What is the Transfer of Learning in Psychology?Transfer of learning occurs when old knowledge is applied to solve a new problem.Transfer of learning is achieved when a student applies knowledge learned in the classroom to solve a new, more realistic problem. The likelihood that transfer of learning will be successful is based on many factors, including characteristics of the learner, the initial learning method and differences between the learning context and the context in which students must apply what they have learned.

1. Definitiono Whenever a student uses learned knowledge or skills to solve a never-before-seen problem, that student has successfully engaged in transfer of

learning. Literally speaking, the student has transferred the appropriate knowledge from the original learning context to the new problem-solving context.Transfer of learning is an important topic among educators and psychologists because the primary goal of classroom education is to teach students skills that they can transfer to problems that they will encounter in the workplace and in daily life.

2. Distance of Transfero One important distinction that psychologists make is the difference between "near" and "far" transfer. Near transfer refers to the student's ability

to solve problems that are very similar to problems that were initially taught during learning, while far transfer refers to the student's ability to solve a problem in a situation that is greatly different from the initial learning episode.In general, it is easier to teach people to succeed at near transfer (i.e., using geometric principles to solve new geometry problems) than it is to teach people to succeed at far transfer (i.e., using geometric principles to infer the height of a tower from the length of its shadow).

3. Factors Influencing Likelihood of Transfero There are several factors that influence the likelihood that successful transfer will occur. These factors fall into three general categories: the

teaching methods used during initial learning, characteristics of the learner and differences between the learning and transfer situations.Influence of the Initial Learning Method

o There are several qualities of the initial learning environment that affect whether students will be able to transfer learned information to solve new problems. In particular, when students initially learn through active problem solving it is more likely that they will be able to transfer what they learned to solve new, different problems. On the other hand, students in classrooms that focus on memorization, but not application, of information are less likely to engage in successful transfer.Characteristics of the Learner

o There are several characteristics of the individual learner that have been proposed to account for differences in ability to transfer knowledge to new contexts. Working memory capacity, IQ and Spearman's general intelligence quotient have all been hypothesized to represent an underlying ability to transfer knowledge between content areas.Differences Between Learning and Transfer Contexts

o In general, the greater the difference between the learning and transfer context, the less likely that successful transfer will occur. Factors that have been identified as affecting the likelihood of transfer include whether learning and transfer occur in the same physical place, the duration of time elapsed between learning and transfer, and whether the content domain changes between learning and transfer contexts.

Read more: What is the Transfer of Learning in Psychology? | eHow.com http://www.ehow.com/about_6651742_transfer-learning-psychology_.html#ixzz1SdMZZQkl

Factors Involved in Achieving an Effective Transfer of Learning

o Teaching is essentially transferring your past learning to a student. To successfully do so, you need to do more than just understand the subject matter. You also need to understand the key factors that affect learning -- motivation, feedback, application and understanding. By combining these factors in your lesson plans, you can make your teaching more effective.

Motivationo Students need to want to learn. This doesn't necessarily mean that they need to be extremely interested in the direct subject

matter, but they do need to be interested in what you're saying or the activity you're using. You need to motivate your students by using games, an engaging speaking style or anything else that will make them want to focus on you. You can impose discipline to force them to listen, but this is not as effective as making yourself entertaining enough for them to actually choose to focus on you and, by association, the subject matter.

Feedbacko Students also need feedback to encourage their learning. When they get a question right or make an insightful point, it is

your job to tell them they are on the right track. It's a psychological "more like this." If you encourage right answers, students will feel more inclined to give more right answers. What's more, they will know they are on the right track and will follow that same train of thought. Teaching isn't just throwing information at students -- it's letting them know when they are processing it correctly and when they are not and encouraging the former.

Applicationo Application is another key factor of learning. Students need to apply their knowledge through activities and discussions in

order to understand the point of it. By applying the knowledge they've acquired, they are making it relevant to their life, which solidifies it in their memory and gives them a reason to learn more.

Understandingo The combination of the other processes result in understanding. This is when the students assimilate the information,

understand it in context and can apply it on their own. Once understanding or "getting their head around it" is reached, the student has learned the concept. An example of this is in English teaching for speakers of second languages. Making students memorize vocabulary words is part of the learning process, but they aren't really English speakers until they can apply those vocabulary words in conversation. They need to be able to understand why they follow different rules rather than just blindly follow an algorithm. Understanding the difference between "better" and "best" is learning; pronouncing each word correctly without knowing its meaning is parroting.

http://www.ehow.com/list_7603934_factors-achieving-effective-transfer-learning.htmlBy Robert Haskell, University of New England, Biddeford, Maine, U.S.A.

Description Educators and educational psychologists recognize transfer of learning as perhaps the most significant issue in all fields of instruction. Transfer of learning cuts across all educational domains, curricula, and methods. Despite its importance, research and experience clearly show that significant transfer of learning in either the classroom or in everyday life seldom occurs. Simply put, transfer of learning is illustrated by the phrases "It reminds me of..." or "It's like..." or "It's the same as...". This book addresses the fundamental problem of how past or current learning

is applied and adapted to similar and/or new situations. Based on a review of the applied educational and cognitive research, as well as on the author's teaching experience with transfer of learning, this book presents a new framework for understanding and achieving transfer of learning. Current education and educational psychology textbooks either lack or lament the lack of research and guidance to educators on promoting transfer of learning. Thus this book is a necessary basis for all instruction and learning. Based on history and research, the book shows that transfer of learning is not just a technique of learning or instruction, but a way of thinking and knowing.Transfer of LearningTeaching for transfer is one of the seldom-specified but most important goals in education. We want students to gain knowledge and skills that they can use both in school and outside of school, immediately and in the future.Transfer of learning deals with transferring one's knowledge and skills from one problem-solving situation to another. You need to know about transfer of learning in order to help increase the transfer of learning that you and your students achieve.Transfer of learning is commonplace and often done without conscious thought. For example, suppose that when you were a child and learning to tie your shoes, all of your shoes had brown, cotton shoelaces. You mastered tying brown, cotton shoelaces. Then you got new shoes. The new shoes were a little bigger, and they had white, nylon shoe laces. The chances are that you had no trouble in transferring your shoe-tying skills to the new larger shoes with the different shoelaces.This example gives us some insight into one type of transfer of learning. Transfer occurs at a subconscious level if one has achieved automaticity of that which is to be transferred, and if one is transferring this learning to a problem that is sufficiently similar to the original situation so that differences are handled at a subconscious level, perhaps aided by a little conscious thought.However, there are many transfer of learning situations that are far more difficult than shoe tying. For example, a secondary school math class might teach the metric system of units. From the math class, students go to a science class. Frequently the science teacher reports that the students claim a complete lack of knowledge about the metric system. Essentially no transfer of learning has occurred from the math class to the science class.On a more general note, employers often complain that their newly hired employees have totally inadequate educations. Part of their complaint is that the employees cannot perform tasks on the job that they "should have" learned to do while in school. Schools respond by saying that the students have been taught to accomplish the tasks. Clearly, this is a transfer of learning problem that is owned jointly by schools, employees, and employers.The goal of gaining general skills in the transfer of your learning is easier said than done. Researchers have worked to develop a general theory of transfer of learning--a theory that could help students get better at transfer. This has proven to be a difficult research challenge.At one time, it was common to talk about transfer of learning in terms of near and far transfer. This "near and far" theory of transfer suggested that some problems and tasks are so nearly alike that transfer of learning occurs easily and naturally. A particular problem or task is studied and practiced to a high level of automaticity. When a nearly similar problem or task is encountered, it is automatically solved with little or no conscious thought. This is called near transfer. The shoe-tying example given above illustrates near transfer. A major goal in learning to read is to develop a high level of decoding automaticity. Then your conscious mind can pay attention to the meaning and implications of the material you are reading. A significant fraction of children are able to achieve this by the end of the third grade.Many potential transfer of learning situations do not lend themselves to the automaticity approach. There are many problems that are somewhat related, but that in some sense are relatively far removed from each other. A person attempting to make the transfer of learning between two such problems does not automatically "see" or sense the connections between the two problems. Far transfer often requires careful analysis and deep thinking.The theory of near and far transfer does not help us much in our teaching. We know that near and far transfer occur. We know that some students readily accomplish far transfer tasks, while others do not. We know that far transfer does not readily occur for most students. The difficulty with this theory of near and far transfer is that it does not provide a foundation or a plan for helping a person to get better at far transfer and dealing with novel and complex problems. It does not tell us how to teach to increase far transfer.In recent years, the low-road/high-road theory on transfer of learning, developed by Salomon & Perkins (1988), has proven to be a more fruitful theory. Low-road transfer refers to developing some knowledge/skill to a high level of automaticity. It usually requires a great deal of practice in varying settings. Shoe tying, keyboarding, steering a car, and single-digit arithmetic facts are examples of areas in which such automaticity can be achieved and is quite useful.High-road transfer involves: cognitive understanding; purposeful and conscious analysis; mindfulness; and application of strategies that cut across disciplines. In high-road transfer, there is deliberate mindful abstraction of an idea that can transfer, and then conscious and deliberate application of the idea when faced by a problem where the idea may be useful.References on Transfer of LearningPerkins, David N. and Salomon, Gavriel (September 2, 1992). Transfer of Learning: Contribution to the International Encyclopedia of Education, Second Edition Oxford, England: Pergamon Press. [Online]. Accessed 2/27/02: http://learnweb.harvard.edu/alps/thinking/docs/traencyn.htm. Quoting from the Website:High road and low road transfer. Salomon and Perkins (1989, Perkins and Salomon 1987) synthesized findings concerned with transfer by recognizing two distinct but related mechanisms, the ``low road'' and the ``high road.'' Low road transfer happens when stimulus conditions in the transfer context are sufficiently similar to those in a prior context of learning to trigger well-developed semi-automatic responses. In keeping with the view of Greeno et al. (in press), these responses need not be mediated by external or mental representations. A relatively reflexive process, low road transfer figures most often in near transfer. For example, when a person moving a household rents a small truck for the first time, the person finds that the familiar steering wheel, shift, and other features evoke useful car-driving responses. Driving the truck is almost automatic, although in small ways a different task.High road transfer, in contrast, depends on mindful abstraction from the context of learning or application and a deliberate search for connections: What is the general pattern? What is needed? What principles might apply? What is known that might help? Such transfer is not in general reflexive. It demands time for exploration and the investment of mental effort. It can easily accomplish far transfer, bridging between contexts as remote as arteries and electrical networks or strategies of chess play and politics. For instance, a person new to politics but familiar with chess might carry over the chess principle of control of the center, pondering what it would mean to control the political center.Salomon, G., & Perkins, D. (1988, September). Teaching for transfer. Educational Leadership, 22-32.Salomon and Perkins have developed the high-road/low-road theory of transfer of learning. The article listed here provides a good overview of the domain of transfer of learning and how to teach transfer. It also contains an extensive bibliography, so it is a good starting point if you want to study the research on transfer of learning.Transfer of Learning: Planning Workplace Education Programs [Online]. Accessed 4/8/01: http://www.nald.ca/nls/inpub/transfer/Engish/page01.htm. Quoting from the Website:Transfer of learning is pervasive in our everyday life at work, at home and in the community. Transfer takes place whenever our existing knowledge, abilities and skills affect the learning or performance of new tasks. But what are the principles of effective transfer of learning? How can workplace instructors design training programs to facilitate transfer? What can the shop floor supervisor do to encourage transfer of learning? How should trainees or participants prepare for transfer back on the job? Given the centrality of this topic to so many areas of workplace education, this discussion paper will draw together the results of research and some practical techniques that will help practitioners in

the field. It Is organized into four parts: 1) definitions of learning transfer, 2) factors influencing the transfer of learning, 3) integrating learning transfer into program planning and 4) strategies to enhance the transfer of learning. The report is summarized through a number of application exercises that challenges the reader to recall former workplace education experiences and interact with contents of the document. http://otec.uoregon.edu/learning_theory.htm

Transfer of Learning Information or skills related to one topic can sometimes either help or hinder the acquisition of information or skills related to another topic. When learning from one situation assists learning in another, this is referred to aspositive transfer. This positive transfer is most likely to occur when the learner

recognizes common features among concepts, principles, or skills; consciously links the information in memory; and sees the value of using what was learned in one situation in another (Schunk, 1996b).

For example, knowledge about the Revolutionary War may be helpful in understanding the Civil War. Knowledge of French may help a student learn Spanish. Skill at tennis may help a person learn racquetball. Positive transfer plays a major role in encoding (described in this chapter) and in many higher order thinking skills (described in the next chapter). When learning from one situation interferes with learning in another situation, this is referred to as negative transfer. This negative transfer is most likely to occur when the learner incorrectly believes there are common features, improperly links the information while encoding it, or incorrectly sees some value in using information from one setting in another. For example, knowledge of the Revolutionary War may actually confuse the student about events in the Civil War. Knowledge of French may confuse the student with regard to Spanish. Skill at tennis may cause a person to make mistakes at racquetball. Negative transfer is usually detrimental to learning and has been discussed as part of forgetting in this chapter.As was pointed out earlier, analogies can often serve a useful purpose by serving as advance organizers to alert students to activate pertinent information to help process incoming information. To the extent that the resulting connections are appropriate, analogies help promote positive transfer. However, a serious problem with analogies is that they usually activate some information that is inappropriate. To the extent that analogies lead to connections with inappropriate information, they can lead to negative transfer.A misconception is a particularly important type of negative transfer. Misconceptions were introduced in chapter 4 as part of the discussion of constructivism. In terms of chapter 4, a person with a misconception is likely to assimilate new information through faulty structures and consequently make inappropriate accommodations within those structures. In terms of the present chapter, the person is likely to store the information incorrectly in long-term memory or retrieve the improper information from long-term to working memory, and will consequently be likely to deal ineffectively with the information at hand.Positive transfer is a very important part of learning. In addition to helping learners acquire specific information more easily, positive transfer helps learners function effectively in situations for which they have no previously acquired information. It enables learners to solve problems they have never seen before. This aspect of positive transfer will be discussed in the next chapter. A major goal of education is to facilitate positive transfer and to minimize negative transfer.In a very real sense, no useful learning takes place unless positive transfer occurs. The only reason for teaching most topics in the classroom is to enable students to use what they learn in settings beyond the school. Knowledge that cannot be activated in new situations in which it is obviously applicable is referred to as inert knowledge. For example, Lochhead (1985) has found that eighty to ninety percent of American college students aren't really able to solve problems that require the application of ninth-grade algebraic principles, even though they can manipulate the symbols and meet standard behavioral objectives. An important goal of education should be to minimize inert knowledge by promoting positive transfer. The following are effective ways to promote positive transfer:

1. Teach subject matter in meaningful rather than rote contexts. This is a necessary but not sufficient step in promoting positive transfer. As earlier sections of this chapter indicated, information that is not meaningful will not be associated with other information and will be forgotten quickly.

2. Employ informed instruction. That is, students should learn not only to describe a concept or strategy, but also to understand when and why the concept or strategy is useful (Paris et al., 1982). The scaffolding strategies described in Chapter 12 will often be useful in delivering informed instruction.

3. Teach subject matter in contexts as similar as possible to those in which it will be employed. To the extent that information is learned in settings similar to those in which it will be applied, learners can use clues from the learning situation to trigger the use of appropriate skills and information when they are later needed.

4. Provide opportunities to practice employing the subject matter in settings that represent the full range of eventual applications. If all the practice takes place in a single, narrowly defined setting, then it should not be surprising that the learner will fail to apply it in settings that seem to be different. It is important to provide opportunities to practice in settings that represent an accurate sample of the full range of realistic applications that the learner is likely to encounter.

5. Provide opportunities for distributed practice after the information has been initially learned. Once information has been initially learned, the additional opportunities for practice in a variety of realistic settings described in the preceding guideline should be spread out over a lengthy period of time, rather than combined into a single study session.

6. Promote positive attitudes toward subject matter, so that students will feel inclined to deal with rather than avoid topics when they are encountered elsewhere. When people need an idea to deal with a new problem or a novel situation, they are more likely to draw upon learning about which they have positive feelings than learning that evokes hostility or resentment. The development of attitudes toward learning is discussed in Chapter 8.

Thinking skills are sets of strategies which we would like students to generalize to new settings. These are discussed in detail in Chapter 7.The preceding discussion has suggested that in order to be effective, instruction should be integrated with other instruction. Actually, it is desirable but not always essential for new learning to be integrated with other information. Morris, Shaw, & Perney (1990) have described a tutorial program what employed a constructivist approach to tutor children in reading skills. The tutorial approach would probably be considered preferable by most reading teachers, but the regular classroom teachers in this study simply were not going to use that approach. The tutors hardly talked to the classroom teachers at all and certainly made no attempt to integrate their efforts with those of the classroom teachers, but the students showed substantial improvement on standardized tests taken in the classrooms. Likewise, Pogrow's (19xx) HOTS program consistently generates improvements in subject areas, even though the HOTS teacher and the classroom teachers may be mutually unaware of one another's activities.

This kind of incidental transfer probably occurs because the students acquire useful skills that they spontaneously generalize to other situations in which these skills appear to be useful. Transfer is most likely to occur when instruction is integrated, as when the teacher helps students recognize the similarities between one setting and another. However, non-integrated development of useful skills is better than not having those skills at all.http://education.calumet.purdue.edu/vockell/edPsybook/Edpsy6/edpsy6_transfer.htm

Transfer of learning

From Wikipedia, the free encyclopedia

Transfer of learning is the study of the dependency of human conduct, learning, or performance on prior experience. The notion was originally

introduced as transfer of practice by Edward Thorndikeand Robert S. Woodworth.[1] They explored how individuals would transfer learning in one

context to another context that shared similar characteristics – or more formally how "improvement in one mental function" could influence

another related one. Their theory implied that transfer of learning depends on the proportion to which the learning task and the transfer task are

similar, or where "identical elements are concerned in the influencing and influenced function", now known as identical element theory. Transfer

research has since attracted much attention in numerous domains, producing a wealth of empirical findings and theoretical interpretations.

However, there remains considerable controversy about how transfer of learning should be conceptualized and explained, what its probability

occurrence is, what its relation is to learning in general, or whether it may be said to exist at all.[2]

Most discussions of transfer to date can be developed from a common operational definition, describing it as the process and the effective extent

to which past experiences (also referred to as the transfer source) affect learning and performance in a current novel situation (the transfer

target) (Ellis, 1965; Woodworth, 1938). This, however, is usually where the general consensus between various research approaches ends.

There are a wide variety of viewpoints and theoretical frameworks apparent in the literature. For review purposes, these are categorized as

follows:

a taxonomical approach to transfer research that usually intends to categorize transfer into different types;

an application domain-driven approach by focusing on developments and contributions of different disciplines that have traditionally been

interested in transfer;

the examination of the psychological scope of transfer models with respect to the psychological functions or faculties that are being

regarded; and

a concept-driven evaluation, which reveals underlying relationships and differences between theoretical and empirical traditions.

[edit]Transfer taxonomies

Of the various attempts to delineate transfer, typological and taxonomic approaches belong to the more common ones (see, e.g., Barnett & Ceci,

2002; Butterfield, 1988; Detterman, 1993; Gagné, 1977; Reeves & Weisberg, 1994; Salomon & Perkins, 1989; Singley & Anderson, 1989).

Taxonomies are concerned with distinguishing different types of transfer, and therefore less involved with labeling the actual vehicle of transfer,

i.e., what is the explanatory mental unit of transfer that is carried over. Hence, a key problem with many transfer taxonomies is that they offer an

excessive number of labels for different types of transfer without engaging in a discussion of the underlying concepts that would justify their

distinction; i.e., similarity and the nature of transferred information. This makes it very difficult to appreciate the internal validity of the models.

The following table presents different types of transfer, as adapted from Schunk (2004, p. 220).Type Characteristics

Near Overlap between situations, original and transfer contexts are similar.

Far Little overlap between situations, original and transfer settings are dissimilar.

Positive What is learned in one context enhances learning in a different setting. (+)

Negative What is learned in one context hinders or delays learning in a different setting. (+)

Vertical Knowledge of a previous topic is essential to acquire new knowledge. (++)

Horizontal Knowledge of a previous topic is not essential but helpful to learn a new topic. (++)

Literal Intact knowledge transfers to new task.

Figural Use some aspect of general knowledge to think or learn about a problem.

Low Road Transfer of well-established skills in almost automatic fashion.

High Road Transfer involves abstraction so conscious formulations of connections between contexts.

High Road/Forward Reaching Abstracting situations from a learning context to a potential transfer context.

High Road/Backward Reaching

Abstracting in the transfer context features of a previous situation where new skills and knowledge were learned.

(+) from Cree and Macaulay, (2000). (++) from Ormrod (2004).

Apart from the effect-based distinction between negative and positive transfer, taxonomies have largely been constructed along two, mostly tacit,

dimensions. One concerns the predicted relationship between the primary and secondary learning situation in terms of the categorical overlap of

features and knowledge specificity constraints. The other concerns general assumptions about how transfer relationships are established, in

terms of mental effort and cognitive process.

[edit]The effect-perspective: positive vs. negative transfer

Starting by looking at the effect side of transfer – in terms of the common performance criteria, speed and accuracy – transfer theories

distinguish between two broad classes that underlie all other classifications: negative and positive transfer. Negative transfer refers to the

impairment of current learning and performance due to the application of non-adaptive or inappropriate information or behavior. Therefore,

negative transfer is a type of interference effect of prior experience causing a slow-down in learning, completion or solving of a new task when

compared to the performance of a hypothetical control group with no respective prior experience. Positive transfer, in contrast, emphasizes the

beneficial effects of prior experience on current thinking and action. It is important to understand that the positive and negative effects of transfer

are not mutually exclusive, and therefore real-life transfer effects are probably mostly a mixture of both.

[edit]The situation perspective: specific vs. general, near vs. far transfer

The situation-driven perspective on transfer taxonomies is concerned with describing the relation between transfer source (i.e., the prior

experience) and transfer target (i.e., the novel situation). In other words, the notion of novelty of the target situation per se is worthless without

specifying the degree of novelty in relation to something that existed before. Butterfield and Nelson (1991), for example, distinguish

between within-task, across-task, and inventive transfer. A similar classification approach reappears in many situation-driven transfer taxonomies

(e.g., similar vs. different situations,example-to-principle and vice versa, simple-to-complex and vice versa) and can be noted as distinctions

made along the specific vs. general dimension. Mayer and Wittrock (1996, pp. 49ff.) discuss transfer under the labels of general "transfer of

general skill" (e.g., "Formal Discipline", Binet, 1899), "specific transfer of specific skill" (e.g., Thorndike’s, 1924a, b, "identical elements" theory),

"specific transfer of general skill" (e.g., Gestaltists' transfer theory, see origins with Judd, 1908), and "meta-cognitive control of general and

specific skills" as a sort of combination of the previous three views (see, e.g., Brown, 1989).

Haskell's (2001) taxonomy proposes a more gradual scheme of similarity between tasks and situations. It distinguishes between non-specific

transfer (i.e., the constructivist idea that all learning builds on present knowledge), application transfer (i.e., the retrieval and use of knowledge on

a previously learned task), context transfer (actually meaning context-free transfer between similar tasks), near vs. far transfer, and finally

displacement or creative transfer (i.e., an inventive or analytic type of transfer that refers to the creation of a new solution during problem solving

as a result of a synthesis of past and current learning experiences). Both near and far transfer are widely used terms in the literature. The former

refers to transfer of learning when task and/or context change slightly but remain largely similar, the latter to the application of learning

experiences to related but largely dissimilar problems.

[edit]The process perspective

The specific vs. general dimension applies not just to the focus on the relation between source and target, i.e., from where to where is

transferred, but also to the question about the transfer process itself, i.e., what is transferred and how. Reproductive vs. productive transfer (see

Robertson, 2001) are good examples of this type of distinction, whereas reproductive transfer refers to the simple application of knowledge to a

novel task, productive transfer implies adaptation; i.e. mutation and enhancement of retained information.

A similar dichotomous distinction is the one between knowledge transfer and problem-solving transfer (Mayer & Wittrock, 1996). Knowledge

transfer takes place when knowing something after learning task A facilitates or interferes with the learning process or performance in task B.

Knowledge used is referred to by many different terms, such as declarative or procedural types (Anderson, 1976), but it means that there are

representational elements that suit A and B. Problem solving transfer, on the other hand, is described as somewhat more "fluid knowledge"

transfer, so that experience in solving a problem A helps finding a solution to problem B. This can mean that the two problems share little in

terms of specific declarative knowledge entities or procedures, but call for a similar approach, or solution search strategies (e.g., heuristics and

problem solving methods).

The issues discussed in problem-solving transfer literature are also closely related to the concepts of strategic and theoretic transfer (Haskell,

2001, p. 31), and cognitive research on analogical reasoning, rule-based thinking and meta-cognition. Indeed, far transfer can be considered as

the prototypical type of transfer, and it is closely related to the study of analogical reasoning (see also Barnett & Ceci, 2002, for a taxonomy of far

transfer). Within the problem-solving literature the distinction between specific and general methods is made mostly with reference to Newell and

Simon's (1972) strong vs. weak problem solving methods (Chi, Glaser & Farr, 1988; Ericsson & Smith, 1991; Singley & Anderson, 1989;

Sternberg & Frensch, 1991).

Another concern that is frequently addressed in transfer taxonomies is the question of conscious effort. High-road vs. low-road transfer (Mayer &

Wittrock, 1996; Salomon & Perkins, 1989) expresses a distinction between such instances of transfer where active retrieval, mapping, and

inference processes take place, as opposed to those instances that occur rather spontaneously or automatically. Hence, low-road transfer

concerns frequently employed mental representations and automated, proceduralized knowledge, and occurs preferably in near transfer settings.

In contrast, high-road transfer is more conception-driven, and requires cognitive and meta-cognitive effort.

[edit]Traditional fields of transfer research

There are a nearly unlimited number of research fields that share some applied interest into the study of transfer, as it pertains to learning in

general. Three fields that contributed in most substantial ways to the progress of transfer research, both from a conception and empirical point of

view, are the fields of education science, linguistics, and human-computer interaction (HCI). In fact, most transfer research has been conducted

in reference to one of these applied settings, rather than in basic cognitive psychological laboratory conditions.

[edit]Education science: teaching for transfer

Due to their core concern with learning, educational science and practice are the classic fields of interest regarding transfer research, and

probably the prime target for the application of theories. Transfer of learning represents much of the very basis of the educational purpose itself.

What is learned inside one classroom about a certain subject should aid in the attainment of related goals in other classroom settings, and

beyond that it should be applicable to the student's developmental tasks outside the school; the need for transfer becomes more accentuated.

This is because the world educators teach in today is different from the world they themselves experienced as students, and differs equally from

the one their students will have to cope with in the future.

By nature of their applied interest, educationalists' main concern has been less with the question of how transfer takes place, and much more

with under what conditions, or, that it happens at all. The basic conviction that student's learning and achievement levels depend primarily on

learning and achievement prerequisites, has constituted a central part in educational learning theories for quite some time (Gage & Berliner,

1983; Glaser, 1984). The major focus in educational transfer studies has, therefore, been on what kind of initial learning enables subsequent

transfer: teaching for transfer. Research on learning and transfer has identified key characteristics with implications for educational practice.

[edit]From Formal Discipline to meta-cognition

Educational transfer paradigms have been changing quite radically over the last one hundred years. According to the doctrinaire beliefs of the

Formal Discipline (Binet, 1899) transfer was initially viewed as a kind of global spread of capabilities accomplished by training basic mental

faculties (e.g., logic, attention, memory) in the exercise of suitable subjects, such as Latin or geometry. With the turn of the 20th century,

learning, and therefore transfer of learning, was increasingly captured in behavioral and empiricist terms, as in the Connectionist and

Associationist theories of Thorndike (e.g., 1932), Guthrie (e.g., 1935), Hull (e.g., 1943), and Skinner (e.g., 1938). Thorndike (1923, 1924a and b)

attacked the Formal Discipline empirically and theoretically and introduced the theory of "identical elements", which is probably still today the

most influential conception about transfer (Thorndike, 1906; Thorndike & Woodworth, 1901a, b and c). Thorndike's belief that transfer of learning

occurs when learning source and learning target share common stimulus-response elements prompted calls for a hierarchical curricular structure

in education. "Lower" and specific skills should be learned before more complex skills, which were presumed to consist largely of configuration of

basic skills. This small-to-large learning, also referred to as part-to-whole or vertical transfer, has been popular with theories of learning

hierarchies (Gagné, 1968).

It has later been challenged from conceptualistic point of views, which argue that learning is not just an accumulation of pieces of knowledge

(i.e., rote memorization), but rather a process and product of active construction of cognitive knowledge structures (Bruner, 1986; Bruner,

Goodnow & Austin, 1956). Knowledge, from a constructivist perspective, was no more believed to be a simple transfer by generalization to all

kinds of situations and tasks that contain similar components (i.e., stimulus-response patterns; see also Logan, 1988; Meyers & Fisk, 1987;

Osgood, 1949; Pavlov, 1927).

The critical issue was the identification of similarities in general principles and concepts behind the facades of two dissimilar problems; i.e.,

transfer by insight. This idea became popular in the Gestaltists' view on transfer (e.g., Katona, 1940), and, in combination with growing interest in

learners as self activated problem-solvers (Bruner, 1986), encouraged the search for abstract problem-solving methods and mental schemata,

which serve as analogy-enhancing transfer-bridges between different task situations.

Emerging from these developments, a new theme started to dominate educationalists' research in transfer: meta-cognition (Brown, 1978; Brown

& Campione, 1981; Campione & Brown, 1987; Flavell, 1976). In contrast to classical knowledge forms like declarative and procedural

knowledge, different types of meta-knowledge and meta-cognitive skills such as strategic knowledge, heuristics, self-monitoring skills, and self-

regulation quickly became the road to learning and transfer. Characterized as self-conscious management and organization of acquired

knowledge (Brown, 1987) it is evident that meta-cognitive awareness of task features, problem structures, and solution methods makes relations

between different situations cognitively salient: only an individual who learns from learning, learns for future learning. Soini (1999) developed on

the same core ideas an examination of the preconditions for active transfer. Her emphasis is on the active and self-reflected management of

knowledge to increase its accessibility. To some researchers, meta-cognition and transfer have become so entangled that the argument was

generated that only the measurement of positive transfer effects truly supports inferences that meta-cognitive learning has taken place (e.g.

MacLeod, Butler & Syer, 1996).

[edit]The generality predicament: return to the specificity view

Ever since the introduction of the meta-knowledge theme in education science, transfer discussions have been oscillating between the position

taken by those representing the meta-cognitive view and those who stress that generic knowledge forms alone do not allow an effective transfer

of learning. When knowledge stays "on the tip of the tongue", just knowing that one knows a solution to a problem, without being able to transfer

specific declarative knowledge (i.e., know-what) or automated procedural knowledge (i.e., know-how), does not suffice. Specific teaching of the

cognitive and behavioral requisites for transfer marked in principle a return to the identical element view, and can be summarized with

Dettermann's (1993) conclusion that transfer does not substantially go beyond the restricted boundaries of what has been specifically taught and

learned. The basic transfer paradigms in educational psychology keep replicating themselves, and fundamental promotion of transfer itself is

seen to be achievable through sensibilization of students by creating a general culture and "a spirit of transfer" inside the classroom on the one

hand, and by allowing concrete learning from transfer models on the other (Haskell, 2001).

[edit]Learning and transfer: implications for educational practice

A modern view of transfer in the context of educational practice shows little need to distinguish between the general and specific paradigms,

recognizing the role of both identical elements and metacognition. In this view, the work of Bransford, Brown and Cocking (1999) identified four

key characteristics of learning as applied to transfer. They are:

1. The necessity of initial learning;

2. The importance of abstract and contextual knowledge;

3. The conception of learning as an active and dynamic process; and

4. The notion that all learning is transfer.

First, the necessity of initial learning for transfer specifies that mere exposure or memorization is not learning; there must be understanding.

Learning as understanding takes time, such that expertise with deep, organized knowledge improves transfer. Teaching that emphasizes how to

use knowledge or that improves motivation should enhance transfer.

Second, while knowledge anchored in context is important for initial learning, it is also inflexible without some level of abstraction that goes

beyond the context. Practices to improve transfer include having students specify connections across multiple contexts or having them develop

general solutions and strategies that would apply beyond a single-context case.

Third, learning should be considered an active and dynamic process, not a static product. Instead of one-shot tests that follow learning tasks,

students can improve transfer by engaging in assessments that extend beyond current abilities. Improving transfer in this way requires instructor

prompts to assist students – such as dynamic assessments – or student development of metacognitive skills without prompting.

Finally, the fourth characteristic defines all learning as transfer. New learning builds on previous learning, which implies that teachers can

facilitate transfer by activating what students know and by making their thinking visible. This includes addressing student misconceptions and

recognizing cultural behaviors that students bring to learning situations.

A student-learning centered view of transfer embodies these four characteristics. With this conception, teachers can help students transfer

learning not just between contexts in academics, but also to common home, work, or community environments.

[edit]Inter-language transfer

Another traditional field of applied research is inter-language transfer. Here, the central questions were: how does learning one language (L1)

facilitate or interfere (Weinreich, 1953) with the acquisition of and proficiency in a second language (L2), and how does the training and use of

L2, in turn, affect L1? Several variations of this conception of inter-language transfer can be found in the literature, also referred to as mother

tongue influence or cross language interference (Corder, 1983, 1994; Faerch & Kasper, 1987; Jiang & Kuehn, 2001; Odlin, 1989; O’Malley and

Chamot, 1990). What makes inter-language transfer a complex and valuable research matter is the fact that language knowledge skills

continuously develop. This is so for L1, as well as for L2, when only bilingualism is considered, while alternately at least one of them is

continuously in use. This has led to the development of very different models of how languages are mentally represented and managed, with L1

and L2 seen as two independent or autonomous mental systems (e.g. Genesee, 1989; Grosjean, 1989), as being represented in a single unified

system (e.g. Redlinger & Park, 1980; Swain, 1977), and as rooting in a common underlying, multi-lingual conceptual base (CUCB; see Kecskes

& Papp, 2000).

[edit]Human-Computer Interaction: designing for transfer

A third research area that has produced a variety of transfer models and empirical results can be located within the field of Human-Computer

Interaction (HCI). With the start of the user age in the 1980s, HCI and all kinds of virtual environments have, in many ways, become something

like psychological micro-worlds for cognitive research. This is naturally also reflected in the study of transfer.

Developments in favor of cognitive approaches to transfer research were especially accelerated by rapid changes in modern lifestyles, resulting

in a virtual upsurge of cognitive demands in interaction with technology. Thus, the call was on clearly domain-focused cognitive models to study

the way users learn and perform when interacting with information technological systems (Card, Moran & Newell, 1980a and b, 1983; Olson &

Olson, 1990; Payne & Green, 1986; Polson, 1987, 1988).

[edit]Transfer based on the user complexity theory

Thorough investigations of cognitive skills involved in HCI tasks have their origins with the research on text editing (e.g., Kieras & Polson, 1982,

1985; Singley & Anderson, 1985). The offspring of this type of research were computational cognitive models and architectures of various

degrees of sophistication, suitable for all kinds of man-machine interaction studies, as well as studies outside of the HCI domain. The original

examples for these have become Kieras and Polson's (1985) user complexity theory (later rephrased as cognitive complexity theory) and the

GOMS family (i.e., Goals, Operators, Methods, Selection) rules based on the Model Human Processor framework (Card et al., 1980a and b,

1983; John & Kieras, 1996a and b). All of these models have their roots in the basic principles of production systems and can be comprehended

with the help of ends-means-selections and If-Then-rules, combined with the necessary declarative and procedural knowledge (Anderson, 1995;

Newell & Simon, 1972).

The crucial perspective for transfer became that of technology design. By applying cognitive models, scientists and practitioners aimed at

minimizing the amount and complexity of new knowledge necessary to understand and perform tasks on a device, without trading off too much

utility value (Polson & Lewis, 1990). A key responsibility was given to skill and knowledge transfer. Due to the fact that the cognitive complexity

theory is a psychological theory of transfer applied to HCI (Bovair, Kieras, & Polson, 1990; Polson & Kieras, 1985), the central question was how

these models, united under the GOMS umbrella, can be used to explain and predict transfer of learning.

The basic transfer-relevant assumptions of the emerging models were that production rules are cognitive units, they are all equally difficult to

learn, and that learned rules can be transferred to a new task without any cost. Because learning time for any task is seen as a function of the

number of new rules that the user must learn, total learning time is directly reduced by inclusion of productions the user is already familiar with.

The basic message of the cognitive complexity theory is to conceptualize and induce transfer from one system to another by function of shared

production rules, which is a new interpretation of Thorndike's (1923, 1924a and b) identical element premise and eventually echoed in Singley

and Anderson's (1989) theory of transfer (Bovair et al., 1990; Kieras & Bovair, 1986; Polson & Kieras, 1985; Polson, Muncher & Engelbeck,

1986).

A practical implication of the procedural communality principle has been formulated by Lewis and Rieman (1993), who suggest something like

"transfer of design" on the side of the industry: "You should find existing interfaces that work for users and then build ideas from those interfaces

into your systems as much as practically and legally possible."

[edit]Emergence of holistic views of use

Discouraged by the confined character of the GOMS-related transfer models, many research groups began to import and advance new

concepts, such as schemata principles and general methods; a general development encouraged by the emerging cognitive approach to transfer

that was also witnessed by other applied fields. Bhavnani and John (2000) analyzed different computer applications and strived to identify such

user strategies (i.e., general methods to perform a certain task), which generalize across three distinct computer domains (word processor,

spreadsheet, and CAD). Their conclusive argument is that "strategy-conducive systems could facilitate the transfer of knowledge" (p. 338). Other

research groups' authors that assessed the questions about how people learn in interaction with information systems, evaluated the usefulness

of metaphors and how these should be taken into consideration when designing for exploratory environments (e.g. Baecker, Grudin, Buxton, &

Greenberg, 1995; Carroll & Mack, 1985, Condon, 1999).

As researchers became increasingly interested in the quality of a user's knowledge representation (e.g., Gott, Hall, Pokorny, Dibble, & Glaser,

1993), mental models and adaptive expertise, as knowledge and skills which generalizes across different contexts of complex problem-solving

tasks, became of paramount concern (Gentner & Stevens, 1983; Gott, 1989; Kieras & Bovair, 1984). In contrast to the knowledge of strategies

(Bhavnani & John, 2000), the accentuation shifted towards strategic knowledge (Gott et al., 1993). Gott et al. demonstrated that surface

similarities between different technical domains alone did not essentially facilitate transfer of learning because they limited the user's flexibility in

the adaptation process. In accordance with the ideas of schema-based and meta-cognitive transfer, the authors further formulated that "robust

performance is one in which procedural steps are not just naked, rule-based actions, but instead are supported by explanations that perform like

theories to enable adaptiveness" (p. 260).

Gott et al. (1993) finally noted that mental models might be powerful instruments to analyze similarities between tasks as represented within a

formulated cognitive architecture. However, they do not explain what particular similarities and differences are sufficiently salient from the

individual's mental point of view to affect transfer of learning, nor can they predict motivational or emotional conditions of transfer that are

essential requisites for every learning process.

[edit]Psychological scope of transfer research

As transfer pertains to the dependency of an individual's experience and behavior on prior experience and behavior, its research must involve all

aspects of psychological functioning, ranging from physical activities, cognitive processes (e.g., thinking), emotion and connation, to its social

and environmental dimensions. Although the cognitive connotation of skill has largely emerged as the dominant conception, is not truly possible

to appreciate the real meaning of skill without linking it to its motor or behavioral origins (Adams, 1987; Pear, 1927, 1948), and without extending

its scope to include socio-emotional dimensions.

[edit]Cognitive transfer

The greatest bulk of theoretical and empirical research published in recent decades has been done with reference to transfer of cognitive skills

and knowledge; for example with regard to problem-solving and analogical reasoning (Gentner & Gentner, 1983; Gick & Holyoak, 1980, 1983;

Holland, Holyoak, Nisbett, & Thagard, 1986; Robertson, 2001). The cognitive shift in psychology showed a great impact on the evolution of new

and refined concepts, methods, theories, and empirical data in transfer research, and it put the investigation of the phenomenon back on the

general research agenda after a clear decline in relevant scientific publications between 1960 and the 1980s (Cormier & Hagman, 1987; Haskell,

2001).

Cognition-oriented theories reinforced a series of key research frameworks to the study of transfer, including production systems, analogical

reasoning (Gentner & Gentner, 1983; Gick & Holyoak, 1980; Holland et al., 1986), mental models, schema, heuristics, and meta-cognition

(Brown, 1978; Flavell, 1976; Gentner & Stevens, 1983; Gott, 1989; Kieras & Bovair, 1984). Specifically, research on transfer has profited from

three main drivers within the study of human cognition: these are analogy, the computational metaphor, and the intensified interests with the

nature and quality of mental representations.

[edit]Metaphor and analogy

Metaphor refers to the use of a word or phrase to denote an object or concept not in a literary sense, but rather by suggesting an enhancement

or replacement of the understanding and interpretation of the targeted object with the metaphor. The object we are indicating by a metaphor is

holistically mapped onto the metaphor – and essentials of the metaphor's content are therefore transferred to the representation of the denoted

object. The term metaphor comes from the Greek word "metapherein", meaning "to transfer" (see Ortony, 1991, for a overview).

In contrast to metaphor, the concepts of similarity and analogy are actually less inherently linked to the mental nature of transfer because they

refer only to the circumstance of the relation between two representations. Here, object P is "seen" to be like Q (according to the Latin word

"similis", meaning "like") in certain aspects. By inferring that there might be other similar states between P and Q to be found, P can be used as

an analogy for Q. Transfer by analogy is not understood in the holistic way as is the case with metaphorical substitution of meaning, but rather in

a channeled fashion due to aspectual (perceived or inferred) resemblance between P and Q.

Nevertheless, research on analogy, in all its nuances, proved to be most influential to the conceptualization of cognitive transfer. Many cognitive

scientists, as well as road leading philosophers, consider analogy to be one if not the core principle of human thinking and thought (e.g., Forbus,

2001; Hesse, 1966; Hofstadter, 2001). According to these views transfer has to be placed within the framework of analogy, rather than the other

way around. Although research into analogy frequently penetrates traditional cognitive boundaries, for instance by involving emotionality and

social cognition (see Thagard et al., 2002), it is usually associated with analogical reasoning and problem solving, both of which are closely

related to the issue of transfer (Robertson, 2001).

[edit]Computational models

The nearly unifying cognitive metaphor is known as the information-processing approach (Eysenck, 2000; Kuhn, 1970; Lachman, Lachman &

Butterfield, 1979), and with the understanding of the learning individual inspired by the General Problem Solver (GPS; Newell, Shaw & Simon,

1958 and 1960; Newell & Simon, 1963, 1972). Cognitive research brought forth a variety of computational models and methods to study and

simulate knowledge acquisition, retention, and use (e.g. Anderson, 1983, 1985, 1993; Anzai & Simon, 1979; Atwood & Polson, 1976; Hayes &

Simon, 1974 and 1977; Simon & Hayes, 1976). This also provided a new framework for transfer theory development, particularly Singley and

Anderson's (1985, 1989) cognitive account of Thorndike's identical element theory. Emphasis is put on the classic knowledge form distinction

between declarative and procedural knowledge (Anderson, 1995) as well as between weak problem-solving methods (i.e., generalized, domain-

independent knowledge and skills) and strong problem-solving methods (i.e., domain specific knowledge and skills) (Anderson, 1987, Klahr,

1985; Larkin, 1985; Newell, 1980; Newell & Simon, 1972; Simon & Simon, 1978).

Anderson (1995) criticized preceding research on analogical transfer for its dominant focus on traits of the source and target in terms of

declarative knowledge, instead of performance orientated processing aspects. He points out for skill acquisition that declarative memory plays

only initially a significant role and is in the course of practice quickly replaced by procedural memory; encoded and strengthened in the form use

specific production rules (also called the effect of Einstellung; Luchins, 1942). The performance benefits from already compiled production rules

are believed to be automatic, errorless, independent of each other, and largely independent of contextual variations of tasks within the same

knowledge domain. The transfer distance between the performances in two tasks, or the solutions to two problems, is assumed to decrease

proportionally to the number of share specific procedures. This procedural "proportionality-relationship" (Allport, 1937) is in effect the most

straightforward interpretation of the Greek term of analogy, meaning proportion, and has in ideal cases of procedure-to-procedure transfer

settings, been shown to make relatively good predictions (see also Moran, 1983; Polson & Kieras, 1985; Singley & Anderson, 1985, 1989).

Anderson's assessment echoed the fact that research on human learning and problem-solving started to put increasing emphasis on issues like

cognitive skills and mental operators, which found implementations in a variety of cognitive architectures such as Soar (i.e., State, Operator, And

Result; Laird, Newell & Rosenbloom, 1987; Laird, Rosenbloom & Newell, 1984; Newell, 1990; Rieman et al., 1994), CE+ (Polson, Lewis,

Rieman, & Wharton, 1992; Wharton, Rieman, Lewis & Polson, 1994), and the development of several versions of Anderson's ACT theory

(Adaptive Control of Thought; e.g., ACT-R, see Anderson, 1982, 1983, 1993, 1996; Anderson & Lebiere, 1998).

In recent decades, cognitive scientists have developed numerous computational models of analogy such as the Structure Mapping Engine (SME)

and the "model of similarity-based retrieval" (MAC/FAC; Forbus, Ferguson, & Gentner, 1994; Gentner & Forbus, 1991), Analogical Coherence

Models (Holyoak & Thagard, 1989, 1995) Learning and Inference with Schemas and Analogies (LISA; Holyoak & Hummel, 2001) to name just a

few (see Gentner, Holyoak & Kokinov, 2001, for an overview). Within LISA's cognitive architecture, for instance, analogical mapping and retrieval

functions are based on the premise that structural units in long-term memory (i.e., propositions, sub-propositions, objects and predicates) of

source and target are represented by a collection of shared activated semantic units (Holyoak & Hummel, 2001; Hummel & Holyoak, 1997).

[edit]Motor transfer

Senso-motor skills are an essential ingredient in learning and performance in most tasks and can be categorized into continuous (e.g., tracking),

discrete, or procedural movements (see Magill, 2004; Schmidt & Wrisberg, 2004, for recent basic overviews). Proceduralized motor skills have

recently become the most referred to because they are consistent with the models of cognitive architectures and because they are seen as

relevant to nearly all physical interactions with the environment, as is the case in transfer situations.

[edit]Open-loop and closed-loop processes

Before the birth of the proceduralization concept, theories of motor learning have been influenced by the open-loop vs. closed loop system

distinction (Adams, 1971; Schmidt, 1975). The original formulation of the closed-loop view on motor performance and learning build on the

momentum of internal feedback from executed movements, which allow for error detection and adjustment of actions through the process of

contrasting perceptual traces against memory representations (Adams, 1971). Motor learning was accordingly seen as dependent on repetition,

accuracy, refinement, and synchronization of a series of called-up movement units (i.e., open-loop structures) that are regulated by closed-loop

structures.

In response to this view, a different open-loop perspective emerged, namely the one of motor programs (Schmidt, 1975). The learning of motor

skills was hereby seen in terms of the build-up, modification, and strengthening of schematic relations among movement parameters and

outcomes. This learning results in the construction "generalized motor programs" (i.e., a sequence or class of automated actions) that are

triggered by associative stimuli, habit strengths, and re-enforcers, and can be executed without delay (Anderson, 1995; Schmidt, 1975, 1988).

Both theories have their origin with Thorndike's "Law of Effect", because the formation of motor behavior is essentially dependent on knowledge

of the outcome of the action taken. This is regardless of whether the essence of motor skills is seen with specific movements or parameters in a

schematic motor program (Adams, 1971; Bartlett, 1947a and b, Schmidt, 1988).

Another classic theme that was revived in the literature on transfer of motor skill is the part-to-whole transfer of training (Adams, 1987, p. 51ff.;

Thorndike, 1924a and b). It emerged because it is nearly inconceivable to learn a highly complex motor task as a complete entity. Much like in

curriculum research, positive generalization of skill units into coherent task situations has been very limited. Particularly, it was found that initial

whole-task performances after part-task training remains seriously impaired due to difficulties in the time-sharing of the activities. Whole task

training remains generally superior to the part-task-whole-task transfer approach of learning (Adams, 1987; Adams & Hufford, 1962; Briggs &

Brodgen, 1954).

Finally, motor research provided some evidence for context- and task-independent savings in learning effort on a new task that seems to be

explainable by heightened plasticity and functional reorganisation in the senso-motor neural network system. This is naturally in line with the

formal discipline argument.

[edit]Socio-emotional dimensions of transfer

Motor and cognitive transfer are, in many respects, inseparable from issues of emotion and motivation, just as cognitive research in general must

embrace affective dimensions of experience and behavior (Barnes & Thagard, 1996; Thagard & Shelley, 2001). This basic awareness has a long

tradition in psychology and in the philosophical works of Aristoteles, Descartes, and Hume, but has to date not been sufficiently regarded in

cognitive research (Damasio, 1994; Leventhal & Scherer, 1987; Mandler, 1975; Oatley & Johnson-Laird, 1987; Rapaport, 1950; Scherer, 1995).

Naturally, emotions and especially motivation have always been closely linked to learning in educational psychology, but their role was generally

conceptualized as more of an assistant or moderating nature, i.e., in facilitating vs. hindering cognition (Bruner, 1960; Gudjons, 1999; Pea, 1987,

1988; Pintrich, Marx, & Boyle, 1993; Salomon & Perkins, 1989; Thorndike, 1932). Approaches that focus on the same kind of relation between

affect and transfer belong to the group that study main effects of affective beliefs on cognition in general, and in particular on transfer-relevant

moderation and mediation effects of "will" on "skill" (see also Bong, 2002; Gist, Stevens, & Bavetta, 1991; Mathieu, Martineau, & Tannenbaum,

1993; Saks, 1995). In short: "Knowing how to solve problems and believing that you know how to solve problems are often dissonant" (Jonassen,

2000, p. 14).

In a review of research on motivation and transfer, Pugh and Bergin (2006) concluded that motivational factors can influence transfer, although

the research is limited and not wholly consistent. They found that mastery goals were more consistently linked to transfer success than

were performance goals. They also found that interest was related to transfer success when this interest was associated with the learning

content. However, when the interest was related to peripheral things, such as seductive details in text, it inhibited transfer success. In addition,

they found evidence that transfer success was positively related to self-efficacy. Finally, the reviewers proposed that the transfer process is

affected by the presence of an explicit goal of achieving transfer. Pugh and Bergin (2006) predicted that motivational factors influence transfer in

three ways. First, they can influence the quality of initial learning in ways that support transfer. Second, they can influence the initiation of transfer

attempts, particularly in situations where individuals have an opportunity to apply learning but are not required to. Third, motivational factors can

influence individuals’ persistence when engaged in transfer tasks.

[edit]Transfer of emotions

Emotional transfer must, however, be regarded as a distinct aspect or type of transfer itself, i.e., one where the experiential relation between two

situations is of affective nature (e.g., affective connotations and skills). It occurs wherever previously experienced feelings and attitudes toward a

situation, object, or task are re-evoked in a current confrontation with related "symbols" (see Hobson & Patrick, 1995). The preferred emotional

transfer model to date has been the one of analogical inference, e.g., if you like product X, and product Y is similar to X, then you will probably

like Y. Thagard and Shelley (2001) criticized the simplicity of analogical inference based on mere comparison of objects and properties and

proposed a more complex model that accounts for structures of analogies, e.g., by including relations and causality structures. Their emotional

coherence theory implemented this idea in the form of the HOTCO model (standing for "hot coherence") by drawing on assumptions made in

preceding models, including explanatory coherence (ECHO), conceptual coherence (IMP), analogical coherence (ACME), and deliberative

coherence (DECO) (see Thagard, 2000).

[edit]Conceptual foundation of transfer research

The cognitive shift in psychology encouraged the research of mental forms and processes engaged in learning and transfer rather than the

simple modification of overt reproductional behavior; a change in viewpoint that the early Gestalt psychologists and constructivists such as

Köhler, Wertheimer, or Piaget had already propagated for a couple of decades. The investigation of cognitive dimensions in transfer quickly

became the major driver of research across applied domains and cognitive transfer emerged in many ways as the quintessential view of transfer

in general.

[edit]Mental representations and transfer: Common element-based vs. schema-based approaches

The majority of mental processes studied in research on human cognition have one thing in common: they all pertain in one way or another to

the construction of mental representations. This is true, for instance, for perceiving, learning, problem-solving, reasoning and thinking, and

recalling, as much as it is true for the phenomenon of transfer.

Although research on mental representation has been utterly manifold, two main traditions can be discerned. Some researchers have regarded

mental representations in terms of abstract schemata, frames, patterns or mental models (Bartlett, 1932; Chase & Simon, 1973; Gentner &

Stevens, 1984; Johnson-Laird, 1983; Johnson-Laird & Byrne, 1990; Minsky, 1975), while others have paid attention to semantic information and

propositional nature of mental representations (Anderson, 1976, 1983, 1994; Collins & Quillian, 1968; Medin & Ribs, 2005; Medin & Smith, 1984;

Minsky, 1968; Rosch, 1978). These differential conceptualizations have, in general, been driven by distinct psychological paradigms adopted,

such as Associationism and Connectionism, Behaviorism, Gestaltism, andCognitivism.

GOMS and ACT-based procedural transfer theses are a good example of modern explanations fitting the atomistic and mechanistic nature of the

Connectionist paradigm, i.e., by seeing transfer as an effect of commonality in semantic conditions-action-goal structures, mainly instantiated as

If-Then production rule associations overlap. This view on transfer clearly replaced Behaviorist explanatory concepts of stimuli and response with

more sophisticated mental concepts that serve as units of transfer. The cognitive architecture background also added important processing

capabilities and some degree of flexibility concerning the identicality constraint (e.g., declarative-to-procedural, and declarative-to-declarative

transfer). It did not, however, essentially defy the common underlying common element-based thought model of transfer.

Both the original habitual response-based idea of common element transfer as well as the modern production rule compilation and knowledge

encapsulation account are in their core assumptions refuted by Gestaltists' theories. Koffka's (1925) scrutiny of Thorndike's (1911, 1913) and

Köhler's (1917) arguments and findings revealed that explanations of learning and transfer based on the notions of association and automation

fall short of explicating the nature of mental activity even for simple problem-solving tasks. Novel explanatory concepts were needed to account

for "learning by understanding" (Katona, 1940) and problem-solving transfer (Mayer & Wittrock, 1996). These were found with reference to the

organization and structure of knowledge (Clement & Gentner, 1991; Gentner & Gentner, 1983; Gentner & Toupin, 1986), abstraction and general

principle inferences (Bourne, Ekstrand, & Dominowski, 1971, p. 104ff.; Judd, 1908, 1939; Simon & Hayes, 1976), the goal- and meaning-

directedness of thinking and its holistic nature (Bühler, 1907, 1908a; Holyoak, 1985; Humphrey, 1924; Selz, 1913, 1922), and functional relations

(Duncker, 1935; Köhler, 1917). Because this tradition of investigating transfer is based on Gestaltist ideas, they could be summarized under the

header of schema-based theories of transfer.

In accord with the traditions regarding research on mental representation, two mainstream explanatory models for transfer can be concluded to

date. One is the model of common element-basedtransfer, rooting in Thorndikean ideas, which explains transfer as confined to elementary

correspondences between a primary and a secondary learning situation, such as procedures and their automated effect (e.g., Allport, 1937;

Singley & Anderson, 1985, 1989; Thorndike, 1924a, b). The other model emerging from the Gestalt tradition can be labeled schema-based or

analogical transfer, emphasizing elementary loosened structural or principle/rule-based coherence between transfer source and target (e.g.,

Duncker, 1935; Gentner, 1983; Gentner & Gentner, 1983; Gick & Holyoak, 1980, 1983; Köhler, 1917/1957; Reed, 1993). They continued Judd's

(1908) line of work, resulting in further accentuation of "insightful" transfer, using terms like knowledge structures and schemata, solution

principles, and functionality (Katona, 1940; Wertheimer, 1945/1959).

The problem is that as far as transfer of learning in both traditions refers to one and the same phenomenon, there can not be a situation with two

incompatible theoretical frameworks standing side-by-side. Conceptual resolution in some form is clearly imperative. Several efforts have been

made in recent years to review and revive transfer research, and to resolve controversies (cf. the content- and apperception-based approach:

Helfenstein, 2005[2]), but empirical justification is still in early stages.

[edit]The similarity predicament

The notion of similarity has been particularly problematic for transfer research for a number of reasons. The main problem is

that similarity implies dissimilarity, i.e., although two instances may in parts be identical, they are after all also different.

First, similarity has been the cause for debate about how to distinguish transfer of learning from learning or problem-solving. The distinction of

transfer of learning from learning is usually done with reference to a cut-off point on the similarity dimensions, by which the relation between a

current and a past situation is estimated. The more similar two situations are rated, the more probable it becomes that any witnessed

improvement in performance is due to learning rather than to transfer. The same logic is true in the other direction of the transfer-learning

dimension. The discussion on the dissimilarity-similarity distinction has the ambivalent character of being conducted in reference to a

dimensional or polar conception and dichotomous model interchangeably. Learning is usually implicitly awarded its own place at the periphery of

transfer taxonomies that are based on near-far distinctions, and this raises the question whether it would not be sounder to concentrate more

intensively on the common cognitive bases of learning and transfer than on some conceptual distinction between them.

For instance, while Butterfield and Nelson's (1991) categorization is intuitively appealing, it also conveys some typical problems and challenges.

For instance, if transfer is to a task or situation which is so similar to a previously experienced one that it actually can be considered as the same

task (i.e., within-task transfer), then how do we distinguish transfer from learning in general? The corresponding deliberation is that learning

refers to mental processes involved in the course of a repeated confrontation with a certain type of task or situation, of which the single accounts

can never be identical. Butterfield and Nelson have themselves not been blind to this argument, but they still refrain from equating learning and

transfer as proposed by Salomon and Perkins (1989, p. 115). Across-task transfer, according to Butterfield and Nelson's (1991) model, refers to

the application of a learned principle in a new task situation which is superficially different, yet functionally equivalent to the prior one. Inventive

transfer, finally, is used to describe incidences where learners cannot make use of the same solution principles previously learned, but have to

develop a new solution on the grounds of similarities and critical differences of source and target task. Understandably, Butterfield and Nelson

pose the question to whether this should be rather characterized as problem-solving than transfer.

Second, transfer theories are built on the premise of identical constituents between transfer source and target, while differences are usually seen

as cause of transfer failure. In spite of the manifold attempts to dissociate from one-to-one similarity concepts, the identicality constraint

continued to produce most of the headaches to cognitive scientists, especially in the area of analogy research. Considering the diversity of

transfer conditions, application domains, and contextual dependency of analogical thought, it is not surprising that few psychologists have

conclusively put their fingers on what seems as the essence of analogical relations. While the talk of "sameness" and "transpositional similarity"

appeals to common sense, much about what similarity means precisely, how it is established mentally, and, therefore, what justifies analogical

reasoning remains unclear.

Overall, similarity constraint factors have been identified with respect to predicates, objects and propositions, relational and structural

isomorphism, procedural matches, in relation to purpose or goals of tasks or episodes under analogical consideration (see e.g., Robertson,

2001), as well as in relation to the level or type of mental engagement (see results from research on Transfer-appropriate processing (TAP); e.g.,

Cermak & Craik, 1979; Francis, Jameson, Augustini, & Chavez, 2000; Jacoby, 1983; Roediger & Blaxton, 1987; Schacter, Cooper, Delaney,

Peterson & Tharan, 1991; Vriezen, Moscovitch, & Bellos, 1995). As noted, analogical transfer and analogical memory recall has been

demonstrated with respect to similarity in superficial traits rather than in respect to relational analogy or structural correspondence (e.g., Kaiser,

Jonides, & Alexander, 1986), and has been best attained in within-domain and near-transfer settings. In spite of the claim that similarity between

analogs fundamentally refers to the qualitative "alikeness" in the relations that hold within one common structure of mental objects, and not

simply to the quantitative surface similarity of properties or features from which analogy is then inferred (Forbus, 2001; Gentner, 1982, 1983).

Nevertheless, if transfer by analogy is not to stumble over the boundaries of identical matches – be these superficial attributes between target

and retrieved source, elements of declarative knowledge, procedural memory content, relational aspects, or otherwise – then the question what

similarity means in the context of dissimilarity should be resolved. The focus should be on explicating the sameness in mental representations

and assessing their impact on transfer, and not so much on the question "how similar is similar enough to be considered as an analog?"Transfer of Training — "That almost magical link between classroom performance and something which is supposed to happen in the real world" — J. M. Swinney. Transfer of training is effectively and continuing applying the skills, knowledge, and/or attitudes that were learned in a learning environment to the job environment.Transfer of Learning is the application of skills, knowledge, and/or attitudes that were learned in one situation to another learning situation. This increases the speed of learning.In a backhoe course where I once taught, we had about twenty machines consisting of three different models. One model was an old Massy-Ferguson. Its controls consisted of about eight levers that only moved back and forth. The newest model was a John Deere. It had two joystick-type controls (they moved similar to a computer joystick) and two foot-pedals. The other model was a Case that was a cross between the other two.The learners took turns operating the various models. Although a casual observer unfamiliar with transfer of learning might assume we were confusing the issue with three highly different models, the different models were not only conductive to the learning environment in that they provided transfer of learning (hence quicker and deeper learning), but they also provided the learners with the confidence and skills for transferring their newly acquired skills to the job.

The first place to practice transfer of learning is within the classroom. This makes it much easier to transfer new skills and knowledge to the job. Transfer of learning is the influence of prior learning on performance in a new situation. If we did not transfer some of our skills and knowledge from prior learning, then each new learning situation would start from scratch.Some learning professionals only think of transfer of learning (or transfer of training) in terms of "the classroom to the job environment." However, these trainers fail to realize the importance of "task variation" within the classroom. That is, practicing on a variety of tasks will enhance and quicken the learning process as compared to practicing on the same category or class. Also, the learners become accustomed to using their newly acquired knowledge and skills in novel situations, thus encouraging transfer of learning to the job.Transfer of learning is a phenomenon of learning more quickly and developing a deeper understanding of the task if we bring some knowledge or skills from previous learning. Therefore, to produce positive transfer of learning, we need to practice under a variety of conditions.Note that there is a brief slow down in the learning curve (confusion occurs) when the variation is first introduced. However, the variation soon begins to strengthen our previously acquired skills and knowledge. This is perhaps why some trainers are reluctant to use task variation — they see the initial confusion and assume they are slowing and confusing the learning process. Hence, classrooms become sterile of transfer of learning. And since the learners have no practice in transferring their newly acquired skills and knowledge in the classroom, they have trouble transferring their learning when they return to the job as most work environments are neutral towards the transfer of new skills (that is, they do very little to encourage the transfer of learning). Do NOT fall into this trap. Variation is GOOD! Provide as many variations and conditions in the learning environment as possible. There are two main principles that work with transfer of learning:

o The variation should not be too easy.o The shift or transfer should be progressive but rapid.

For example, introductory computer classes often follow a course similar to this:o — One day of introductiono — One day of word processingo — One day of spreadsheeto — One day of database training

Why not combine the 4 days into integrated classes that goes similar to this:o Starting the three applicationso Typing text into the three applicationso Copying and pasting in the same application and then to a different applicationo Saving, etc.

The above are closely related tasks that would enhance the power of transfer of learning.We benefit (or suffer) from our prior experiences. People improve in their ability to learn new skills more proficiently because of prior practice on a series of related tasks. This helps us to acquire new views on a topic by looking at the task from a different approach, which strengthens our understanding of the topic. For example, practicing to drive a variety of cars provides experience with different stimulus situations and makes new learning easier. Another example is that greater learning occurs not by rereading the same text, but by reading another text on the same subject matter.Transfer of learning begins with the learning of a task in a unique situation and ends when we quit learning (experimenting) with that task. The power of varied context, examples, different practice scenarios, etc. cannot be overemphasized. No matter if you are learning simple discriminations or complex concepts, stimulus variations are helpful. Encouraging transfer of learning in the classroom provides the skills and knowledge for its successful implementation outside of the class.

T h e o r y o f F o r m a l D i s c i p l i n eIt was once thought that taking courses such as Latin would lead a person to think more logically. This assumption is called the " Theory of Formal Discipline." Thorndike (1924) studied the it concluded that the expectation of any large difference in general improvement of the mind from one study to another was false.The main reason why it looks as if good thinkers have been helped by taking certain school studies is that the there is an inherent tendency of the good thinkers to take such courses. When the good thinkers studied Greek and Latin, these studies seemed to make good thinking.Thorndike continued his study of transfer, and eventually formulated the "Theory of Identical Elements" previous learning facilitates new learning only to the extent that the new learning task contains elements identical to those in the previous task.

N e a r t r a n s f e rNear transfer of skills and knowledge are applied the same way every time the skills and knowledge are used. Near transfer training usually involves tasks that are procedural in nature, that is, tasks which are always applied in the same order. Although this type of training is easier to train and the transfer of learning is usually a success, the learner is unlikely to be able to adapt their skills and knowledge to changes.

F a r t r a n s f e rFar transfer tasks involve skills and knowledge being applied in situations that change. Far transfer tasks require instruction where learners are trained to adapt guidelines to changing situations or environments. Although this type of training is more difficult to instruct (transfer of learning is less likely), it does allow the learner to adapt to new situations.

Transfer of learning, a basic principle of learning theory in educational psychology, involves the ability to transfer understanding from one situation to a another situation, whether similar or different in nature.DefinitionThe International Encyclopedia of Education defines transfer of learning as the application of knowledge or skills gained in one context to a problem in another context.ExampleTying shoelaces represents a simple example of transfer of learning, according to the Oregon Technology in Education Council. Once people know how to tie one type of shoelaces, they can easily apply that knowledge to other types of shoelaces.TypesThe two basic types of transfer include near transfer and far transfer. Near transfer involves two closely related concepts or tasks, while far transfer involves concepts or tasks that bear little obvious relation to each other.

MechanismsTwo mechanism for transfer of learning include "low road transfer," in which the person makes a conceptual connection instinctively or reflexively, and "high road transfer," which requires a conscious effort to connect abstract concepts.ApplicationsTransfer of learning helps people learn additional languages, move from one job to another or use one discipline in the context of another, such as applying math skills to scientific work.Source:Wide World (Harvard Graduate School of Education): Transfer of LearningOregon Technology in Education Council: Learning Theories and Transfer of Learning

Read more: What is the transfer of learning in psychology? | Answerbag http://www.answerbag.com/q_view/2091629#ixzz1SdymmOCi

Connectionism (E. Thorndike)Overview:The learning theory of Thorndike represents the original S-R framework of behavioral psychology: Learning is the result of associations forming between stimuli and responses. Such associations or "habits" become strengthened or weakened by the nature and frequency of the S-R pairings. The paradigm for S-R theory was trial and error learning in which certain responses come to dominate others due to rewards. The hallmark of connectionism (like all behavioral theory) was that learning could be adequately explained without refering to any unobservable internal states.Thorndike's theory consists of three primary laws: (1) law of effect - responses to a situation which are followed by a rewarding state of affairs will be strengthened and become habitual responses to that situation, (2) law of readiness - a series of responses can be chained together to satisfy some goal which will result in annoyance if blocked, and (3) law of exercise - connections become strengthened with practice and weakened when practice is discontinued. A corollary of the law of effect was that responses that reduce the likelihood of achieving a rewarding state (i.e., punishments, failures) will decrease in strength.The theory suggests that transfer of learning depends upon the presence of identical elements in the original and new learning situations; i.e., transfer is always specific, never general. In later versions of the theory, the concept of "belongingness" was introduced; connections are more readily established if the person perceives that stimuli or responses go together (c.f. Gestalt principles). Another concept introduced was "polarity" which specifies that connections occur more easily in the direction in which they were originally formed than the opposite. Thorndike also introduced the "spread of effect" idea, i.e., rewards affect not only the connection that produced them but temporally adjacent connections as well.Scope/Application:Connectionism was meant to be a general theory of learning for animals and humans. Thorndike was especially interested in the application of his theory to education including mathematics (Thorndike, 1922), spelling and reading (Thorndike, 1921), measurement of intelligence (Thorndike et al., 1927) and adult learning (Thorndike at al., 1928).Example:The classic example of Thorndike's S-R theory was a cat learning to escape from a "puzzle box" by pressing a lever inside the box. After much trial and error behavior, the cat learns to associate pressing the lever (S) with opening the door (R). This S-R connection is established because it results in a satisfying state of affairs (escape from the box). The law of exercise specifies that the connection was established because the S-R pairing occurred many times (the law of effect) and was rewarded (law of effect) as well as forming a single sequence (law of readiness).Principles:1. Learning requires both practice and rewards (laws of effect /exercise)2. A series of S-R connections can be chained together if they belong to the same action sequence (law of readiness).3. Transfer of learning occurs because of previously encountered situations.4. Intelligence is a function of the number of connections learned.

Transfer of LearningTransfer of learning occurs when learning in one context or with one set of materials impacts on performance in another context or with other related materials. For example, learning to drive a car helps a person later to learn more quickly to drive a truck, learning mathematics prepares students to study physics, learning to get along with one's siblings may prepare one for getting along better with others, and experience playing chess might even make one a better strategic thinker in politics or business. Transfer is a key concept in education and learning theory because most formal education aspires to transfer. Usually the context of learning (classrooms, exercise books, tests, simple streamlined tasks) differs markedly from the ultimate contexts of application (in the home, on the job, within complex tasks). Consequently, the ends of education are not achieved unless transfer occurs. Transfer is all the more important in that it cannot be taken for granted. Abundant evidence shows that very often the hoped-for transfer from learning experiences does not occur. Thus, the prospects and conditions of transfer are crucial educational issues.1. Transfer DefinedTransfer versus ordinary learning. In a sense, any learning requires a modicum of transfer. To say that learning has occurred means that the person can display that learning later. Even if the later situation is very similar, there will be some contrasts ─ perhaps time of day or the physical setting. So no absolute line can be drawn between ordinary learning and transfer.However, transfer only becomes interesting as a psychological and educational phenomenon in situations where the transfer would not be thought of as ordinary learning. For example, a student may show certain grammar skills on the English test (ordinary learning) but not in everyday speech (the hoped-for transfer). The student may solve the problems at the end of the chapter (ordinary learning) but not similar problems when they occur mixed with others at the end of the course (the hoped-for transfer). In other words, talk of transfer is always at least implicitly contrastive: it assumes learning within a certain context and asks about impact beyond that context.Positive versus negative transfer. Positive transfer occurs when learning in one context improves performance in some other context. For instance, speakers of one language find it easier to learn related than unrelated second languages. Negative transfer occurs when learning in one context impacts negatively on performance in another. For example, despite the generally positive transfer among related languages, contrasts of pronunciation, vocabulary, and syntax generate stumbling blocks. Learners commonly assimilate a new language's phonetics to crude approximations in their native tongue and use word orders carried over from their native tongue.While negative transfer is a real and often problematic phenomenon of learning, it is of much less concern to education than positive transfer. Negative transfer typically causes trouble only in the early stages of learning a new domain. With experience, learners correct for the effects of negative transfer. From the standpoint of education in general, the primary concern is that desired positive transfers occur. Accordingly, the rest of this article focuses on positive transfer.Near versus far transfer. Near transfer refers to transfer between very similar contexts, as for instance when students taking an exam face a mix of problems of the same kinds that they have practiced separately in their homework, or when a garage mechanic repairs an engine in a new

model of car, but with a design much the same as in prior models. Far transfer refers to transfer between contexts that, on appearance, seem remote and alien to one another. For instance, a chess player might apply basic strategic principles such as ``take control of the center'' to investment practices, politics, or military campaigns. It should be noted that ``near'' and ``far'' are intuitive notions that resist precise codification. They are useful in broadly characterizing some aspects of transfer but do not imply any strictly defined metric of ``closeness.''2. Prospects of TransferAs noted earlier, transfer is especially important to learning theory and educational practice because very often the kinds of transfer hoped for do not occur. The classic investigation of this was conducted by the renowned educational psychologist E. L. Thorndike in the first decades of the 20th century. Thorndike examined the proposition that studies of Latin disciplined the mind, preparing people for better performance in other subject matters. Comparing the performance in other academic subjects of students who had taken Latin with those who had not, Thorndike (1923) found no advantage of Latin studies whatsoever. In other experiments, Thorndike and Woodworth (1901) sought, and generally failed to find, positive impact of one sort of learning on another. Thorndike concluded that transfer depended on ``identical elements'' in two performances and that most performances were simply too different from one another for much transfer to be expected. In terms of the rough near-far distinction, near transfer is much more likely than far transfer.Thorndike's early and troubling findings have reemerged again and again in other investigations. For instance, the advent of computer programming as a school subject matter stimulated the proposal that computer programming developed general problem solving skills, much as Latin was thought to cultivate mental discipline. Unfortunately, several experiments seeking a positive impact of learning to program on problem solving and other aspects of thinking yielded negative results (see Pea and Kurland 1984, Salomon and Perkins 1987).Another learning experience that might impact broadly on cognition is literacy, the mastery of reading and writing. Wide-ranging transfer might be expected from experience with the cognitive demands of reading and writing and the cognitive structures that text carries. However, Scribner and Cole (1981) reported a study of an African tribe, the Vai, with an indigenous form of writing not accompanied by schooling. Using a variety of general cognitive instruments, they found no differences between Vai who had mastered this script and others who had not. They argued that the impact of literacy depends on immersion in diverse activities surrounding literacy, not on acquisition of reading and writing per se. The Vai only employed their script in a very specific way, in contrast with the many uses of literacy apparent in many cultures.For still another example, researchers have looked for transfer effects between puzzles or games that are isomorphs of one another, sharing the same logical structure but presented or described in very different physical terms. For example, some research has focussed on the well-known Tower of Hanoi puzzle, that requires moving three (or more) rings of different sizes among three pegs according to certain rules. One isomorph involves a story about three extra-terrestrial monsters, each holding a crystal globe of a different size. The rules for the monsters passing the globes to one another are logically equivalent to the rules for moving the disks from peg to peg.It is not clear whether one should consider study of problem isomorphs near or far transfer, because isomorphs are near identical structurally but very different in external trappings. In any case, subjects usually do not recognize the connection between one isomorph and the other and hence do not carry over strategies they have acquired while working with one to the other. However, if the relationship is pointed out, then subjects can do so fruitfully (Simon and Hayes 1977).While the preponderance of results concerning transfer appears to be negative, it is important to recognize that occasional positive findings have appeared. For instance, Clements and Gullo (1984) and Lehrer et al. (1988) achieved positive transfer from engagement in Logo computer programming to certain cognitive measures, including measures of divergent thinking. Ann Brown (1989) reported a series of studies showing positive transfer by preschool children of abstract concepts, for instance the idea of stacking objects to climb on to reach something, or the idea of mimicry as a defence mechanism in animals. Campione et al. (1991) report that when children are taught to self-monitor and self-direct themselves during reading in what has been called ``reciprocal teaching,'' this transfers also to learning in other text-mediated areas of learning such as social studies and mathematics. Salomon et al. (1989) showed that students can transfer from a computer program designed to make students more strategic readers to their performance a while later on writing, suggesting that what the students acquired was transferable tendencies to self-monitor and self-direct.3. Transfer and Local KnowledgeAs emphasized earlier, near transfer seems to have much better prospects than far transfer. Not only does this trend appear in the empirical findings, but it makes sense in terms of contemporary research on ``expertise.'' Since the 1970's, a number of investigators have built a case for the importance of ``local knowledge'' (with knowledge taken in a broad sense to include skills, concepts, propositions, etc.). In areas as diverse as chess play, physics problem solving, and medical diagnosis, expert performance has been shown to depend on a large knowledge base of rather specialized knowledge (see Ericsson and Smith 1991). General cross-domain principles, it has been argued, play a rather weak role. In the same spirit, some investigators have urged that learning is highly situated, that is, finely adapted to its context (Brown et al. 1989, Lave 1988).A strong local knowledge position would predict little far transfer under any conditions, because knowledge in one context would not be very relevant to others. However, the research on expertise does not really force such a position: The importance of local knowledge does not imply the unimportance of rather general knowledge that works together with local knowledge (Perkins and Salomon 1989). Moreover, the idea of situated learning does not necessarily imply that the prospects of transfer are limited. Greeno et al. (in press) offer a situated learning view of transfer in which transfer depends on similar opportunities for action across situations that may be very different superficially. In sum, a monolithic local knowledge position is difficult to sustain.4. Conditions of TransferPositive findings of transfer, near and far, suggest that whether transfer occurs is too bald a question. It can, but often does not. One needs to ask under what conditions transfer appears.Thorough and diverse practice. Consider again the question of literacy. In a classic study of the impact of literacy and education in Russia, Luria (1976) found major influence on a number of cognitive measures. His results concerned a population where reading and writing played multiple roles. The contrast between Luria's and Scribner and Cole's findings suggests that transfer may depend on extensive practice of the performance in question in a variety of context. This yields a flexible relatively automatized bundle of skills easily evoked in new situations.Explicit abstraction. Transfer sometimes depends on whether learners have abstracted critical attributes of a situation. In one demonstration, Gick and Holyoak (1980, 1983) presented subjects with a problem story that allowed a particular solution. From subjects that solved the problem, they elicited what the subjects took to be the underlying principle. Then they presented the subjects with another analogous problem that invited a similar approach. Those subjects with the fullest and soundest summary of the principle for the first puzzle were most successful with the second. These and other results suggest that explicit abstractions of principles from a situation foster transfer.Active self-monitoring. Relatedly, metacognitive reflection on one's thinking processes appears to promote transfer of skills. This contrasts with the explicit abstraction category above in that abstraction focuses on the structure of the situation whereas self-monitoring focuses on one's own thinking processes. Belmont et al. (1982) undertook a synthesis of a number of efforts to teach retarded children simple memory strategies and to test whether the children would apply these in slightly different contexts. Many of these studies showed no transfer, while a few revealed some. The researchers isolated the factor that appeared to account for success: teaching the children not just to apply the strategy but to monitor their own thinking processes in simple ways. Presumably, this activation of self-monitoring helped the children later to recognize when they might apply the strategy they had learned.Arousing mindfulness. Mindfulness refers to a generalized state of alertness to the activities one is engaged in and to one's surroundings, in contrast with a passive reactive mode in which cognitions, behaviors, and other responses unfold automatically and mindlessly (Langer 1989). More encompassing than explicit abstraction and active self-monitoring, mindfulness would foster both of those.

Using a metaphor or analogy. Transfer is facilitated when new material is studied in light of previously learned material that serves as an analogy or metaphor. Things known about the ``old'' domain of knowledge can now be transferred to a ``new'' domain thereby making it better understood and learned. For example, students may initially understand the idea of an atom better by thinking of it as a small solar system, or how the heart works by thinking of it as a pump. Of course, most such analogies are limited and need elaboration and qualification.5. Mechanisms of TransferWhy do factors of the kind identified above encourage transfer? Answers to that question can best come from an examination of the mechanisms of transfer, the psychological paths by which transfer occurs.Abstraction. It is still possible today to grant Thorndike's point that identical elements underlie the phenomenon of transfer. However, research suggests that a more complex picture of how identical elements figure in the process of transfer. An identity that mediates transfer can sit at a very high level of abstraction. Phenomena such as the branching of arteries and that of electrical power networks can evince the same deep principle (the need to deliver something to a region point by point) with great differences in what constitutes a conduit (arteries versus wires) and the something being carried (blood versus electricity). Such a degree of abstraction helps to account for far transfer, because highly abstract identical elements can appear in very different contexts.Transfer by affordances. Writing from the perspective of situated cognition, Greeno et al. (in press) argue that transfer need not depend on mental representations that apply to the learning and target situations. Rather, during initial learning, the learner may acquire an action schema responsive to the affordances ─ the action opportunities ─ of the learning situation. If the potential transfer situation presents similar affordances and the person recognizes them, the person may apply the same or a somewhat adapted action schema there. External or internal representations may or may not figure in the initial learning or the resulting action schema.High road and low road transfer. Salomon and Perkins (1989, Perkins and Salomon 1987) synthesized findings concerned with transfer by recognizing two distinct but related mechanisms, the ``low road'' and the ``high road.'' Low road transfer happens when stimulus conditions in the transfer context are sufficiently similar to those in a prior context of learning to trigger well-developed semi-automatic responses. In keeping with the view of Greeno et al. (in press), these responses need not be mediated by external or mental representations. A relatively reflexive process, low road transfer figures most often in near transfer. For example, when a person moving a household rents a small truck for the first time, the person finds that the familiar steering wheel, shift, and other features evoke useful car-driving responses. Driving the truck is almost automatic, although in small ways a different task.High road transfer, in contrast, depends on mindful abstraction from the context of learning or application and a deliberate search for connections: What is the general pattern? What is needed? What principles might apply? What is known that might help? Such transfer is not in general reflexive. It demands time for exploration and the investment of mental effort. It can easily accomplish far transfer, bridging between contexts as remote as arteries and electrical networks or strategies of chess play and politics. For instance, a person new to politics but familiar with chess might carry over the chess principle of control of the center, pondering what it would mean to control the political center.In a particular episode of transfer, the two roads can work together ─ some connections can occur reflexively while others are sought out. But in principle the two mechanisms are distinct.This framework matches well a number of the points made earlier. It acknowledges that sometimes transfer is stimulus driven, occurring more or less automatically as a function of much and diverse practice (the low road). On the other hand, sometimes transfer involves high levels of abstraction and challenges of initial detection of possible connections (the high road). The framework makes room for identical elements in Thorndike's original sense ─ identities that the organism simply responds to (the low road) ─ but insists on the importance of identities discovered and exploited by mindful exploration (the high road).This analysis along with the views and findings of Luria, Scribner and Cole, Greeno, and others emphasizes that the conditions for transfer are stringent. Reflexive (low road) transfer requires well-automatized patterns of response that are thus easily triggered by similar stimulus conditions ─ and it requires stimulus conditions enough like prior contexts of learning to act as triggers. Many learning situations offer practice only for a narrow range of examples and not enough practice to achieve significant automaticity, providing a poor basis for reflexive transfer. Mindful (high road) transfer requires active abstraction and exploration of possible connections. Many learning situations do not encourage such mental investments, although people more inclined to mindfulness or metacognition are by definition more likely to make them.6. Teaching for TransferThese points about mechanism clarify why transfer does not occur as often as would be wished. They also provide guidelines for establishing conditions of learning that encourage transfer.In many situations, transfer will indeed take care of itself ─ situations where the conditions of reflexive transfer are met more or less automatically. For example, instruction in reading normally involves extensive practice with diverse materials to the point of considerable automaticity. Moreover, when students face occasions of reading outside of school ─ newspapers, books, assembly directions, and so on ─ the printed page provides a blatant stimulus to evoke reading skills.In contrast, in many other contexts of learning, the conditions for transfer are less propitious. For example, social studies are normally taught with the expectation that history will provide a lens through which to see contemporary events. Yet the instruction all too commonly does not include any actual practice in looking at current events with a historical perspective. Nor are learners encouraged to reflect upon the eras they are studying and extract general widely applicable conclusions or even questions. In other words, the conventions of instruction work against both automatic (low road) and mindful (high road) transfer.In response to such dilemmas, one can define two broad instructional strategies to foster transfer: hugging and bridging (Perkins and Salomon 1988). Hugging exploits reflexive transfer. It recommends that instruction directly engage the learners in approximations to the performances desired. For example, a teacher might give students trial exams rather than just talking about exam technique, or a job counselor might engage students in simulated interviews rather than just talking about good interview conduct. The learning experience thus ``hugs'' the target performance, maximizing likelihood later of automatic low road transfer.Bridging exploits the high road to transfer. In bridging, the instruction encourages the making of abstractions, searches for possible connections, mindfulness, and metacognition. For example, a teacher might ask students to devise an exam strategy based on their past experience, a job counselor might ask students to reflect on their strong points and weak points and make a plan to highlight the former and downplay the latter in an interview. The instruction thus would emphasize deliberate abstract analysis and planning. Of course, in the cases of exam technique and job interview, the teachers might do both. Instruction that incorporates the realistic experiential character of hugging and the thoughtful analytic character of bridging seems most likely to yield rich transfer.In summary, a superficial look at how research on transfer casts its vote is discouraging. The preponderance of studies suggest that transfer comes hard. However, a closer examination of the conditions under which transfer does and does not occur and the mechanisms at work presents a more positive picture. Education can achieve abundant transfer if it is designed to do so.