;

Guarino 1995). In the learning objects field, an ontol-

ogy is typically a network of semantically related learn-

ing objects for a specific learning or instructional

domain. An ontology allows people to share common

understanding of the subject domain. A large ontology

for an entire domain is a composition of a set of

primitive ontologies. Given the complexity of learning

objects structures in general, a learning object itself can

be represented by an ontology.

Ontology Presenting the Object-Oriented Vision of Learning ObjectsRepositoryAll learning objects are natural objects. Rationally,

a learning objects repository can be represented by an

object-oriented model. The premise of object-oriented

modeling is that objects are grouped into categories or

classes for the application domain (Wang 1999). Clas-

ses are organized into hierarchies in which the sub-

classes inherit properties from their superclass. For

instance, the subclasses of learning objects inherit

metadata from their superclass (or meta-learning-

object). A subclass can inherit frommultiple superclasses.

Ontology of Learning Objects Repository for Knowledge Sharing O 2511

Inheritance relationships result in static connections

between learning objects. In addition to inheritance

relationships, the object-oriented paradigm applies so

called message sending from one class to another

to make dynamic connections between the classes.

These messages accentuate the dynamic relationships

between the classes that represent contingent access

paths to objects. All static and dynamic relationships

between the classes specify the semantic properties of

the entire sets of classes.

O

Ontology as the User-RepositoryInterface for the Learning DomainAn ontology serves as the user-repository interface that

provides views of learning objects in various perspec-

tives to enhance the learning objects repository usabil-

ity for diverse applications in the learning domain. The

ontology is envisaged as knowledge structures that fit

the individual applications. For instance, a learning

objects repository can have two different views:

learners’ view and instructors’ view. An ontology for

the learners articulates general interactive learning pro-

cesses, while an ontology for the instructors describes

a scheme of pedagogical design.

Ontologies have been with us for a quite long time.

For instance, an ER (entity-relationship) diagram is

a general type of ontology for relational databases. In

comparison with ER charts for relational databases,

ontologies for learning objects repositories are compli-

cated due to the complex properties of learning objects

and the richness of semantics in the learning and

instructional context. More importantly, ER diagrams

are basically used only for database designers, but

ontologies for learning objects must be used for all

creators of learning objects, as well as end-users of

learning objects, for the knowledge sharing purpose.

Hence, an ontology of learning objects acts as the

interface between all users and the learning objects

repository.

Ontology Incorporating Metadata ofLearning ObjectsMetadata standards of learning objects intend to gen-

eralize taxonomies and vocabularies for learning

objects repositories for all disciplines (IMS 2006).

There is a tacit ontology behind a meta-data standard.

Such a tacit ontology is too complicated to present

because the semantic relationships between all learning

objects are hard to be standardized. Specifically, the

taxonomies can never be exhaustive for all disciplines,

and vocabularies can be interpreted in a variety of ways

depending upon the disciplines. Without the support

of ontologies, tagging all types of metadata and relevant

keywords to every learning object could be prohibi-

tively expensive and will eventually make any search

engine practically powerless. On the other hand, an

ontology of a specific domain for a learning objects

repository serves as a map and suggests paths for

retrieving candidate learning objects to reach a certain

objective of learning or teaching. The use of ontology

does not exclude the use of metadata; rather, an ontol-

ogy usually incorporates metadata to make the meta-

data more context relevant for searching learning

objects.

Important Scientific Research andOpen Questions

An Ontology of Learning Objects ina RepositoryAn ontology of learning objects repository can be illus-

trated with the example in Fig. 1, which shows the

interrelational structure of the ontology at the top

level. As shown in the ontology network, the learning

subject, learning objective, instructional method, deliv-

ery instrument, assessment instrument, and assessment

outcome learning objects are semantically linked and

represent the virtual learning objects repository. Each

learning object icon has a number of methods associ-

ated with the command buttons. The Detail command

button allows the user to view the details of the learning

object through a built-in hyperlink. The Drill-Down

command button allows the user to search learning

objects that directly inherit from the current meta-

learning-object. The Local Search command button

allows the user to search learning objects that are

semantically similar to the current learning object

using metadata or keywords. If the user does not spec-

ify a search criterion, the system retrieves learning

objects one by one in the backward chronicle order.

The Back command button allows the user to view the

previous ontology network. The ontology network

would provide a variety of ways for the user to navigate

the learning objects repository to meet diversified

needs of pedagogical knowledge sharing (Wang 2008).

Learning Subject

Learning Objective

Delivery Instrument

Instructional MethodMIS Instructional Methods

Assessment Outcome

MIS Delivery Instruments

Is_achieved_through

Is_achieved_through

Is_assessed_by

Is_assessed_by

Assessment Instrument

Applies

Uses

Is_measured_by

Is_measured_by

Is_measured_by

MIS Learning Objectives

MIS Assessment Instruments

MIS Assessment Outcomes Creates

Detail

Local Search Back

Achieves

Drill-Down

Detail

Local Search Back

Drill-Down

Detail

Local Search Back

Drill-Down

Detail

Local Search Back

Drill-Down

Detail

Local Search Back

Drill-Down

Detail

Back

Drill-Down

Management Information Systems (MIS)

Local Search

Ontology of Learning Objects Repository for Knowledge Sharing. Fig. 1 Example of ontology of learning objects

repository

2512 O Ontology of Learning Objects Repository for Knowledge Sharing

Research Issues and Open QuestionsThe ontology approach is a powerful modeling

approach; however, without a domain analysis for par-

ticular types of learning domain, the ontology

approach remains a virtual philosophy, rather than

a concrete technique for learning objects reuse and

knowledge sharing (Wang and Wang 2008). To build

ontologies based on the methodology progression,

ontologies of learning objects repository must present

the common understanding of the learning domain in

the learning community. The task of a domain analysis

and the construction of an ontology is to actualize

the knowledge components and their semantic rela-

tionships. This is virtually the central and challenging

issue of research into ontology for knowledge sharing

with learning objects techniques for learning of the

individuals with diversified learning styles in various

learning subjects.

Cross-References▶Classification of Learning Objects

▶Collaborative Learning

▶ eLearning Authoring Tools

▶Knowledge Organization

▶Knowledge Representation

▶Ontology and Semantic Web

▶ Schema(s)

▶ Semantic Networks

ReferencesGruber, T. (1995). Toward principles for the design of ontologies used

for knowledge sharing. International Journal of HumanComputer

Studies, 43(5/6), 907–928.

Guarino, N. (1995). Formal ontology, conceptual analysis and knowl-

edge representation. International Journal of Human and Com-

puter studies, 43(5/6), 625–640.

IMS (2006). IMS Meta-data Best Practice Guide for IEE 1484.12.1-

2002 Standard for Learning Object Meta-data Version 1.3 Final

Specification. http://www.imsproject.org/meta-data/. Accessed

25 Sep 2010.

Wang, S. (1999). Analyzing business information systems: An object-

oriented approach. Boca Raton: CRC Press.

Wang, S. (2008). Ontology of learning objects repository for

pedagogical knowledge sharing. Interdisciplinary Journal of

E-Learning and Learning Objects, 4, 1–12.

Open Instruction and Learning O 2513

Wang, H., & Wang, S. (2008). Ontology for data mining and its

application to mining incomplete data. Journal of Database

Management, 19(4), 81–90.

Wiley, D. A., & Edwards, E. K. (2002). Online self-organizing social

systems: The decentralized future of online learning. Quarterly

Review of Distance Education, 3(1), 33–46.

Open- and Closed-Mindedness

▶Dogmatism

▶Dogmatism and Learning

Open and Distance Learning

▶Open Learning

O

Open Instruction and Learning

ROBIN STARK1, PETRA HERZMANN2

1Institute of Education and Educational Psychology,

Saarland University, Saarbrucken, Germany2Institute of General Didactics and School Research,

University Cologne, Cologne, Germany

SynonymsConstructivist learning environments; Individualized

instruction; Inquiry learning; Situated learning

DefinitionIn Germany, the rather vague and enigmatic concept of

“Offener Unterricht” (e.g., Peschel 2002) constitutes

a democratic and rather radical form of open instruc-

tion. In contrast to traditional content-centered forms

of instruction this individualized form of instruction

emphasizes the learner’s autonomy, responsibility,

participation, and self-regulation with respect to

central dimensions of teaching and learning at school.

The adjective “open” in this context does not only

refer to organizational aspects of learning (time,

place, social dimensions) and methods of knowledge

and competence acquisition but also to contents and

tasks, aspects of social structure and regulation (e.g.,

questions of class management, planning of lessons,

interaction and communication rules), and personal

dimensions (e.g., relations between learners as well as

learners and teacher). Open instruction in the Anglo-

Saxon language area refers to a family of constructivist

approaches in which the process character and the

context- and situation-specific nature as well as the

distributed nature of knowledge, and the significance

of self-regulated, social-interactive, and participative

aspects of knowledge construction are underlined and

characteristics of informal out-of-school-learning are

appreciated.

Theoretical BackgroundThe concept of “Offener Unterricht” is on the one

hand inspired by ideas of the German

“Reformpadagogik” (e.g., Diesterweg) in which school

is organized from the perspective of the child, and on

the other hand by alternative educational approaches

like “Antiautoritare Erziehung” and political move-

ments propagated vehemently in the 1960s of the last

century. In its radical form, “Offener Unterricht” in

Germany is a highly individualized kind of instruction

primarily implemented in primary schools. According

to the underlying instructional “philosophy,” the

action- and product-oriented, direct and self-regulated

forms of learning that share central features with tra-

ditional apprenticeship learning meet the concerns of

children to actively explore their environment and

make hands-on experiences. However, in spite of its

historical roots the theoretical background as well as

the empirical foundation of this concept are rather

meager, compared to the rich research base of forms

of open instruction operating under proliferating

constructivism-labels in the Anglo-Saxon tradition.

Constructivist positions can be categorized as mild,

moderate, or strong corresponding to their relative

stances referring to the existence of an objective reality,

the role of internal processes, the effects of instructional

interventions, and the relation between description

and prescription. In addition, the social dimension

can be more or less explicitly pronounced (e.g., social

constructionism).

Although forms of open instruction in the Anglo-

Saxon and the German tradition can be traced back to

the same “heroes” (e.g., Georg M. Kerschensteiner,

2514 O Open Instruction and Learning

John Dewey, Lew S. Vygotsky, Jerome S. Bruner, Martin

Wagenschein), and both share relevant instructional

ideals, methods, and procedures, the “new” construc-

tivist siblings are more than old wine in new bottles.

From our perspective, there are interesting new per-

spectives not only with respect to the theoretical foun-

dation and methodological questions but also

concerning (often technology-based) instructional

consequences.

In order to systematize the wilderness of instruc-

tional “philosophies” Reinmann and Mandl (2006)

demarcate the so-called technological position with

either behaviorist or cognitive roots from the construc-

tivist position by differentiating between the primacy

of “instruction” and “construction.” Whereas the tech-

nological position functions as theoretical foundation

of traditional, content-centered learning environments

(e.g., direct instruction), the constructivist position

provides theoretical underpinnings of open, situated-

learning environments. These contrasted positions dif-

fer on various levels, beginning with different episte-

mological and ontological beliefs. These meta-

theoretical differences come along with differences on

a theoretical level (e.g., concepts of knowledge and

learning processes, role of situational contexts for

knowledge acquisition), and these differences in turn

entail practical consequences (e.g., design principles of

learning environments, characteristics of tasks and

their contextual embedding, roles ascribed to learners

and teachers in the context of learning environments).

Furthermore, there are differences on the level of

research and evaluation methodology.

In the 1980s and 1990s of the last century, various

theoretical perspectives of situated learning were

discussed. With respect to knowledge construction,

the common denominator of these perspectives is the

critique of (knowledge) representation metaphors, the

assertion of the process-character, the context- and

situation-specific nature and the distributed nature of

knowledge, as well as the significance of self-regulated,

social-interactive, and participative aspects of knowl-

edge construction. Furthermore, various instructional

models were developed, implemented, and evaluated in

multiple domains, for instance the cognitive appren-

ticeship model, the anchored instruction model, and

the random assess instruction. For a characterization of

theoretical perspective and instructional models see

Reinmann and Mandl (2006).

In contrast to the political roots and connotations

of the German tradition of open instruction, the

instructional perspective of the situated-cognition

movement was influenced by serious pedagogical prob-

lems, primarily the problem of knowledge transfer that

is closely connected to the history of the learning sci-

ences. In the context of the situated-cognition dis-

course, this persevering problem that was given the

ostensive label “inert knowledge” is attributed to learn-

ing at school in general and especially to traditional

forms of instruction. As a consequence, characteristics

of informal out-of-school-learning contexts are explic-

itly appreciated.

Strongly inspired by the reform of medical educa-

tion that in the Netherlands was already brought for-

ward in the late 1960s, problem-based learning

environments were developed that share central design

principles with situated-learning models and combine

them with various methods of instructional support

(Stark et al. 2010). These “integrated learning environ-

ments” (Reinmann and Mandl 2006) use design fea-

tures of both “worlds”: the constructivist and the

technological position. Consequently, learners are

confronted with authentic (and therefore complex)

problems embedded in multiple contexts, multiple

perspectives on the problems to be worked on are

induced, and various forms of participation between

all parties involved in the learning process are realized.

In addition, learners are supported in multiple ways,

for instance by providing specific glossaries, prompting

and cooperation tools, feedback and explanation pro-

cedures, etc. Therefore, in contrast to overdrawn asser-

tions of the “direct-instruction-party” in recent

instructional debates (e.g., Kirschner et al. 2006),

these learning environments are not per se minimally

guided. In spite of their high complexity, they are not

necessarily incompatible with findings of the cognitive

load research and respective design recommendations

(Anyhow, these problems have to be taken far more

serious from the “constructivist party”).

Important Scientific Research andOpen QuestionsIn the German tradition, the evaluation of open

instruction primarily consists of impressionist descrip-

tions that do not meet conventional methodical stan-

dards of evaluation studies in psychology. There are

some empirical hints in favor of open instruction,

Open Instruction and Learning O 2515

O

especially with respect to dimensions like creativity,

curiosity, and autonomy of the learners, as well as

attitudes toward school and teachers. However, in

these comparative studies, learning outcomes and

achievement motivation were higher in traditional

learning environments. With regards to constructivist

variants of open instruction, the research base is better;

however, the pattern of results is confusing (Reinmann

and Mandl 2006). This problem is not at least due to

the specific theoretical and methodological complexity

of evaluating constructivist learning environments.

On the one hand, there is a lot of evidence

supporting the effectiveness of problem-based learn-

ing, especially with respect to complex knowledge

application and procedural knowledge. For example,

an influential meta-analysis including 43 field studies

comparing the effectiveness of problem-based and tra-

ditional learning environments by Dochy, Segers, Van

den Bossche, and Gijbels (2003) showed moderate

effects in favor of problem-based learning with respect

to knowledge application and retrieval. On the other

hand, traditional learning environments seem to be

more effective in fostering declarative knowledge, espe-

cially when learners’ prerequisites are low (e.g.,

Kirschner et al. 2006). A recent quasi-experimental

study from Stark et al. (2010) in the domain of teacher

education showed clear advantages of an integrated

problem-based learning environment with respect to

knowledge application, scientific quality of the

acquired knowledge, and various motivational dimen-

sions; in this study, no negative effects on declarative

knowledge were found. However, in another compara-

tive study of our research group (Krause et al in press)

in which at least to some extent other problem-based

and traditional design features were stressed, there were

clear advantages of the more traditional, content-

centered learning condition.

From our perspective, it is clear that the question of

effectiveness, like every interesting question in educa-

tional psychology, cannot be answered in general. The

answers have to be qualified by referring to relevant

learner prerequisites, instructional contexts and situa-

tions, domains, contents, time frames, and additional

dimensions of instructional goals. In addition,

theoretical and methodological problems especially

concerning aspects of the study’s design and its reali-

zation (e.g., the problem of internal and external valid-

ity), as well as choice and operationalization of

outcome measures, have to be critically reflected in

the context of comparative evaluation studies. There-

fore, typical “What-is-better-horse-race-studies”

already given up in other evaluation-research domains

(e.g., the evaluation of technology-based learning envi-

ronments) are based on naıve assumptions. This also

holds true for recent instructional debates (e.g.,

Kirschner et al. 2006) in which one instructional “phi-

losophy” is played off against the other. As

a consequence, their theoretical and practical relevance

is questionable. At best they are interesting from

a political and/or socio-psychological perspective.

Cross-References▶Action-Based Learning

▶Apprenticeship Learning in Production Schools

▶Approaches to Learning and Studying

▶Autonomous Learning

▶Constructivist Learning

▶Content-Area Learning

▶Creativity, Problem Solving and Learning

▶Dewey, John (1858–1952)

▶Direct Instruction and Learning

▶ Learner-Centered Teaching

▶ Learning by Problem Solving

▶ Learning Environments

▶Motivation and Learning: Modern Theories

▶Motivational Variables in Learning

▶Open Learning

▶Open Learning Environments

▶ Problem-Based Learning

▶ Self-Directed Learning and Learner Autonomy

▶ Self-Regulation and Motivation Strategies

▶ Situated Learning

▶ Social Construction of Learning

▶ Student-Centered Learning

▶Validity of Learning

ReferencesDochy, F., Segers, M., Van den Bossche, P. M., & Gijbels, D. (2003).

Effects of problem-based learning: A meta-analysis. Learning and

Instruction, 13, 533–568.

Kirschner, P., Sweller, J., & Clark, R. (2006). Why minimal guidance

during instruction does not work: An analysis of the failure of

constructivist, discovery, problem-based, experiential, and

inquiry-based teaching. Educational Psychologist, 41, 75–86.

Krause, U.-M., Stark, R., Herzmann, P. (in press). Forderung

anwendbaren theoriewissens in der Lehrerbildung: Vergleich

problembasierten und instruktionsorientierten lernens.

2516 O Open Learning

Psychologie in Erziehung und Unterricht. Preprint online.

Retrieved 23 March 2011, from http://www.reinhardt-verlag.de/

de/zeitschriften/peu/titel/50330/zsdnr/1120/abstract_de/

Peschel, F. (2002). Offener Unterricht. Idee, Realitat, Perspektive und

ein praxiserprobtes Konzept zur Diskussion. Band 1: Allgemein-

didaktische Uberlegungen. Band 2: Fachdidaktische

Uberlegungen. Schneider Verlag Hohengehren: Baltmannsweiler.

Reinmann, G., Mandl, H. (2006). Unterrichten und

Lernumgebungen gestalten. In A. Krapp, B. Weidenmann,

(Eds.), Padagogische Psychologie (pp 613–658). Weinheim: Beltz.

Stark, R., Herzmann, P., & Krause, U.-M. (2010). Effekte integrierter

Lernumgebungen – Vergleich problembasierter und instruktion-

sorientierter Seminarkonzeptionen in der Lehrerbildung.

Zeitschrift fur Padagogik, 56, 548–563.

Open Learning

HASAN CALISKAN

School Of Communication Sciences, Anadolu

University, Eskisehir, Turkey

Synonymse-Learning; Flexible learning; Online learning; Open

and distance learning

DefinitionThe term “open learning” is used to describe learning

situations in which learners have the flexibility to

choose from a variety of options in relation to the

time, place, instructional methods, modes of access,

and other factors related to their learning processes. It

should be understood from this perspective that

a learning situation or process should be open to every-

one, under any circumstances, at any place and at any

time. In many situations, the term open learning is

used interchangeably to refer to e-learning, flexible

learning, and distance learning.

The term “openness” refers to any teaching organi-

zation or institution that offers a variety of choices to

learners by giving them the opportunity to study and

learn in ways that are independent of time and place.

Current approaches to distance learning and e-learning

opportunities offer learners a wide range of learning

possibilities that take into account learners’ limitations

and preferences. Learning in this perspective should be

perceived as a lifelong process in which learners feel and

exercise a wide range of flexibility – especially outside

formal learning settings.

Theoretical BackgroundThere is a significant volume of literature dealing with

open and distance learning. Contributions to this area

of discourse generally approach the concept from dif-

ferent perspectives. In order to provide a theoretical

basis for the concept of open learning, some of its

major characteristics will first be outlined. Some of

the theoretical perspectives that emphasize the concept

from different angles will then be considered.

Open learning, distance learning, supported self-

study, informal adult learning, home study, e-learning,

lifelong learning, and flexible study concepts have been

used to describe methods of learning in higher educa-

tion for the past 40 years. From a learner’s perspective,

their openness is a common feature, which makes these

approaches to educational delivery so attractive.

The term “open” came into use as a result of the

approaches used by the Open University in the United

Kingdom. Indeed, the UK’s Open University was the

first to be named as open. Open University has since

been an example for many other higher education

institutions that want to offer open and distance learn-

ing courses. These universities or institutions may vary

in terms of the extent to which they are “open.” In the

field, the term “open education” has evolved into dis-

tance learning and later into open and distance learning

as a result of developments and trends that require

systems to place learners and their needs at the center

of teaching and learning processes. Another concept

that is used in open learning is open educational

resources (OERs). This term refers to content and

tools that are openly licensed for being used for educa-

tional purposes. Openness in terms of OER brings

about flexibility and freedom (to a certain extent) on

issues such as payment, use, copying, and derivative

works related to the originally created works.

Open learning is more of a system than of

a product. Sariola (1997) points out some of the char-

acteristics of an open system. Openness, according to

Sariola, has physical, didactic, psychological, and vir-

tual characteristics.

● Physical characteristics refer to the physical nature

of the learning situation and its accessibility by

learners. These characteristics determine whether

facilities are open to users at any time.

Open Learning O 2517

O

● Didactic characteristics are related to the learning

methods and evaluation processes. They try to

answer how learners study and learn.

● Psychological characteristics are related to themoti-

vational factors regarding learning. They try to

answer what motivates the learner and how?

● Virtual characteristics refer to the advanced media

and technologies used in teaching and learning

processes. They determine which technologies or

media best suit the needs of learners under partic-

ular circumstances.

Lewis (1990) in his review discusses the meaning of

open learning. According to some, open learning is an

alternative area where those who are unable to get

conventional education because of time and space con-

straints have a chance to attend alternative learning

situations. This view, as Lewis argues, limits the mean-

ing of open learning by putting the concept into the

same category as distance learning and, thus, having its

suitability for everyone questioned. For some other

people on the other hand, open learning is more than

just convenience. It gives learners not only the freedom

of choosing the best time and place to learn but also

gives the freedom of choosing what and how to learn.

According to this view, it empowers learners to take

control of their own learning in relation to the curric-

ulum, methods, and other processes that are used. So,

rather than just being a flexible way of delivering

instruction, open learning can now be seen as

a learning method that builds the active, autonomous,

and responsible learners equipped with skills that are

vital for the twenty-first century.

Williams (1996) provides a useful theoretical back-

ground for open learning. He explains the descriptive,

prescriptive, and explanatory theories of open learning.

Descriptive theory views open learning as a system of

teaching and delivery that employs advanced commu-

nication technologies and design considerations

thereby making learning process more flexible, accessi-

ble, and learner-centered. From this perspective, open

learning is approached as a way that concentrates on

removing the barriers to participation that learners

may encounter. To do this, open learning systems

incorporate a quite wide range of independent and

individualized teaching and learning strategies.

Prescriptive theory treats open learning as

a philosophical approach to education. In this view,

open learning is seen as being an approach to meet

a need or philosophy. Snell et al. (1987) suggest that

the purpose of open learning is to empower individuals

as learners who can make and question their own and

others’ meanings (Williams 1996).

Explanatory theory examines events and processes

with their relations to their surrounding factors such as

technological, economical, and political developments.

From a technological perspective, open learning can be

seen as an advanced application of distance education

systems. From an economic perspective, open learning

cannot be thought and discussed without the sur-

rounding economical or industrial terms and pro-

cesses. Instructional forms, designs, production

processes, specialties, teams, and distribution channels

are all affected by the prevailing economical terms.

Contemporary views of open learning appear to be

closely related to education of adults. Although educa-

tional theory is seen as a lifelong process, it generally

aims at the children learning, which is pedagogy. But

learning of adults is a lot different than pedagogy. Open

learning, on the other hand, is more for adults than it is

for children because of its openness. Especially for its

nature as being a form of non-formal education, many

adults might have a chance for making use of the

advantage of the flexible nature of the methods and

strategies of open learning. From this perspective, it can

be said that open learning facilitates lifelong and adult

learning.

Kember and Murphy (1990) view open learning as

an umbrella concept that embraces and contains dis-

tance learning, resource-based learning, correspon-

dence courses, self-paced learning, student-centered

learning, and flexible learning. From this perspective,

open learning opportunities in general can be said to be

a pool for non-formal education.

Important Scientific Research andOpen QuestionsOpen learning is not a new trend or concept that is

developing in parallel to the new advances and trends

in people’s lives. It can be traced back to distance

learning history with its relation to it. On the other

hand, there is relatively little in-depth analysis of its

effectiveness on other related areas and variables of

learning. Another point to make on open learning is

that there is confusion in the field as for what it is and

2518 O Open Learning Environments

its boundaries. For sure, open learning is not the same

as distance learning (Kember and Murphy 1990).

Research on open and distance learning in general

focuses on systems, theories, management, organiza-

tion, technology, teaching, and learning issues. Current

trends in the open and distance learning field may be

listed as political, economical, social, and technologi-

cal. Political trends involve access to and equality of

resources, economic developments and workforce

training, and cost-effectiveness of applications in

terms of teaching and learning. Economical trends are

related to information-based economy, better lifelong

trained and qualified workforce, new education and

training markets, and alternative sources for flexible

training system issues. Social trends involve develop-

ments in entertainment and leisure, demographics,

community activities, citizenship, health and crime,

and lifestyle change issues. Technology-related trends

focus on multimedia, distance-learning technologies,

personal and mobile technology, telecommunications,

television, computing, flexibility, access, information

highway, interactivity, and low-cost issues.

Contrary to the amount of research on open learn-

ing, the concept has been gaining more and more

attention each year. Access and equality in contrast to

traditional methods of learning have been two of the

most important factors that distance education

research has mainly concentrated on for quite a long

time. Many universities and institutions are now

trying to reach new target learner groups by moving

into global markets. These institutions use online

learning opportunities under the umbrella of open

learning to reach to learners who vary in terms of

time and place and many other characteristics. Since

new learners will bring about new cultural, political,

and structural demands and problems to the system,

flexibility issues might be said to gain more impor-

tance. Moreover, cross-cultural aspects, globalization

of education, access and equality, and ethics are

and will be important research issues in the field

(Jegede 1994).

Cross-References▶Adult Learning

▶Distance Education

▶Distance Learning

▶ e-Learning and Digital Learning

▶ Informal Learning

▶ Inquiry Learning

▶ Lifelong Learning

ReferencesHodgson, V., Mann, S., & Snell, R. (1987). Beyond distance teaching –

Towards open learning (pp. 161–170). Milton Keynes: Open

University Press.

Jegede, O. J. (1994). Distance education research priorities for Aus-

tralia: A study of the opinions of distance educators and practi-

tioners. Distance Education, 15(2), 234–253.

Kember, D., & Murphy, D. (1990). A synthesis of open, distance and

student centred learning. Open Learning, 5(2), 3–8.

Lewis, R. (1990). Open learning and its relevance to higher education.

Higher Education, 19(2), 259–269.

Sariola, J. (1997). The planning of an open learning environment and

didactic media choice in teacher education. www.edu.helsinki.fi/

media/mep6/sariola.pdf. Accessed 12 April, 2010.

Williams, H. M. (1996). Curriculum conceptions of open learning:

Theory, intentions, and student experience in the Australian

open learning initiative. Ph.D. thesis, Queensland University of

Technology, Brisbane.

Open Learning Environments

SUSAN M. LAND1, KEVIN OLIVER2

1Education Instructional Systems Program,

The Pennsylvania State University, University Park,

PA, USA2Department of Curriculum & Instruction,

North Carolina State University, Raleigh, NC, USA

SynonymsLearning environments; Open-ended learning environ-

ments; Student-centered learning

DefinitionOpen Learning Environments (OLEs) are rooted in

learner-centered design principles and highlight activ-

ities and contexts that “support the individual’s efforts

to understand what he or she determines to be impor-

tant” (Hannafin et al. 1994, p. 48). The term is used in

the sciences of learning as a general design framework

to describe environments that support personal sense

making via problem contexts enriched with technology

tools, resources, and scaffolding (Hannafin et al. 1999).

Open Learning Environments emphasize student- or

self-directed learning but provide guidance and

Open Learning Environments O 2519

O

support strategies to assist students to productively

engage complex, open-ended problems.

Theoretical BackgroundThe origin of OLEs appeared during the early 1990s in

response to emerging instructional-design consider-

ations that reflected constructivist views of learning

(Hannafin et al. 1994). These views reflected

a fundamental shift in paradigms of learning and

design, and few guidelines were available for designers

to create learner-centered environments. Likewise,

technology advancements at that time had begun to

enable integration of digital resources, tools, and inter-

net connectivity to expand the development toolkit of

instructional designers. These shifts in the learning-

design-technology landscape required corresponding

shifts in theoretical frameworks for designing new

learning environments that capitalized on affordances

of emerging technologies (Hannafin et al. 1994). Early

theoretical notions highlighted the importance of

alignment among psychological, pedagogical, techno-

logical, pragmatic, and cultural foundations of the

learning environment (Hannafin et al. 1999).

Open learning environments are based on several

key assumptions about the nature of learning, the

structure of the environments, and role of the learner

(Hannafin et al. 1999). One key assumption is that

learners’ own experiences, personal theories, or existing

beliefs mediate their learning. OLEs assume that indi-

vidual’s efforts to direct their own learning must start

with a recognition of what is already known. Initial

understanding is the basis for building more refined

understanding that can be examined, tested, and

revised through engagement with the OLE (Hannafin

et al. 2009; Land and Hannafin 1997).

Theoretical assumptions about the pedagogy

behind OLEs reflect authentic, problem- or project-

based contexts that organize individual efforts to

learn (Hannafin et al. 1999). Contexts for learning are

typically open-ended, suggesting that there is not one

correct answer or way to solve the problem. Activities

and contexts that readily connect to learners’ experi-

ences are assumed to increase relevance and engage-

ment. Tools and resources are provided to support

learners to represent and explore various aspects of

the problem as well as their ideas.

OLEs are based on an assumption that individual

monitoring and metacognition are important elements

of open-ended learning, and as such, require opportu-

nity to be utilized. OLEs are complex and open ended,

requiring learners to initiate reflection, monitoring,

and self-assessment of what is known and what needs

to be known (Hannafin et al. 1999). OLEs facilitate use

of metacognitive strategies but also assume that

learners will need support at critical points in the

learning process to identify needs and deploy effective

monitoring strategies (Oliver and Hannafin 2001).

OLEs represent a broad design framework for

environments that encourage open-ended learning.

OLEs are comprised of the following four components:

Enabling contexts, tools, resources, and scaffolds

(Hannafin et al. 1994, 1999). Enabling contexts repre-

sent the activity structures or problems that guide and

orient students to learning. They span a continuum of

structure – from contexts that specify problems and

outcomes to individually generated problems or issues

that are uniquely defined. Tools typically offer technol-

ogy-based support for representing, organizing,

manipulating, or constructing understanding.

Hannafin et al. (1999) characterize three types of

tools typically employed in OLEs:

● Processing tools (i.e., tools that aid in cognitive

processing, information seeking, collecting, orga-

nizing, integrating, and generating)

● Manipulation tools (i.e., tools that function based

on user input, changing and testing parameters,

and visualizing effects)

● Communication tools (i.e., tools that promote

social interaction and dialog)

Resources represent source information, and may

range from static information resources (e.g., text,

video) to dynamically evolving resources that are

socially constructed (e.g., WIKIs). Scaffolds are support

mechanisms designed to support individual’s efforts to

understand. Scaffolds are typically designed to provide

the following functions:

● Conceptual guidance on concepts related to the

problem

● Metacognitive guidance on how to reflect, plan, and

monitor

● Procedural guidance on how to use the environ-

ment’s features

● Strategic guidance on how to approach the task or

refine strategies

2520 O Open Learning Environments

The relationship among the various components is

interconnected and should reflect alignment among

core theoretical foundations. Current theoretical work

(Hannafin et al. 2009) emphasizes the importance of

identifying the cognitive and metacognitive demands

for open-ended learning, and delineating criteria for

design that inform the productive use of tools,

resources, and scaffolds for learning.

Important Scientific Research andOpen QuestionsOLEs can prepare students for complex problem solv-

ing in subjects that require such processes (e.g., science,

math, social studies), and they have been the subject of

research with a range of learners from adolescents to

adults. In one case study with four seventh grade sci-

ence students, Land and Hannafin (1997) employed

think-aloud protocols and interviews to trace student

thinking as they manipulated an OLE called

ErgoMotion. The goal was for students to learn physics

concepts by altering input parameters in a roller coaster

simulation, then test and refine personal theories to

explain outcomes. The authors identified eight com-

mon patterns of student thinking, noting students

develop personal theories to explain outcomes. Stu-

dents tended to assimilate conflicting data into their

naive theories, however, rather than elaborating and

retesting modified theories. The authors suggest

extended exposure to conflicting data, as well as diver-

gent contexts and perspectives, may be required before

students comprehend and stretch the limits of their

resilient initial conceptions.

In another case study with 12 eighth grade science

students, Oliver and Hannafin (2001) studied an OLE

called the Knowledge Integration Environment (KIE).

The goal was for students to find and resolve subprob-

lems associated with building collapse in earthquakes,

by reviewing, organizing, and annotating a collection

of Web pages and print resources, responding to ques-

tion scaffolds, and proposing design solutions.

Through this induced context, students were to break

the comprehensive engineering problem into manage-

able subproblems, but they struggled to do so with

limited prior knowledge. To improve problem fraction-

ation, the authors recommended simulating relevant

factors and encouraging students to reason analogically

from everyday objects. Students also failed to

adequately frame problems and justify their naive solu-

tions with collected evidence, so the authors

recommended bracketing searches with fewer

resources, requiring students to state hypotheses up-

front to guide their research (i.e., this evidence

supports my hypothesis) and requiring students to

communicate solution ideas with others to help

identify faulty reasoning.

Goldman et al. (1996) describe two experiments

with 44 fifth and 49 ninth graders, designed to test

content learning and attitudes resulting from use of

an anchored instruction video that externally imposed

the context for several embedded science problems

associated with a chemical spill. The video included

pauses allowing students to break into groups and work

with authentic materials and lab exercises to generate

problem solutions, before viewing the video again with

expert recommendations. Effects on attitudes were

negligible, but students in both fifth- and ninth-

grade treatment groups were significantly better able

to describe scientists and steps involved in dealing with

spills compared to students in a comparison groups

that only viewed a news segment about spills.

While each of these studies allowed students to

practice problem-solving processes in the context of

open-ended problems, they illustrate how OLEs can

differ in terms of tools and resources (e.g., dynamic

simulation versus static Web pages and video),

enabling contexts and the degree of open-endedness

(i.e., the anchored study imposed research questions

and made expert solution paths explicit), and scaffolds

(i.e., the ErgoMotion and anchored studies supported

recursive testing of processes through simulation and

multiple embedded problems, respectively, and the KIE

and anchored studies encouraged cooperative learning

from peers). In the ErgoMotion and KIE studies, mid-

dle-grade students’ solutions to open-ended problems

were resistant to change, and students did not subject

their models to revision as new evidence was encoun-

tered. It follows that the more open the environment,

the more time it may take students to recursively refine

their own models in reference to divergent perspectives

and contexts, be those from simulation, peer commu-

nication, and/or experts and instructors.

Educators can increasingly leverage the Web to

contextualize open-ended problems for students with

online video, virtual worlds, and ready access to experts

Open-Loop Process O 2521

O

and data. Also increasingly available are both static

resources such as open content and digitized primary

sources and dynamic resources such as interactive

learning objects. With ready access to context and

resources online, the key question for OLEs remains

how to best leverage emerging tools and encourage

student use of scaffolds as they inquire into complex

problem contexts and divergent resources, particularly

younger learners who may lack metacognitive abilities

to effectively use tools and scaffolds with intention as

shown (Hannafin et al. 2009).

The OLE tool types introduced by Hannafin, Land,

and Oliver (1999) have not changed, but the nature of

use has changed. Web 2.0 tools now allow students to

co-collect information into common social bookmarks

or drop boxes, co-organize information on group-

edited maps or displays, or collaborate in generating

positions and solutions on blogs or wikis. Such features

address the need in OLEs to communicate ideas and to

consider divergent perspectives from both resources

and peers. Web tools that interact with previously static

resources address the need in OLEs for students to

propose and justify novel solutions on the basis of

evidence. Students can now propose hypotheses and

use tools to collect, annotate, or visually organize evi-

dence, and embed or mash-up evidence in new forms.

Static resources become dynamic through reuse and re-

presentation. Emerging technology may also influence

scaffold delivery in OLEs. Since OLEs often present

heuristics to aid students in problem solving, person-

alized metacognitive suggestions may be generated by

future Web 3.0 systems that track learner trials and

errors in problem solving and offer the most pertinent

rules to help with recurring individual or common

group problems.

Cross-References▶Constructivist Learning

▶Design of Learning Environments

▶ Learning Technology

▶Open Learning

▶Resource-Based Learning

▶ Scaffolding for Learning

▶ Situated Learning

▶ Student-Centered Learning

▶Technology-Enhanced Learning Environments

ReferencesGoldman, S. R., Petrosino, A. J., Sherwood, R. D., Garrison, S.,

Hickey, D., Bransford, J. D., & Pellegrino, J.W. (1996). Anchoring

science instruction in multimedia learning environments. In

S. Vosniadou, E. De Corte, R. Glaser, & H. Mandl (Eds.),

International perspectives on the psychological foundations of

technology-based learning environments (pp. 257–284). New York:

Springer.

Hannafin, M. J., Hall, C., Land, S. M., & Hill, J. R. (1994). Learning in

open environments: Assumptions, methods, and implications.

Educational Technology, 34(8), 48–55.

Hannafin, M. J., Land, S., & Oliver, K. M. (1999). Open learning

environments: Foundations, methods, and models. In

C. Reigeluth (Ed.), Instructional-design theories and models:

Volume II (pp. 115–140). Mahwah: Lawrence Erlbaum.

Hannafin, M. J., Hannafin, K. M., & Gabbitas, B. (2009). Re-

examining cognition during student-centered, web-based learn-

ing. Educational Technology Research and Development, 57, 767–

785.

Land, S. M., & Hannafin, M. J. (1997). Patterns of understand-

ing with open-ended learning environments: A qualitative

study. Educational Technology Research and Development, 45(2),

47–73.

Oliver, K., & Hannafin, M. (2001). Developing and refining

mental models in open-ended learning environments: A case

study. Educational Technology Research and Development, 49(4),

5–32.

Open-Ended Learning

▶Deep Approaches to Learning in Higher Education

Open-Ended LearningEnvironments

▶Open Learning Environments

Open-Loop Process

Process that does not use information about its out-

comes as input.

2522 O Openness to Experience

Openness to Experience

STEPHEN J. DOLLINGER

Department of Psychology, MC 6502, Southern Illinois

University Carbondale 281-C, Life Sciences II,

Carbondale, IL, USA

SynonymsAbsorption; Culture; Factor V; Imagination; Intellect

DefinitionOpenness to Experience is a term used by personality

psychologists to denote one of the five broad factors of

personality. The others are Extraversion, Neuroticism,

Agreeableness, and Conscientiousness (McCrae and

Costa 1997b). Openness to Experience reflects the

extent to which people think in broad (vs narrow)

and deep (vs shallow) ways, and the permeability of

boundaries in their consciousness and experience

(McCrae 1993/1994; McCrae and Costa 1997a). The

concept of permeable boundaries is similarly evident in

concepts like open vs closed classrooms (i.e., humanis-

tic and student-centered vs teacher-/authority-cen-

tered) and open vs closed organizations or societies.

Thus, openness in general reflects the degree to which

information can flow in multiple directions within an

entity (person/classroom/society), thereby encourag-

ing or minimizing diversity of thought, feeling, and

action. To the extent that boundaries are open, indi-

viduality and freedom of expression are encouraged.

Being open, then, reflects a richness of inner life expe-

rience, broad interests, and receptivity to new ideas.

Being closed may reflect a narrow rigidity that

demands uniformity but more commonly would be

seen in conformity to what is conventional, and

a preference for down-to-earth or “tried-and-true”

ways of experiencing the world.

When applied to a person, the phrase “open book”

is sometimes used to describe someone who is friendly,

non-defensive, self-disclosive, and “easy to know”

rather than secretive. This use of “open” is inconsistent

with the broad factor of Openness to Experience.

A person described in such terms would likely score

highly on Agreeableness and Extraversion and low on

Neuroticism. There could also be an implication that

such an individual is “not too complex” which could

actually mean that he/she might have a low score on

Openness to Experience. In contrast, describing

a person as “open” in the sense of receptive to new

perspectives or information would be consistent with

the meaning of this personality factor.

Theoretical BackgroundSince the five-factor model or “big five” systematized

personality measurement, it should not be surprising

that earlier psychological constructs bear conceptual

similarity to Openness. The most important classic

constructs actually measured being closed to experi-

ence; these include right-wing authoritarianism, con-

servatism (which some take to be very similar

constructs), and dogmatism. In addition, older con-

structs like flexibility, tolerance of ambiguity, and

inquiring intellect bear similarity to the high end of

the Openness factor.

Openness to Experience is the term preferred by

Paul Costa Jr. and Robert McCrae, developers of the

most commonly used measure of the five-factor model,

the revised NEO Personality Inventory (NEO-PI-R).

The NEO-PI-R operationalizes Openness to Experi-

ence in terms of six facets or lower-order traits: Fantasy,

Aesthetics, Feelings, Actions, Ideas, and Values. The

fantasy component measures enjoyment of imaginative

products and thinking in terms of the “possible” rather

than the “real” or “actual.” Persons highly open to

fantasy may be seen by their less open counterparts as

unrealistic dreamers. Aesthetic interests include the

enjoyment of and active participation in the world of

the arts; it also reflects the experience of aesthetic chills

(McCrae 2007). The artist and poet can be taken as

prototypes of the open person (McCrae and Costa

1997a). The feelings facet refers to an interest in the

inner world of emotions akin to the older concept of

intraception or psychological-mindedness, and shares

commonality with the newer concept of alexithymia.

The actions facet consists of the preference for variety

and new forms of stimulation. Thus, openness shares

some meaning with the concept of sensation seeking.

(Sensation seeking also seems to involve a degree of

Extraversion.) The ideas facet taps the attitudinal com-

ponent of intelligence which is similar to the older

concept of needs for autonomy and understanding,

and the contemporary construct of need for cognition.

Open individuals enjoy deeper more effortful thought

and are attracted to the theoretical; closed individuals

Openness to Experience O 2523

O

avoid these. For example, open learners prefer more

elaboration-oriented strategies like discovering rela-

tionships, consulting the literature, and critical evalu-

ation (Blickle 1996). (Conscientious is the factor which

predicts more effortful work so the student who is both

open and conscientious would be preferred by most

faculty.) Finally, open values show up as a preference

for liberal as opposed to conservative positions on

political issues. Note, however, that one should not

treat openness and political attitudes as synonymous.

Rather the personality factor may be one of several

things that predispose people to lean in one or another

political direction.

Important Scientific Research andOpen QuestionsOpenness to Experience has been the most controver-

sial of the big five factors, with some scholars noting

that it possesses a greater breadth of meaning than the

other four. In addition, various names have been pro-

posed based in part on different instruments and mea-

surement approaches (e.g., Ostendorf and Angleitner

1994; Trapnell 1994). Some authors have chosen to

label this factor as Culture, reflecting a sophistication

and refinement of interests. Others prefer the term

Imagination, denoting an emphasis on the creative

and imaginative component of this cluster of traits.

Still others identify the factor as Intellect, focusing on

intellectual interests, preference for deeper and more

effortful cognition, and the factor’s correlation with

intelligence tests (typically on the order of .3). Others

refer to it simply as Factor V, allowing all interpreta-

tions and meanings to coexist and remaining open to

the findings of future research. (The roman numeral

for five was used because it was the fifth factor to

emerge in the earliest study identifying the big five.)

To appreciate Openness to Experience completely,

one should be cognizant of its wide range of correlates.

Among the positive correlates of Openness are absorp-

tion (openness to absorbing, self-altering experiences

like hypnosis), higher scores on measures of ego devel-

opment and moral development, accepting rather than

prejudicial attitudes toward racial and sexual minority

groups, breath of cultural interests (visual arts, movies,

reading, music) particularly in terms of abstract or

experimental art forms, interest in and ability to recall

one’s dreams, and need for autonomy. Among adoles-

cents, openness is seen in greater exploration of

occupational and personal identities rather than

foreclosing on the life roles and paths expected by

one’s family and friends. Openness relates to several

concepts in contemporary cognitive social psychology

such as need for cognition, need for closure, and per-

sonal need for structure as well as the older construct of

need for uniqueness. Open and closed individuals dif-

fer on the Jungian perceptual function of Sensing-Intu-

ition as measured by the very popular Myers-Briggs

Type Indicator. Openness correlates positively with

Intuition, reflecting the preference for perceiving the

world intuitively in its broad patterns and meanings; it

correlates negatively with Sensation, reflecting the pref-

erence of low open persons for perceiving the world in

terms of real, practical, and tangible details.

Open and closed persons differ in their values sys-

tems (followingMilton Rokeach and Shalom Schwartz,

the trans-situational goals that serve as guiding princi-

ples for life). Open individuals usually hold stronger

values in the domains of self-direction and stimulation;

closed individuals hold stronger values in the domains

of conformity, security, and tradition (Dollinger et al.

1996). Indeed, these domains help to define one of

Schwartz’s two broad value factors labeled openness

to change vs conservation. (His second factor is self-

transcendence vs self-enhancement but it is less rele-

vant to Openness to Experience.)

Although Extraversion and Agreeableness are con-

sidered the more interpersonal factors of the big five,

Openness does seem to influence social relations inso-

far as people tend to associate more often with those

who have similar political or religious values (McCrae

1996). Some evidence suggests that husbands and wives

are more similar on Openness than the other factors

(i.e., assortative mating) and that this pattern appears

for reported ideal qualities in mates as well as preferred

friends.

In terms of benefits for society, Openness to Expe-

rience is a major predictor of creativity (Dollinger et al.

2004; Feist 1998; McCrae 1987). First, this claim is

evident in terms of open individuals’ artistic interests

on John Holland’s hexagonal model of vocational pref-

erences. Second, Openness shows up as the most impor-

tant correlate of creative personality measures which

themselves predict creativity. Third, Openness corre-

lates with creativity-relevant processes such as prefer-

ences for more complex and asymmetrical visual

stimuli (relative to simple symmetrical stimuli) and

2524 O Operant Behavior

ability to generate uncommon or unique responses to

divergent thinking tasks. Fourth, it correlates with self-

reported creative accomplishments in a variety of

domains like literary work (poetry, writing, journal-

ism), visual arts (painting, drawing, sculpting), and

performing arts (dance, theater) plus crafts projects.

Fifth, Openness correlates with the creativity of

visual- and verbal-modality products judged by

expert raters (e.g., devising creative short stories or

drawings, creating richer more individualistic photo-

graphic essays about the self.) Finally, Openness to

Experience is a key personality trait in distinguishing

two pairs of groups: artists vs non-artists and scientists

judged by their peers as more versus less creative.

In short, Openness to Experience is similar to the

aspirational goal of a liberal arts education – being

intellectually curious about a range of domains in the

arts and sciences, having a deeper knowledge in some

areas in which one becomes more creatively produc-

tive, and being a critical thinker who does not accept

information on face value or on the word of an author-

ity figure. However, as a personality construct, the

variance in Openness to Experience seems to be better

explained by heredity than by a college education.

Cross-References▶Creativity and Learning Resources

▶Curiosity and Exploration

▶Dogmatism and Belief Formation

▶ Flexibility in Learning and Problem Solving

▶ Individual Differences

▶ Jungian Learning Styles

▶Measurement of Creativity

▶ Personality and Learning

ReferencesBlickle, G. (1996). Personality traits, learning strategies, and perfor-

mance. European Journal of Personality, 10, 337–352.

Dollinger, S. J., Leong, F. T. L., & Ulicni, S. K. (1996). On traits and

values: With special reference to openness to experience. Journal

of Research in Personality, 30, 23–41.

Dollinger, S. J., Urban, K. K., & James, T. A. (2004). Creativity and

openness: Further validation of two creative product measures.

Creativity Research Journal, 16, 35–47.

Feist, G. J. (1998). A meta-analysis of personality in scientific and

artistic creativity. Personality and Social Psychology Review, 2,

290–309.

McCrae, R. R. (1987). Creativity, divergent thinking, and openness

to experience. Journal of Personality and Social Psychology, 52,

1258–1265.

McCrae, R. R. (1993/1994). Openness to experience as a basic dimen-

sion of personality. Imagination, Cognition, and Personality, 13,

39–55.

McCrae, R. R. (1996). Social consequences of experiential openness.

Psychological Bulletin, 120, 323–337.

McCrae, R. R. (2007). Aesthetic chills as a universal marker of open-

ness to experience. Motivation and Emotion, 31, 5–11.

McCrae, R. R., & Costa, P. T., Jr. (1997a). Conceptions and correlates

of openness to experience. In R. Hogan, J. Johnson, & S. Briggs

(Eds.), Handbook of personality psychology (pp. 825–847). San

Diego: Academic.

McCrae, R. R., & Costa, P. T., Jr. (1997b). Personality trait structure as

a human universal. The American Psychologist, 52, 509–516.

Ostendorf, F., & Angleitner, A. (1994). Reflections on different labels

for factor V. European Journal of Personality, 8, 341–349.

Trapnell, P. D. (1994). Openness versus intellect: A lexical left turn.

European Journal of Personality, 8, 273–290.

Operant Behavior

FREDERICK TOATES

Department of Life Sciences, The Open University,

Milton Keynes, UK

SynonymsInstrumental behavior (though not entirely equivalent)

DefinitionOperant behavior is that which is said to meet two

conditions: (1) It is freely emitted by an animal, in the

sense that there is no obvious triggering stimulus. (2) It

is susceptible to reinforcement and punishment by its

consequences, such that it can be caused to go up or

down in frequency, respectively.

Theoretical BackgroundThe notion of operant behavior is closely tied to that of

operant conditioning, which in turn can be best under-

stood in the broader historical context of conditioning.

The earliest scientific research on conditioning was

carried out by Ivan Pavlov and concerned what came

to be known as “classical conditioning.” In the best-

known study, a dog was presented with a pairing of

sound and food. Thereby, the sound acquired

a capacity to trigger salivation. This exemplified

a change in behavior as a result of experience. The

sound was defined as a “conditional stimulus” since

its capacity to trigger salivation was conditional upon

Operant Behavior O 2525

O

the pairing with food. Psychologists applied

a stimulus–response model to such behavior. The stim-

ulus of the food triggered salivation (stimulus-

response), and, in the process of conditioning, the

sound came to form a stimulus–response association

with salivation. Behavior was said to be elicited by the

stimulus and was defined as “elicited behavior.”

Operant conditioning also exemplifies a pairing of

events and a change in behavior as a result. It is best

understood in terms of similarities and differences with

the technique of classical conditioning.

People ranging from circus trainers to school

teachers and parents have long known that much of

the behavior of both human and nonhuman species is

either more or less likely to reoccur in the future as

a result of the consequences that follow it. For example,

giving food to a hungry dog when it raises its paw is

likely to lead to an increased frequency of paw-raising

in the future when the dog is hungry. For another

example, a person touches a hot object, gets burned,

and is less likely in future to touch such an object. In the

twentieth century, psychologists started to do experi-

ments on such behavior to study its properties scien-

tifically. Edward Thorndike put cats into a so-called

puzzle box and measured the length of time that it

took them to manipulate a latch and escape from the

box to obtain the food that was outside. This time

decreased over trials suggesting a stamping in of the

behavior that led to the escape and food. The action of

turning the latch was described as instrumental behav-

ior, and the learning shown was termed “instrumental

conditioning.” Psychologists also worked with mazes,

which rats needed to navigate in order to find food at

the goal box. A stimulus–response model could be

applied here, the stimulus being the various locations

in the maze, and the response, the act of turning in

a particular direction at a given location.

Skinner devised a piece of apparatus that became

known as a Skinner box. Typically, a hungry animal

would be placed in a Skinner box and, as a result of

making a manipulation, obtains a pellet of food. For

a rat, the object of manipulation is normally a lever, and

rats usually press it with their front paws. For a pigeon,

a key needs to be displaced, which the pigeon normally

does by pecking it with its beak. The Skinner box

represents an example of instrumental conditioning

since behavior is instrumental in the outcome: gaining

food. However, it is a special form of instrumental

conditioning in which the animal paces itself, rather

than the experimenter pacing the events. The behavior

that is shown in the Skinner box is defined as “operant

behavior,” meaning that the animal operates on its

environment by performing operants. In contrast to

elicited behavior, that shown in the Skinner box is

defined as “emitted behavior.” Operants are freely emit-

ted by the animal, and the frequency of performing

them is sensitive to their consequences.

In the context of the Skinner box, a reinforcer is

defined as that which gives the experimenter control

over operant behavior, in that the tendency to exhibit

a particular behavior is strengthened by reinforcement.

This strengthening can consist of bringing the fre-

quency of showing a behavior up to a maximum level

or maintaining it at this level over time (in the absence

of food the animal would tend to slow its responding).

Food would normally be positively reinforcing to

a hungry animal in that the response shown just prior

to the food arriving increases in frequency as a result of

the food. Since there is no obvious triggering stimulus

that occurs just prior to the emission of an operant, the

stimulus–response model of elicited behavior seemed

to Skinner to be inappropriate for describing behavior

shown under these conditions.

Skinner advanced the notion of a “discriminative

stimulus,” which is different from the stimulus as it

appears in the expression “stimulus-response.” The

discriminative stimulus is one in the presence of

which a response either will or will not earn reinforce-

ment. For example, suppose that a pigeon earns food

only when a red light is on. In time, it will come to

respond only in the presence of the red light and refrain

from responding when the light is not on.

A technique known as “time-out from positive

reinforcement” is employed by therapists in trying to

reduce undesirable behavior, e.g., self-destructive

behavior. It consists of offering some form of reward

but then removing it when an undesired behavior is

exhibited.

Important Scientific Research andOpen QuestionsThe Skinner box was used to demonstrate the effect of

“schedules of reinforcement.” The term “continuous

reinforcement” describes where the reinforcement fol-

lows each response. By contrast, the term “partial rein-

forcement” describes where reinforcement does not

2526 O Operant Conditioning

follow every response. A schedule of partial reinforce-

ment usually takes one of two forms. If a number of

responses need to be emitted before a reinforcer is

earned, this is described as a ratio schedule. For exam-

ple, if the animal needs to press the lever five times to

get one pellet of food, this is known as a fixed ratio 5

schedule (FR5). If, following one reinforcement,

a minimum amount of time needs to elapse before

another can be earned, this is an example of an interval

schedule. For example, if a minimum of 1 min must

elapse, this would be described as a fixed interval 1-min

schedule (FI 1 min). The imposition of such schedules

was shown to have a profound effect upon the pattern

of responding. When reinforcement is omitted entirely,

animals tend to persist for longer before finally quitting

(as compared to the condition of continuous

reinforcement).

The notion of operant behavior and conditioning is

closely associated with the behaviorist era in psychol-

ogy and, in the hands of Skinner, linked to his

nontheoretical way of doing psychology. Since the

peak in popularity of behaviorism, this approach now

has a declining number of followers. However, operant

behavior, as shown in a Skinner box, is a vital tool in the

study of learning amongst many psychologists, who do

not necessarily share Skinner’s rejection of theorizing

and cognitive psychology.

Operant behavior, as studied in a Skinner box, has

proven to be of great value to psychology. Operant

conditioning is an effective way of changing the fre-

quency with which operant behavior is shown. There

can be no doubt as to the existence of the phenomena

of operant behavior and operant conditioning.

Reinforcing events increase the frequency of the behav-

ior that they follow. However, under certain condi-

tions, there is disagreement on what interpretation

should be placed upon the phenomena exhibited. For

example, in some cases, what appears to be the

strengthening of behavior by reinforcing events that

follow it might in fact represent classical conditioning.

A number of psychologists, while measuring oper-

ant behavior, adopt a cognitive model of events in the

Skinner box. They describe the rat as forming cogni-

tions (“expectancies”) of the kind that a response (lever

pressing) predicts an outcome (the arrival of food).

This is summarized as (response) ! (outcome). Such

research has provided evidence for the notions of goal

and purpose underlying operant actions.

Although operant behavior is normally described in

terms of its consequences rather than a triggering stim-

ulus, this does not prove that such a stimulus (or

a complex of stimuli) is not present. In the Skinnerian

tradition, it would be argued that the search for such

a stimulus/stimuli is fruitless and contributes nothing

to understanding behavior.

Cross-References▶ Partial Reinforcement Effect

▶ Punishment and Reward

▶Reinforcement Schedules

▶Reward-Based Behavioral Learning

▶ Schedules of Reinforcement

▶ Shaping of New Responses

▶ Skinner, B. F. (1904–1990)

ReferencesFlora, S. R. (2004). The power of reinforcement. Albany: State Univer-

sity of New York Press.

Pearce, J. M. (2008). Animal learning and cognition: An introduction

(3rd ed.). Hove: Psychology Press.

Skinner, B. F. (1953). Science and human behavior. New York: The Free

Press.

Toates, F. (2009). Burrhus F. Skinner. Basingstoke: Palgrave

Macmillan.

Operant Conditioning

This is the process that regulates the frequency of

appearance of the behaviors of an organism according

to the presence or absence of Reinforcement Stimuli

(þ/�).The use of a behavior’s antecedent and/or its

consequence to influence the occurrence and form of

behavior. Generally, a stimulus is presented to a subject.

If the subject’s response is appropriate, the subject

receives some reward; if the response is inappropriate,

the subject is punished. Positive reinforcement usually

results in the appropriate response occuring more

frequently.

Cross-References▶Associative Learning of Pictures and Words

▶Operant Learning

▶Role of Similarity in Human Associative Learning

▶ Schedules of Reinforcement

Operant Learning O 2527

Operant Learning

THOMAS S. CRITCHFIELD

Psychology Department, Illinois State University,

Normal, IL, USA

O

SynonymsInstrumental conditioning; Instrumental learning;

Operant conditioning

DefinitionOperant learning occurs when behavior changes as

a function of its consequences, i.e., the environmental

changes that follow the behavior. This definition is

similar to the Law of Effect proposed initially by

Edward Thorndike (1874–1949). The twomajor classes

of consequences are reinforcers, which strengthen the

behavior they follow, and punishers, which weaken or

suppress the behavior they follow. Reinforcers and

punishers may be (a) either positive (consisting of

stimulation added) or negative (stimulation removed);

(b) either primary (biologically salient) or conditioned

consequences (salient because of past experience); and

(c) either socially mediated or naturally occurring

without the involvement of other people. Operant con-

sequences may affect the probability of occurrence of

already-learned behaviors, or, though a process called

shaping, lead to the development of new types of

behavior (Madden et al. in press).

A minimal definition of operant learning also

includes situational events (often called discriminative

stimuli). That is, a given behavior-consequence rela-

tionship may occur in one situation but not in others;

the resulting behavior change tends to occur only in the

relevant situation. For example, a child with two par-

ents, only one of whom reinforces appropriate behav-

ior, may come to behave appropriately when with that

parent but not when with the other. Also important are

conditional stimuli, which regulate the applicability of

a behavior–consequence relationship to a given situa-

tion. Imagine, for example, that the normally-effective

parent tends to become timid, including by failing to

reinforce appropriate child behavior reliably, in the

presence of an overbearing grandparent. The child

would be expected to behave well for this parent except

when the grandparent (a conditional stimulus) is

present. This complex situation-dependency makes

operant learning readily adaptable to the demands of

a complex world (e.g., Madden et al. in press,

Chap. 17).

Theoretical BackgroundMuch of the scholarly tradition regarding operant

learning has proceeded within the theoretical frame-

work of behavior analysis, as articulated most promi-

nently by B.F. Skinner (1904–1990). Behavior analytic

theory focuses on identifying reliable functional rela-

tions between behavior and environmental events.

When behavior can be well explained in this way, no

need is seen to explain behavior in other ways, e.g., by

reference to mental events such as intentions, plans,

and goals. For this reason, behavior analysis is some-

times thought to ignore mental life, but Skinner’s posi-

tion was simply that phenomena like intentions, plans,

and goals should not be treated as primary cases of

behavior, but rather as behaviors (things people do)

themselves. Thus, mental events bear explaining just as

pubic behavior does, but since they tend to be inacces-

sible to scientific observation, Skinner maintained that

they should be understood by extending the same prin-

ciples that explain public behavior (Madden et al. in

press, Chap. 1).

Cognitively-focused accounts of operant learning

can be, and have been, proposed. For example,

a tradition tracing at least back to Edward Tolman

(1886–1959) assumes that experience with behavior–

consequence relations builds up expectancies, which

may be described as beliefs about what events will

follow behavior. In this view, behavior is a function of

these expectancies, and learning occurs when they are

violated by new experience.

Theoretical arguments do not typically bear on the

fundamental principles of operant learning, about

which there is wide agreement. These include that:

● The efficacy of consequences can be modulated

by a variety of contextual factors, sometimes called

motivating operations. For example, an empty

stomach may boost the reinforcing efficacy of

food, and the possible presence of buried treasure

can make a shovel – normally not a reinforcer for

many people – worth working for.

● In general, consequences become less effective in

changing behavior when they are delayed or do

2528 O Operant Learning

not occur reliably following behavior (Madden

et al. in press, Chap. 32). These effects are referred

to as delay discounting and probability discounting,

respectively.

● Consequences are most efficacious when they are

contingent upon, i.e., caused to occur by, behavior.

● Behaviors that have been strengthened by the same

consequences tend to covary in frequency (and are

said to “mean the same thing”), even when they do

not involve the same physical actions.

● Operant learning reflects not only the influences on

a given behavior, but also the influences on other

behaviors that might share the limited time and

effort available in any given situation. Specifically,

behavior is difficult to increase and easy to decrease

in frequency when competing behaviors are strong,

and easy to increase and difficult to decrease in

frequency when competing behaviors are weak

(Madden et al. in press, Chap. 15).

Important Scientific Research andOpen QuestionsAlthough many foundational ideas about operant

learning had been worked out by the mid-twentieth

century, frontiers in basic operant learning research

continue to be explored. Areas of recent progress

include understanding the moment-by-moment

dynamics of learning episodes, linking operant learning

to cellular and neurochemical events in the brain

(Madden et al. in press, Chap. 16), and developing

quantitative (i.e., equation-based) models that both

specify predictions more exactly and allow for the anal-

ysis of more complex phenomena than narrative theory

(Madden et al. in press, Chaps. 11 and 12). These

quantitative models also show promise when applied

to the everyday world (Madden et al. in press,

Chap. 31).

During the last half century, considerable attention

has been devoted to exploring the relevance of operant

learning to other disciplines. For instance, much has

been learned about drug abuse through research

treating drug doses as reinforcers, to the point where

operant procedures are considered a major preclinical

screening tool in determining the abuse potential of

new drugs (Madden et al. in press, Chap. 24). Operant

behavior also is a sensitive assay for studying selected

psychological effects of environmental toxicants

(Madden et al. in press, Chap. 33). Finally, the concept

of learning from consequences is now an important

consideration in some efforts to create artificial

intelligences.

Another area of progress involves applying princi-

ples of operant learning toward bettering the human

condition. Applied behavior analysis (once called

behavior modification) interventions exist for such

diverse issues as managing problem behavior and

building appropriate repertories in persons with

autism and other child and developmental disabilities;

enhancing work behaviors and promoting safety in

business and industrial settings; establishing reading

and other academic skills; promoting safe driving and

other prosocial behaviors; treating substance abuse;

and training pets and exotic zoo animals (e.g., Cooper

et al. 2007; Madden et al. in press, Chaps. 35, 36, 40, 44–

46.). These successes all depend to some degree on the

general “recipe” for operant learning that was described

above. Operant concepts also underpin a widely used

approach to behavioral assessment called functional

analysis, which is a set of procedures that look past

the topography or appearance of behavior (specific

problematic things that people say or do) to test what

kinds of operant effects have made the behavior strong

(e.g., it is positively reinforced by social attention vs.

negatively reinforced by creating escape from unpleas-

ant demands). This information is essential in planning

effective interventions (Cooper et al. 2007).

The preceding notwithstanding considerable con-

troversy lingers over the claim (e.g., Skinner 1957) that

operant processes underpin the most complex aspects

of human psychological functioning. Skepticism may

be fueled by the fact that fundamental operant princi-

ples often trace back to animal laboratory research. Of

particular importance to the controversy is emerging

research and theory development linking operant pro-

cess to phenomena such as concept formation, analog-

ical reasoning, and stereotyping, and applying related

principles to the development of promising therapies

for high-functioning adults (Hayes et al. 2001; Madden

et al. in press, Chap. 43; Madden et al. in press,

Chap. 17).

Among the conceptual issues still debated is

whether operant learning is a unique phenomenon or

an expression of other phenomena. For instance, Skin-

ner (1938) delineated important differences between

operant learning and classical conditioning, in which

reflexive behaviors become controlled by new eliciting

Opinion Dynamics O 2529

O

stimuli. As Skinner pointed out, classical conditioning

applies to reflexive behaviors whose physical form and

automatic, environmental triggers are specified by biol-

ogy, while operant learning applies to behavior that is

predestined in neither way. Yet ambiguities exist,

including that certain operant learning events (e.g.,

discriminative stimuli, conditioned consequences) are

thought to depend partly on classical conditioning, and

that habituation to operant reinforces has been dem-

onstrated that appears similar to the habituation to

eliciting stimuli that affects reflexive behaviors.

Anderson (2000) has proposed that operant learn-

ing is a special case of procedural (“knowing how”)

memory acquisition. This view places operant learning

separate from the acquisition of declarative (“knowing

about”) memories and is consistent with research

showing that individuals may not be consciously

aware of their own operant learning. Other research,

however, depicts a complex relationship between oper-

ant learning and learning from verbal information.

Verbal instructions, for example, can instate a new

behavioral repertoire faster than gradual shaping by

consequences, but this repertoire may be less readily

adaptable when situational demands change. Resis-

tance to change of rule-directed behavior can, however,

be less pronounced when verbal rules themselves are

gradually shaped via consequences than when they are

simply “told.” Finally, rule following itself may be

thought of as a situationally mediated class of operant

behaviors, the critical feature of which is what past

experiences and current discriminative stimuli indicate

the probable consequences of rule-specified behavior.

Perhaps surprisingly, given the long history of relevant

research, the relationship between operant learning and

verbal information is incompletely understood.

Cross-References▶Behavior Modification as Learning

▶Behaviorism and Behaviorist Learning Theories

▶Conditioning

▶Direct Instruction and Learning

▶ Law of Effect

▶Operant Behavior

▶Operant Variability

▶ Punishment and Reward

▶Reinforcement Learning

▶Reinforcement Learning in Animals

▶Reinforcement Schedules

▶ Skinner, B.F. (1904–1990)

▶Thorndike, Edward L. (1874–1949)

▶Tolman, Edward C. (1886–1944)

ReferencesAnderson, J. R. (2000). Learning andmemory: An integrated approach.

New York: Wiley.

Cooper, J. O., Heron, T. E., & Heward, W. L. (2007). Applied behavior

analysis (2nd ed.). Upper Saddle River: Prentice-Hall.

Hayes, S. C., Barnes-Holmes, D., & Roche, B. (Eds.). (2001). Rela-

tional frame theory: A post-Skinnerian account of human language

and cognition. New York: Springer.

Madden, G. J., Lattal, K. A., & Hackenberg, T. D. (Eds.) (in press).

Handbook of behavior analysis: Volume 1. Washington, DC:

American Psychological Association.

Madden, G. J., Dube, W. V., & Hanley, G. P. (Eds.) (in press).

Handbook of behavior analysis: Volume 2. Washington, DC:

American Psychological Association.

Skinner, B. F. (1938). The behavior of organisms. New York: Appleton-

Century-Crofts.

Skinner, B. F. (1957). Verbal behavior. New York: Appleton-Century-

Crofts.

Operant Variability

▶Reinforced Variability and Operant Behavior

Operationalization

In measurement operationalization refers to the process

of specifying exactly how theoretical concepts will be

measured.

Opinion

▶Attitudes – Formation and Change

Opinion Dynamics

▶ Social Influence and the Emergence of Cultural

Norms

2530 O Opinion Measures

Opinion Measures

Opinion measures encompass the variety of self-report

measures used to assess mental effort. Opinion mea-

sures assume that the investment of effort is a voluntary

process that is under the control of the individual and

as such is available for introspection.

Opinions

▶Attitude Change Through Learning

Optimal Experience

▶ Flow Experience and Learning

Optimal Thinking

▶Cognitive Efficiency

Optimism

▶Hope Theory and Hope Therapy

Optimization

▶Adaptive Proactive Learning with Cost-Reliability

Trade-off

Optogenetic

The use of genetic techniques to develop animal models

that allow for light-sensitive control of neural activity

at a variety of levels (e.g., from ion channels to neural

circuits).

Oral

Of or relating to the mouth; spoken, rather than writ-

ten. Oral imitation in music means imitation through

use of the voice. The oral tradition in music refers to

cultural music that has been passed from generation to

generation through storytelling and performances,

without the use of musical notation.

Orchestras

▶ Instrumental Learning in Music Education

Organizational Change andLearning

WENDELIN KUPERS1, JURGEN DEEG

2

1School of Management & International Business,

Massey University (Albany Campus), Auckland,

New Zealand2Chair of Business Administration, Leadership &

Organization, University of Hagen, Hagen, Germany

SynonymsChange in learning organizations; Organizational

transformation and organizational learning

Definition

Organizational Change and LearningOrganizational change is clearly one of the most irides-

cent terms in organization science, which can be

deducted from its various, different denotations and

synonyms (e.g., transformation, development,

dynamic). This terminological vagueness has become

a fundamental characteristic for theory and research. In

the first instance, the constituent part of the notion of

organizational change is the more general term of

change, which has become one of the most important

topics of social sciences or humanities altogether.

Thereby change is defined as a series of variances or

alterations leading from one state to another or to new

Organizational Change and Learning O 2531

O

forms or qualities of objects. As temporal phenomena,

such changes can only be discerned over a certain

period of observation (cf Van de Ven and Poole

1995). These variations, can – like organizations – be

viewed in two ways: Thus, on the one hand organiza-

tional change is an ex-post observed, eventuated alter-

ation; on the other hand it is the process, which is

taking place in the moment of observation.

Likewise, organizational learning can be defined as

a multidimensional process and accomplishment. As

such it comprises embodied, emotional, cognitive as

well as responsive, individual and/or collective dimen-

sions and levels in organizations. In addition to acquir-

ing knowledge, learning is also a form of making sense

or abstracting meaning, which involves relating parts of

the subject matter to each other and to a greater whole,

and thereby generates a comprehension and reinterpre-

tation of the known in the organizational context.

Accordingly, a learning organization actively creates,

captures, transfers, and mobilizes as well as modifies

knowledge between individuals and groups in

a systemic context to enable it to adapt to as well as to

act in a changing environment (Kupers 2008).

RelevanceTo assert that we live in an age of unprecedented change

and transformation, in which nearly every aspect of

modern life is affected by the rapidity and irreversibility

of such changes, has almost become a truism.More and

more organizations are under an increasing pressure to

respond to even more and more dramatic changes in

order to remain viable, profitable, or attractive (Deeg

2009). Thus the ability to cope with such changing

contexts is now a key variable for organizational suc-

cess, performance, and growth. Without the possibility

to change, organizations would rest upon linearity,

predictability, and readymade structures and artifacts

unviable in today’s context. As current organizations

are facing various challenges concerning an accelera-

tion of complex and discontinuous change processes,

various activities, such as restructuring, delayering,

downsizing, or outsourcing, etc., are increasingly part

of organizational realities. Additionally, being embed-

ded in competitive market dynamics, the necessity to

adapt to changes and pressure for innovation require

corporations to change and transform themselves con-

tinuously. As pressures toward change may even be

stronger in the future, the problem of change is more

virulent than ever and change can sometimes even

become a traumatic event for an organization and its

members. This growing relevance of change and its

management explains the increasing research in orga-

nization science. Correspondingly also learning in and

of organizations has becomemore andmore important

in today’s complex, uncertain, and dynamic business

environments. The growing rate of competitive chal-

lenges imposed by the global economy, the pace of

cultural and technological changes in products, pro-

cesses, and organizations and the often overwhelming

abundance of information is forcing organizations and

their members to appreciate the value of learning in

order to increase knowledge sharing, communication,

improve innovativeness, and effectiveness. As organi-

zational contexts are becoming increasingly

fragmented, learning is seen as a medium for more

effective and flexible acting and dealing with change

and has been critically discussed in organizational and

management literature (Bapuji and Crossan 2004;

Easterby-Smith and Lyles 2005).

Theoretical BackgroundOrganizational change is an old and continuously

reemerging topic in organization, management, and

leadership practices and studies. Work organizations

have always been an important realm for the develop-

ment of human beings and institutions. Although

organizations and its members have in the past

changed themselves, they are currently situated in soci-

etal and environmental contexts, which urge them to

more profound transformations and require a more

sophisticated theoretical advancement. Due to the

aforementioned relevance, understanding organiza-

tional change is nowadays commonly accepted as

a central issue within organization studies and also

one of the great themes in the social sciences. Unfortu-

nately, the scientific discussion of organizational

change is extremely disjointed with no commonly

accepted (unitary) theory of change at sight, as change

has been comprehended and conceptualized in many

different ways. For example, it has been seen as “orga-

nization development,” “transformation,” “turn-

around,” or “corporate renewal.” This wide variety of

perspectives on change has also generated many

models, typologies, and classifications of change or

change processes, which are “abstracting, fixing, and

labeling” (Chia 1999, p. 210) the complex and

2532 O Organizational Change and Learning

multifaceted ways and modes of changing. As many

concepts and models of organizational change repre-

sent more or less mere variations of structural contin-

gency theory, ideas of linearity, homogeneity, and

determinacy are dominant in thinking about change.

Furthermore, due to the oversimplifications of contin-

gency thinking a rather mechanistic understanding of

change is prevailing. Moreover, for a long time research

and theories concentrated on incremental and gradual

change and fostered models of organizational adapta-

tion or development. Such theorizing regards the mere

improving or adjusting of the existing structural form of

organization as sufficient for organizational survival or

as an adequate response for pressures to change coming

from the environment of the organization. Being com-

plex phenomena, organizational learning and the learn-

ing organization have been as well conceptualized from

various theoretical perspectives and comprehensively

investigated in empirical research (see▶Organizational

Learning). Learning in organizations is increasingly

considered a key area in management and organiza-

tional research using various methods (Easterby-Smith

et al. 2009). More recently, the social and temporal

structuring of multilevel and discontinuous learning

dynamics and its politics and unintended consequences

have been studied (Berends and Lammers 2010).

Linking Organizational Change andLearningBoth organizational change and learning are closely

connected, yet not entirely congruent with one another.

There is no transformational change without learning

and no learning in organizations takes place without

implemented change practices. As change represents an

important learning opportunity, learning itself is a kind

of change practice. Importantly, as an ongoing individ-

ual and social accomplishment and dynamic process,

change and learning are not static, embedded capabil-

ities or stable dispositions of actors, but constituted

and reconstituted in the dynamics of everyday practice.

As a capacity to interact, change and learning are com-

petencies and practices of actors and systems as agen-

cies to intervene or to let go in a flow of action,

respectively to modify the course of events in specific

contexts. These contexts consist of historical, social and

cultural and material realities and features. It is in these

multifacetet worlds that change and learning manifest

in a variety of forms and by the use of different media.

Practices of change and learning are circumscribed as

a “bricolage” of embodied, mental, sociocultural

resources. Therefore, the meaning of practices of

change and learning is related to specific local ways of

living and patterns of possibilities and habits, situated

within an integral nexus. Notwithstanding the above,

there can be an asymmetry or discrepancy between

change and learning in the form of a “rushing stand-

still”: Everything seems to change, i.e., modifications

on the surface within a logic of the same are taking

place, but almost no progress is made in terms of

a “real” or transformative learning on a more advanced

or sophisticated level. Furthermore, there is

a continuous need for changing the ways of learning

in and of organizations. To be effective as a learning

organization requires a move toward a deep learning

cycle involving fundamentally new ways of thinking

and interacting as well as innovative methods, tools,

and infrastructures for the sake of long-term growth

and change, in contrast to the short-term orientation of

most organizations today.

Important Scientific Research andOpen QuestionsOrganizational change is widely seen as a pattern of

reaction by which organizations can adapt to their

environment (adaptive change) – often due to a misfit

or resulting from an organizational crisis. Conse-

quently, the major part of research and literature has

so far focused on positive aspects of change, and is

seeking ways of mastering change. In particular,

planned change has been given preference to

unplanned change. Only a minor part of the discourse

on change has considered its problems and pathologies.

For example the resistance against change, structural

inertia preventing organizations from changing in due

time or downward spirals and decline as undesirable

developments have been analyzed. Finally, not only

considering organizations as agencies of change, but

also as the participants and agents of change (and

pioneers of learning) have gained considerable interest.

Yet, the discussion on organizational change is so far in

several respects substantially insufficient, not covering

the ambivalences involved. Following a pervasive “pro-

change bias” (Sturdy and Grey 2003) first, one impor-

tant research field for the future concerns the

unplanned and indeterminate characteristics of change

and learning, as both imply surprise, uniqueness, and

Organizational Knowledge Management O 2533

O

otherness. This requires exploring potential dangers

and dark sides in more detail. Second, research about

organizational change and learning has mainly focused

on gradual and incrementalistic change and thereby

widely neglected radical and discontinuous forms of

re-evolutionary processes, which need to be further

investigated (Deeg 2009). Third, most concepts of

change and learning are (implicitly or explicitly) still

based on the equilibrium model and regard change as

an exception of order and continuity. As change and

learning nowadays often emerge in dynamic and acute

ways, imbalance or steady state models are needed.

Accordingly, the changing nature of change toward

more discontinuity requires new ideas and conceptu-

alizations about how different processes of organiza-

tional change and learning and their interplay can be

analyzed, explained, and handled. As organization sci-

ence has for a long time been dominated by paradigms

of stability and continuity, while change and learning

have been viewed as an exception, epiphenomenon, or

episode, research is quite far from a mature compre-

hensive understanding of the different effects of time,

process, and discontinuity or context. Avoiding being

restricted to reifying definitions or mechanistic or

organic models, both change and learning in and by

organizations demand to be investigated as an embod-

ied relational and responsive event of transformation.

This should include considering developmental (indi-

vidual and collective) levels and lines within an integral

cycle of inter-learning (Kupers 2008). Methodologi-

cally, there is a need for more inter- and transdisciplin-

ary as well as real-time longitudinal research to uncover

process dynamics of learning, instead of retrospective

studies, which tend to highlight continuity and linear

development. Future research may also extend beyond

the organizational level of change and learning to

include embedding temporality, structures, and devel-

opments at environmental, sectoral, and societal or

sociocultural levels. The challenging realities of busi-

ness in contemporary world, calls for bringing change

and learning strategically and inclusively together for

developing more responsible and sustainable organiza-

tions and integral transformations, theoretically and

practically.

Cross-References▶Absorptive Capacity and Organizational Learning

▶Analogy-Based Learning

▶DICK Continuum in Organizational Learning

Framework

▶Management Learning

▶Organizational Learning

▶Technological Learning in Organizations

ReferencesBapuji, H., & Crossan, M. (2004). From questions to answers:

Reviewing organizational learning research.Management Learn-

ing, 35(4), 397–417.

Berends, H., & Lammers, I. (2010). Explaining discontinuity in

organizational learning: A process analysis.Organization Studies,

31(8), 1045–1068.

Chia, R. (1999). A “rhizomic” model of organizational change and

transformation: Perspectives from a metaphysics of change. Brit-

ish Journal of Management, 10, 209–227.

Deeg, J. (2009). Organizational discontinuity: Integrating evolution-

ary and revolutionary change theories. Management Revue,

20(2), 190–208.

Easterby-Smith, M., Li, S., & Bartunek, J. (2009). Research methods

for organizational learning. Management Learning, 40, 439–447.

Easterby-Smith, M., & Lyles M. A., (eds). (2005). The Blackwell

handbook on organizational learning and knowledge manage-

ment. Malden, MA: Blackwell.

Kupers, W. (2008). Embodied ‘inter-learning’ – An integral phenom-

enology of learning in and by organizations. The Learning Orga-

nisation: An International Journal, 15(5), 388–408.

Sturdy, A., & Grey, C. (2003). Beneath and beyond organizational

change management: Exploring alternatives. Organization,

10(5), 651–662.

Van de Ven, A., & Poole, M. S. (1995). Explaining development and

change in organizations. The Academy of Management Review,

20, 510–540.

Organizational KnowledgeManagement

Organizational knowledge management comprises

a range of concepts, methods, tools, and practices of

systematically identifying, representing, organizing,

searching, sharing, and using especially public knowl-

edge or information. As a management sector, organi-

zational knowledge management is connected to other

management disciplines like quality, process, personal,

and strategic management. The term knowledge man-

agement usually means organizational knowledge man-

agement, which focuses on the goals and interests of an

organization.

2534 O Organizational Learning

Organizational Learning

▶DICK Continuum in Organizational Learning

Framework

Organizational Learning fromError

▶ Learning from Failure

Organizational Learning fromFailure

▶ Learning from Failure

Organizational LearningProcess

These processes relate to practices, processes, and man-

agerial modes framing the evolution of existing knowl-

edge, and the creation of new knowledge assets to be

applied to the organisation’s activities.

Organizational Learning Theory

▶ Simulation and Learning: The Role of Mental

Models

Organizational Transformationand Organizational Learning

▶Organizational Change and Learning

Originality

▶Creativity and Its Nature

Orthogonal Factors

In factor analysis orthogonal factors are uncorrelated

factors.

Outcomes of Learning

AYTAC GOGUS

Center for Individual and Academic Development,

Sabanci University CIAD, Istanbul, Turkey

SynonymsEducational objectives; Instructional objectives; Learn-

ing outcomes; Student learning outcomes

DefinitionOutcome, in education literature, is the product or

evidence of students’ learning experience. Outcome

often refers to desired outcomes. Learning outcomes

are statements of what a successful learner is expected

to be able to do at the end of the process of a learning

experience such as the course unit or the course model.

Learning outcomes and learning objectives are often

used synonymously since learning objectives can be

written and used for the similar purpose that learning

outcomes are used for. However, course aims and

course objectives express the intention of the instructor

behind the introduced content or the intention of the

course from instructor’s point of view while learning

outcomes are concerned with the achievements of the

learner and expectations from the successful learner as

an end product of the course. Learning outcomes form

the basis for curriculum or academic program, course

syllabus, course development, as well as learning

assessment.

Outcomes of Learning O 2535

O

Theoretical BackgroundWatson (2002) defines a learning outcome as a change

within the person as a result of the learning experience.

According to Watson (2002), learning outcomes of

higher education are described based on four

approaches:

1. Course aim and course objectives: The stated inten-

tion of the course

2. Subject knowledge: The knowledge content com-

monly identified in syllabuses or course

documentation

3. Discipline: The notion of a discipline as a culture

and value system to which the graduate is admitted

4. Competence: What a graduate can do as a result of

the degree program, including occupational com-

petence (Watson 2002, p. 208)

In addition, Watson (2002) emphasizes that for the

purpose of using learning outcomes within higher edu-

cation, assessment must be both possible and appro-

priate; therefore, the desired learning outcomes of

higher education courses must be clearly stated and

assessable. A well-written learning outcome provides

a basis for planning, developing, delivering, and evalu-

ating an educational activity.

Learning outcomes, like educational objectives or

learning objectives, can be written for one of the three

domains of learning that were identified as the cognitive

domain, affective domain, and psychomotor domain

(Anderson et al. 2001; Krathwohl et al. 1964; Simpson

1966) which are summarized below:

● Cognitive domain: Acquisition of knowledge and

intellectual skills (Knowledge)

● Affective domain: Integration of beliefs and ideas

(Attitude)

● Psychomotor domain: Acquisition of manual and

physical skills (Skills)

The cognitive domain represents the intellectual

skills and knowledge processing. The affective domain

represents objectives that are concerned with attitudes

and feelings. The objectives in the psychomotor domain

concern what students might physically do (Anderson

et al. 2001; Krathwohl et al. 1964; Simpson 1966).

The cognitive domain involves knowledge and the

development of intellectual and critical thinking skills.

Six levels of the cognitive domain, called Bloom’s taxon-

omy, are knowledge, comprehension, application,

analysis, synthesis, and evaluation (Anderson et al.

2001). The six levels are classified hierarchically from

the simplest action to the high order thinking actions.

There are two subdivisions of the cognitive domain,

which are lower cognitive (knowledge and comprehen-

sion) and higher cognitive domains (application, anal-

ysis, synthesis, and evaluation). Also, the last three

levels of higher cognitive domain (analysis, synthesis,

and evaluation) concentrate on critical thinking skills.

Table 1 outlines these levels, sample learning outcomes,

and sample verbs used to write learning outcomes

(Anderson et al. 2001).

Lorin W. Anderson and David R. Krathwohl

revisited the cognitive domain in the learning taxon-

omy to reflect a more active form of thinking andmade

some changes such as changing the names in the six

categories from noun to verb forms, and rearranging

them slightly (Anderson et al. 2001). In contrast with

the single dimension of the original taxonomy, the

revised framework is two-dimensional. The two

dimensions are cognitive process and knowledge

(Anderson et al. 2001). The cognitive process dimen-

sion contains six categories from cognitively simple to

cognitively complex: remember, understand, apply, ana-

lyze, evaluate, and create. The knowledge dimension

contains four categories from concrete to abstract: fac-

tual, conceptual, procedural, and metacognitive

(Anderson et al. 2001). Anderson et al. (2001) defined

the new terms of the cognitive dimension in the revised

taxonomy as:

1. Remembering: Retrieving relevant knowledge from

long-term memory

2. Understanding: Determining the meaning of

instructional messages, including oral, written,

and graphic communication

3. Applying: Carrying out or using a procedure in

a given situation

4. Analyzing: Breaking material into its constituent

parts and detecting how the parts relate to one

another and to an overall structure or purpose

5. Evaluating: Making judgments based on criteria

and standards

6. Creating: Putting elements together to form a novel,

coherent whole or make an original product

(Anderson et al. 2001, pp. 67–68)

The affective domain relates to emotions, attitudes,

appreciations, and values, such as enjoying, conserving,

Outcomes of Learning. Table 1 Bloom’s taxonomy of educational objectives for the cognitive domain

Levels Sample verbs

LowerCognitive

1. Knowledge: Deals primarily with the abilityto memorize and recall specific facts

Define, list, identify, show, describe, recognize,label, outline, state, recall, match, outline, select,reproduce.Example: Describe the basic elements of X

2. Comprehension: Involves the ability tointerpret, and demonstrate students’ basicunderstanding of ideas

Comprehend, convert, contrast, distinguish,defend, infer, explain, extend, generalize,interpret, predict, translate, summarize.Example: Distinguish between X and Y

HigherCognitive

3. Application: Involves the ability to applyconcepts and principles to novel practicalsituations

Apply, solve, change, compute, construct,demonstrate, discover, manipulate, modify,operate, predict, prepare, produce, use.Example: Apply the rules of X in an actual situation

4. Analysis: Involves the ability to analyzeconcepts and separate concepts or principlesinto components

Analyze, break down, compare, contrast, diagram,deconstruct, differentiate, infer, separate, select,arrange, discriminate.Example: Analyze X based on the theory of Y

CriticalThinking

5. Synthesis: Involves the ability to blendelements and parts to form a whole

Integrate, categorize, combine, compile, design,modify, device, compose, rearrange, organize,generate, create, adopt, revise.Example: Synthesize the theories of X and Y with Z

6. Evaluation: Involves the ability to makejudgments of the value of a work

Decide, appraise, interpret, justify, summarize,judge, convince, rank, evaluate, critique, conclude,criticize.Example: Evaluate the ability of X

2536 O Outcomes of Learning

respecting, and supporting. The affective domain is

divided into five main subcategories: receiving,

responding, valuing, organization, and characterization.

Table 2 summarizes the meaning of the levels, sample

learning outcomes, and sample verbs (Krathwohl et al.

1964).

The psychomotor domain concerns things students

might physically do. Although no taxonomy of the

psychomotor domain was compiled by Bloom and his

coworkers, several competing taxonomies for the psy-

chomotor domain (e.g., Simpson 1966) have been cre-

ated over the years. One of the popular versions of the

taxonomy for the psychomotor domain belongs to E. J.

Simpson (1966). Simpson’s (1966) seven major catego-

ries of the psychomotor domain are listed from the

simplest behavior to the most complex: perception, set

(readiness to act), guided response, mechanism, complex

overt response, adaptation, and origination. Table 3

summarizes the meaning of the levels, sample learning

outcomes, and sample verbs (Simpson 1966).

Since 1956, the three domains of educational activ-

ities (cognitive, affective, and psychomotor) have been

used by educational psychologists, instructional

designers, and educators to write learning objectives

and learning outcomes.

Important Scientific Research andOpen QuestionsJenny Moon (2002) uses four categories of learning

outcomes as (1) development of knowledge and under-

standing; (2) cognitive or intellectual skills; (3) key or

transferable skills; (4) practical skills. Table 4 shows

three domains of learning (cognitive, affective, psycho-

motor) and categories of learning outcomes which are

listed under the four headings by Moon (2002). Moon

(2002) emphasizes that not all skills need to be devel-

oped to the level of the course or program.

Moon (2002) mentions using level descriptors

while developing learning outcomes. Level descriptors

are generic outcome statements of what a learner is

Outcomes of Learning. Table 2 Taxonomy of educa-

tional objectives for the affective domain

Levels Sample verbs

1. Receiving phenomena:Developing awareness ofsomething

Choose, describe, name,use, identify, locate.Example: Identify thegeneral properties of X

2. Responding tophenomena: Developingactive participation,willingness to respond, andmotivation

Answer, label, recite, write,report, discuss, help,present, perform.Example: Perform correctlycalculations on X

3. Valuing: Committingoneself to a particularobject, phenomenon, orbehavior

Justify, propose, read,report, select, share.Example: Demonstratebelief in the relevance of X

4. Organization: Makingjudgments or decisionsfrom among severalalternatives

Combine, organize,prepare, synthesize,complete.Example: Organize theprinciples of X to solve Y

5. Internalizing values(characterization):Integrating one’s beliefs,ideas, and attitudes intoa total, all-embracingphilosophy

Question, serve, qualify,practice, listen,discriminate.Example: Cooperate ingroup activities

Outcomes of Learning. Table 3 Taxonomy of educa-

tional objectives for the psychomotor domain

Levels Sample verbs

1. Perception: The ability touse sensory cues to guidemotor activity

Choose, describe, detect,differentiate, distinguish,identify, isolate, relate,select.Example: Relate therelevance of X in Y

2. Set (readiness to act):Mental, physical, andemotional sets

Begin, display, explain,move, proceed, react,show, state, interpret,volunteer.Example: Interpret theresults of X tests in Y

3. Guided response:Adequacy of performanceis achieved by practicing,imitation, and trial anderror

Copy, trace, follow, react,reproduce, respond.Example: Followinstructions to build X byusing Y

4. Mechanism: Developedconfidence and proficiencyof performance andhabitual learned responses

Assemble, calibrate,construct, dismantle,display, fasten, fix,manipulate, measure,organize, sketch.Example: Use a computerprogram accurately to do X

5. Complex overt response:The skillful performance ofmotor acts and automaticperformance

The same key words formechanism are used;nevertheless, the adjectivesor adverbs used indicatethat the performance isbetter, or faster, or moreaccurate, etc.Example: OperateX software quickly andaccurately

6. Adaptation: Use of well-developed skills to modifymovement patterns to fitspecial requirements

Adapt, alter, change,rearrange, reorganize,revise, vary.Example: Reorganize datato be able to interpret X

7. Origination: Creatingnewmovement patterns tofit a particular situation

Arrange, build, combine,compose, construct, create,design, initiate, make,originate.Example: Develop a newX program to analyze Y

Outcomes of Learning O 2537

O

expected to have achieved at the end of a level of

learning – in the case in higher education (Moon

2002). Level descriptors aim to guide the writing of

learning outcomes and may be translated into descrip-

tors for the discipline or program. Learning outcomes

are derived from consideration of level descriptors and

course aims (Moon 2002).

In giving practical advice for writing learning

outcomes, Moon (2002) states that well-written learn-

ing outcomes should:

● Be observable and assessable

● Begin with an action verb

● Have only one verb per learning objective

● Avoid vague terms like know, understand, learn, be

familiar with, etc.

● Be realistic within the timescale of the course to be

able to be achieved and assessed

● Be linked with program outcomes

● Be linked with teaching and assessment methods

Outcomes of Learning. Table 4 Domains of learning and

categories of learning outcomes

Domains oflearning Categories of learning outcomes

Cognitivedomain

1. Development of knowledge andunderstanding (subject specific);

● Knowledge

● Comprehension

2. Cognitive/intellectual skills(generic);

● Analysis

● Synthesis

● Evaluation

● Application

Affective domain 3. Key/transferable skills (generic);

● Group working, learningresources, self-evaluation,management of information,autonomy, communication, problemsolving

Psychomotordomain

4. Practical skills (subject specific)

● Application

● Autonomy in skill use

2538 O Outcomes of Learning

Writing learning outcomes allow instructors to

design learning activities and set up assessment tools.

Learning outcomes inform learners about what are

expected from them upon completion of the course,

thus learners develop a sense of ownership of their own

learning. Learning outcomes help instructors to more

precisely tell students what is expected of them, to

make it clear what students can hope to gain from the

course, and to design course activities and course eval-

uation systems, thus, provide clear information to help

students with their choice of program in higher educa-

tion (Moon 2002).

Developing learning outcomes of higher education

courses contribute to describing qualifications and

qualification structures throughout the European

Higher Education Area (EHEA) in the Bologna process

(Moon 2002). Learning outcomes promote the out-

come-based approach; however, developing learning

outcomes also tries to encourage moving from

a teacher-centered approach to a student-centered

approach which has been increasingly adopted by

European universities (Moon 2002) as well as other

countries. Developing learning outcomes in higher

education contributes to the mobility of students by

recognizing their diploma (Moon 2002).

Maher (2004) examines how learning outcomes are

used in higher education and lists both the proposed

benefits of learning outcomes and the potential draw-

backs of learning outcomes as below:

● Proposed benefits of learning outcomes (Maher

2004):

● Putting the student at the center of the learning

experience, from teaching to learning: Learning

outcomes offer a means by which attention can

be focused on the actual achievements of stu-

dents and this represents a more realistic and

genuine measure of the value of education than

measures of teaching input. Thus, the adoption

of a “learning paradigm” in Higher Education

(HE) puts the learner at the heart of the educa-

tional process, a proposition that appeals to

both teachers and students alike (p. 47).

● Accreditation of learning; recognizing student

achievement outside of the class: Learning out-

comes are also seen to have direct benefits for

accrediting students’ learning outside of the

class, by providing a clear indication of what

students are expected to achieve in relation to

specific awards (p. 47).

● Enhancing employability benefits for employers

and students: Learning outcomes enable univer-

sities to express student achievement beyond the

narrow boundaries of subject knowledge and to

articulate other important skills that are devel-

oped during the educational process. Key or

transferable skills, relevant professional skills

and personal qualities, formerly seen as by-

products of the educational process, are now

regarded as a core part of studying for a degree

(p. 48).

● A more open educational system; public infor-

mation, quality and accountability: Learning

outcomes can help institutions meet govern-

ment priorities. . . The specification of programs

of study using explicit learning outcomes allows

government to “benchmark” courses across the

higher education sector against nationally

Outcomes of Learning O 2539

O

established standards, thus ensuring that uni-

versities are delivering high quality and achiev-

ing value for money from public investment

(p. 48).

● Potential drawbacks of learning outcomes (Maher

2004):

● Stifling creativity: Rather than encouraging

learner autonomy and deep engagement with

the subject, learning outcomes may serve to

restrict learning and encourage a reductionist

approach where students merely aim to meet

minimum threshold standards as specified in

the learning outcomes (p. 49).

● One size fits all, the problem with using

a common set of generic level descriptors:

[There are] criticized approaches to curriculum

development that rely on a common set of

generic level descriptors such as those based on

Bloom’s taxonomy for framing learning

outcomes. . . [There are argumentations about

the fact] that different disciplines are very dif-

ferent in terms of the patterns of learning and

skills required at different levels (p. 49).

● The commodification of knowledge: The use of

learning outcomes in program specifications

and benchmarking statements are central to

the auditing process by the Quality Assurance

Agency (QAA). However, there is increasing

evidence that this imposition has not been wel-

comed by teachers. . . [since] learning outcomes

are viewed as a chore rather than a useful exer-

cise for improving teaching and learning. . .

[Also, it is believed] that increasing emphasis

on auditing and transparency in education has

led to the decline of trust and the disempower-

ment and demoralization of academics (p. 49).

Maher (2004) also gives three recommenda-

tions for using learning outcomes effectively in

higher education:

● Developing a broader conception of learning

outcomes: Defining learning outcomes should

not be seen as a “once and for all” activity, but

rather an iterative process that involves both

learners and teachers as active participants in

their development (p. 50).

● Making learning outcomes congruent with

good learning and teaching: Good teaching

and learning stems from a range of complex

interactions between student, teacher, setting,

and learning activities. A good teacher. . . is

adept at recognizing learning outcomes that

may emerge in the practical realities of

teaching. . . These unplanned outcomes or

“learning moments” are extremely important

in the educational process and can encourage

deep learning in students (p. 51).

● Encouraging creativity through learning out-

comes: Learning outcomes are often written in

a way that represents “threshold achievement”

or what a student needs to do to obtain

a minimum pass grade. Such an approach may

restrict creativity and new knowledge and even

encourage students to aim for the threshold

level. . . Assessment is at the core of students’

experience of higher education and it is impor-

tant that learning outcomes are designed to

encourage creativity within assessment tasks

(p. 52).

As a result, developing learning outcomes provides

clear information on the achievements and character-

istics associated with particular qualifications and

increase the transparency as well as the comparability

of standards between and within qualifications.

Cross-References▶Bloom’s Taxonomy of Learning Objectives

▶ Learning Objectives

▶Mastery Learning

ReferencesAnderson, L. W., Krathwohl, D. R., Airasian, P. W., Cruikshank, K. A.,

Mayer, R. E., Pintrich, P. R., Raths, J., & Wittrock, M. C. (Eds.).

(2001). A taxonomy for learning, teaching, and assessing:

A revision of Bloom’s taxonomy of educational objectives.

New York: Longman.

Krathwohl, D. R., Bloom, B. S., & Masia, B. B. (1964). Taxonomy of

educational objectives. The classification of educational goals;

handbook II: Affective domain. New York: David McKay.

Maher, A. (2004). Learning outcomes in higher education: Implica-

tions for curriculum design and student learning. Journal of

Hospitality, Leisure, Sport and Tourism Education, 3(2), 46–54.

Moon, J. (2002). The module and programme development handbook.

London: Routledge Falmer.

Simpson, E. J. (1966). The classification of educational objectives:

Psychomotor domain. Illinois Journal of Home Economics, 10(4),

110–144.

Watson, P. (2002). The role and integration of learning outcomes into

the educational process. Active Learning in Higher Education,

3(3), 205–219.

2540 O Outcomes-Based Education

Outcomes-Based Education

A design for teaching in which the learning outcomes

the students are intended to achieve, rather than the

topics to be taught, steer teaching and assessment. It

requires teachers to clarify how they want their stu-

dents to behave after having been taught, and to assess

students is in terms of how well those outcomes have

been achieved. Constructive alignment focuses also on

the learning processes the students need to adopt in

order best to achieve those outcomes.

Out-of-School Learning

▶ Learning in Informal Settings

Out-of-School Time Programs

▶ Learning and Development After School

Overconfidence

BARBARA HEMFORTH

Laboratoire de Psychologie et de Neuropsychologie

Cognitives, UFR Institut de Psychologie, CNRS,

Universite Paris Descartes, Boulogne-Billancourt,

Cedex, France

SynonymsCalibration of probability judgments

DefinitionThree phenomena are generally subsumed under the

concept of overconfidence:

1. The overestimation of one’s own performance

2. The overestimation of one’s performance compared

to others

3. The overestimation of the precision of one’s beliefs

Overconfidence has been blamed for high risk taking in

stock market trading, misjudging the probabilities for

winning a war, up to catastrophes like the Chernobyl

accident. The difficulty of the task in which perfor-

mance has to be estimated plays a significant role

(hard/easy effect). People overestimate their own per-

formance systematically in difficult tasks, whereas they

tend to underestimate it in easy tasks. At the same time,

they judge their own performance unrealistically worse

than that of others in difficult tasks and better than that

of others in easy tasks.

Theoretical BackgroundWhen faced with judgments about the probability of

certain events, people rarely apply Bayesian reasoning

in order to arrive at an approximately correct estima-

tion. General heuristics seem to play a more important

role than statistics (Kahneman et al. 1982). One conse-

quence of the heuristic nature of human reasoning is

that people are often more confident in the correctness

of their answers to difficult questions than appropriate.

Lichtenstein et al. (1982) summarize results from

a series of experiments demonstrating the phenome-

non of overconfidence in a variety of tasks. They typi-

cally presented their participants with questions testing

general world knowledge such as (1) or (2).

(1) Which city has more inhabitants?

● Cologne ● Frankfurt

How sure are you that your answer is correct?

● 50% ● 60% ● 70% ● 80%

● 90% ● 100%

(2) When was the gasoline-powered car invented?

● before 1880 ● after 1880

How sure are you that your answer is correct?

● 50% ● 60% ● 70% ● 80%

● 90% ● 100%

When participants’ answers are grouped by confidence

categories, perfect calibration would result in 50%

answers correct in the 50% confidence group, 60%

correct in the 60% confidence group and so forth.

This is, however, not the case in most experiments.

Typically, participants are about 80% correct when

they are 100% sure they are, and about 75% correct

when they are 90% sure (see Fig. 1). Overconfidence

seems, however, to be at least partly due to the way the

question is asked. While participants overestimate the

probability of being correct on a single-item basis,

when asked how many out of a series of questions

they answered correctly, they often do much better.

50%40%

60%

60%

70%

70%

80%

80%

90%

90%

100%

100%

confidence

accu

racy

50%

perfectcalibration

answerscorrect

Overconfidence. Fig. 1 Prototypical distribution of

accuracy plotted against subjective confidence. The gray

dotted line represents perfect calibration

Overconfidence O 2541

O

Experts of some domain are not necessarily exempt

from overconfidence. Meteorologists are generally

well-calibrated, whereas this is much less the case for

doctors and psychologists. Closely time-locked feed-

back as it available for weather apparently helps

adjusting the calibration.

Two phenomena that are often assumed to be

related to overconfidence are the overestimation of

one’s own performance (better-than-average phenom-

enon) compared to others and the overestimation of

the precision of one’s beliefs (see Moore and Healy

2008, for a recent overview).

– Better-than-average phenomenon: Many studies

report that a considerable number of participants

has a tendency to judge themselves being part of the

top 20%, 10%, or even 5% of some relevant peer

group. Far more than 50% of car drivers generally

believe that their driving performance is better than

average. This aspect of overconfidence may lead to

an Optimism bias where, for example, employees

adhere to compensation schemes where only top

performance is rewarded.

– Overestimation of the precision of one’s beliefs: The

subjectively estimated precision of one’s beliefs is

generally tested with the following procedure:

Participants are presented with a question such as

“How big is the Eiffel Tower?” They then have to

estimate the interval comprising the real value with

a probability of 90% (confidence interval). In many

cases, only 40–50% of the real values fall into the

estimated interval.

Overconfidence has classically been seen as one of the

reasoning fallacies humans are prone to. They consti-

tute a deviation from probabilistic reasoning para-

digms (such as Bayesian reasoning) which have been

assumed to result in optimal decision making even in

cases of uncertainty. Heuristics underlying the

overconfidence phenomenon are thus understood as

evidence for less than perfect human reasoning in the

“Heuristics and Biases” program (Kahneman et al.

1982). One of the cognitive biases underlying

overconfidence may be the fact that we have

a tendency to rather search for evidence confirming

our decisions than for contradictory information.

This Confirmation Bias makes us believe that there is

more substance to our decisions than there actually is.

The illusion of control in highly uncertain and uncon-

trollable situations may be another reason for

overconfidence.

Gigerenzer et al. (1991) as well as Gigerenzer (2007)

propose a fundamentally different account of the “mis-

judgments” people produce repeatedly. They consider

human heuristics in reasoning to be highly adaptive

given the constraints of limited time and huge amounts

of possibly relevant information we are confronted

with in real-world situations. We thus need “Fast and

frugal” mechanisms to arrive at a viable decision in

time. In Gigerenzer et al.’s Probabilistic Mental Models

(PMMs), the following Take-the-Best-Algorithm has

been shown to perform at least as good as many ratio-

nal algorithms for representative sets of two-choice

questions such as (1). Given, you do not know the

actual numbers of inhabitants of Cologne and Frank-

furt (i.e., you cannot make a direct decision based on

a Local Mental Model), you create a ranked list of

ecologically valid cues for the reference class under

consideration (German cities in this case). You then

consider the cue with the highest validity for big cities,

such as whether or not they have a first league football

team. If this is the case for only one of the cities, you

decide that this will be the one that is bigger. If not, you

look for the next best cue, such as whether the city has

2542 O Overconfidence

an international airport. The first discriminating cue

will serve as a basis for the decision. Since these fast and

frugal algorithms work well most of the time, confi-

dence in decisions based on them is fairly high. They

can, of course easily be tricked out by a special choice of

questions, such as “Which city is further North, New

York or Rome?” Test situations with representative

questions should, however, reduce overconfidence

considerably.

Important Scientific Research andOpen QuestionsMuch work has gone into the question of whether the

phenomenon of overconfidence is actually a real prob-

lem and not just the consequence of asking very hard or

even trick question as opposed to a representative set of

questions (Juslin 1994). Overconfidence seems actually

to be reduced when the set of questions asked is not

handpicked to be difficult.

Some inconsistencies with respect to

overconfidence and overplacement have inspired

more recent research. Why do people overestimate

their performance and underestimate their placement

compared to others for difficult tasks, whereas they

underestimate their performance and overestimate

their placement compared to others for easy tasks.

Moore and Healy (2008) propose that a lack of infor-

mation leads to a regression toward averages. Since

people generally know even less about the performance

of others than about their own, their judgments of

others’ performance will be more regressive than that

of their own performance. For easy questions, people

get most of the questions right, but since they know

they are only right in say about 70% of the cases on

average, they will underestimate their performance.

Still, they will underestimate others’ performance

even more. For difficult questions, on the other side,

they may only get about 40% right, but regressing

toward the average makes them overestimate their per-

formance and even more so for others’ performance.

Thus, they underplace themselves for hard questions.

Overconfidence is playing an important role in

economics, where it is still mostly viewed as a fallacy

causing sometimes disastrous decisions, whereas

Gigerenzer (2007) insists that it is one of the conse-

quences of the “gut feelings” of a brain adapted to

a complex and uncertain environment, which actually

arrive at the best decision more often than not and

possibly more often than sophisticated and complex

algorithms. An important research question is, by

which means it is possible to teach students and experts

to arrive at more appropriate judgments on one’s per-

formance in situations that are particularly prone to

overconfidence. Direct and precise feedback on indi-

vidual performances seems to play a central role since

erroneous self-judgments can be readjusted and cannot

stabilize this way. The meta-knowledge concerning the

uncertainty of a decision becomes part of the encoded

knowledge. A more natural representation of the prob-

lem (in this case, the judgment of the performance)

seems to be of considerable importance as well. People

generally have some experience as to how well they

usually perform in general knowledge questionnaires.

Their estimation of how many of the answers in the

questionnaire they answered correctly is hence much

better calibrated than the probability of the correctness

of individual answers. This is not only due to better

experience, however, but also to the fact that people are

notoriously bad at drawing inferences based on prob-

abilities and much better with natural frequencies

which are closer to our daily sampling of experiences.

The question to ask an expert in finances (or any other

field) would then not be “How sure are you that your

prediction is correct?” but “How often have your pre-

dictions been correct in the same set of circumstances?”

The meta-knowledge invoked here is essential to

recalibrating judgments on performance. Presenting

problems in an adequate format and teaching meta-

knowledge about the uncertainty of cue-based deci-

sions are two ways to overcome overconfidence.

Cross-References▶Confidence Judgments in Learning

▶Heuristics and Problem Solving

ReferencesGigerenzer, G. (2007). Gut feelings: The intelligence of the unconscious.

New York: Viking.

Gigerenzer, G., Hoffrage, U., & Kleinbolting, H. (1991). Probabilistic

mental models: A Brunswikian theory of confidence. Psycholog-

ical Review, 98(4), 506–528.

Juslin, P. (1994). The overconfidence phenomenon as a consequence

of informal experimenter-guided selection of almanac items.

Organizational Behavior and Human Decision Processes, 57(2),

226–246.

Overgeneralizations O 2543

Kahneman, D., Slovic, P., & Tversky, A. (1982). Judgment under

uncertainty: Heuristics and biases. New York: Cambridge Univer-

sity Press.

Lichtenstein, S., Fischhoff, B., & Phillips, L. (1982). Calibration

of probabilities: The state of the art to 1980. In D. Kahneman,

P. Slovic, & A. Tversky (Eds.), Judgment under uncertainty: Heu-

ristics and biases. Cambridge/New York: Cambridge University

Press.

Moore, D. A., & Healy, P. J. (2008). The trouble with overconfidence.

Psychological Review, 115, 502–517.

Overfitting

When a learner fits its training data too closely, and

does not draw conclusions which are as general as they

should be.

Overgeneralizations

When a learner draws inappropriately general conclu-

sions from its training data.

O