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SCIENCE LEARNING IN EVERYDAY LIFE Lynn D. Dierking, John H. Falk, Section Coeditors Using the Contextual Model of Learning to Understand Visitor Learning from a Science Center Exhibition JOHN FALK, MARTIN STORKSDIECK Institute for Learning Innovation, Annapolis, MD 21401, USA Received 13 May 2003; revised 14 December 2004; accepted 21 January 2005 DOI 10.1002/sce.20078 Published online 18 July 2005 in Wiley InterScience (www.interscience.wiley.com). ABSTRACT: Falk and Dierking’s Contextual Model of Learning was used as a theoreti- cal construct for investigating learning within a free-choice setting. A review of previous research identified key variables fundamental to free-choice science learning. The study sought to answer two questions: (1) How do specific independent variables individually contribute to learning outcomes when not studied in isolation? and (2) Does the Contextual Model of Learning provide a useful framework for understanding learning from museums? A repeated measure design including interviews and observational and behavioral measures was used with a random sample of 217 adult visitors to a life science exhibition at a major science center. The data supported the contention that variables such as prior knowledge, interest, motivation, choice and control, within and between group social interaction, ori- entation, advance organizers, architecture, and exhibition design affect visitor learning. All of these factors were shown to individually influence learning outcomes, but no single fac- tor was capable of adequately explaining visitor learning outcomes across all visitors. The framework provided by the Contextual Model of Learning proved useful for understand- ing how complex combinations of factors influenced visitor learning. These effects were clearerest when visitors were segmented by entry conditions such as prior knowledge and interest. C 2005 Wiley Periodicals, Inc. Sci Ed 89:744 – 778, 2005 Correspondence to: John Falk; e-mail: [email protected] C 2005 Wiley Periodicals, Inc.

The Contextual Model of Learning

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Page 1: The Contextual Model of Learning

SCIENCE LEARNING IN EVERYDAY LIFE

Lynn D. Dierking, John H. Falk, Section Coeditors

Using the Contextual Model ofLearning to Understand VisitorLearning from a Science CenterExhibition

JOHN FALK, MARTIN STORKSDIECKInstitute for Learning Innovation, Annapolis, MD 21401, USA

Received 13 May 2003; revised 14 December 2004; accepted 21 January 2005

DOI 10.1002/sce.20078Published online 18 July 2005 in Wiley InterScience (www.interscience.wiley.com).

ABSTRACT: Falk and Dierking’s Contextual Model of Learning was used as a theoreti-cal construct for investigating learning within a free-choice setting. A review of previousresearch identified key variables fundamental to free-choice science learning. The studysought to answer two questions: (1) How do specific independent variables individuallycontribute to learning outcomes when not studied in isolation? and (2) Does the ContextualModel of Learning provide a useful framework for understanding learning from museums?A repeated measure design including interviews and observational and behavioral measureswas used with a random sample of 217 adult visitors to a life science exhibition at a majorscience center. The data supported the contention that variables such as prior knowledge,interest, motivation, choice and control, within and between group social interaction, ori-entation, advance organizers, architecture, and exhibition design affect visitor learning. Allof these factors were shown to individually influence learning outcomes, but no single fac-tor was capable of adequately explaining visitor learning outcomes across all visitors. Theframework provided by the Contextual Model of Learning proved useful for understand-ing how complex combinations of factors influenced visitor learning. These effects wereclearerest when visitors were segmented by entry conditions such as prior knowledge andinterest. C© 2005 Wiley Periodicals, Inc. Sci Ed 89:744–778, 2005

Correspondence to: John Falk; e-mail: [email protected]

C© 2005 Wiley Periodicals, Inc.

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INTRODUCTION

Few activities will be more important to 21st century free-choice science educationinstitutions in general and science museums1 in particular than meaningfully understandingthe learning they facilitate. Whereas only a few years ago it could be fairly stated that itwas unclear whether visitors to museums truly learned (Crane, 1994; Falk & Dierking,1992, 1995), today the same could not be said. A myriad of studies now clearly documentthe range of learning that museums afford (cf. Falk, 1999; Leinhardt, Crowley & Knutson,2002; Rennie & McClafferty, 1996). However, a full understanding of the complexitiesof the processes of learning that occurs during a visit to a free-choice setting remainselusive.

Historically, much of the research on learning in museums was a-theoretical. This ischanging; currently a variety of theoretical frameworks have been proposed for under-standing the nature of learning from museums, two of these are particularly prevalent---sociocultural models based on the work of Vygotsky (cf. Leinhardt et al., 2002; Martin,2004) and the Contextual Model of Learning as proposed by Falk and Dierking (1992,2000). The work described here was based on the latter of these two models.

Contextual Model of Learning

Falk and Dierking (2000) put forward the Contextual Model of Learning as “a device fororganizing the complexities of learning within free-choice settings.” The Contextual Modelof Learning is not a model in its truest sense; it does not purport to make predictions otherthan that learning is always a complex phenomenon situated within a series of contexts.More appropriately, the “model” can be thought of as a framework. The view of learningembodied in this framework is that learning can be conceptualized as a contextually driveneffort to make meaning in order to survive and prosper within the world; an effort thatis best viewed as a continuous, never-ending dialogue between the individual and his orher physical and sociocultural environment. The Contextual Model of Learning portraysthis contextually driven dialogue as the process/product of the interactions between anindividual’s (hypothetical) personal, sociocultural, and physical contexts over time. Noneof these three contexts are ever stable or constant; all are changing across the lifetime ofthe individual. As the museum examples described below help to clarify, the ContextualModel of Learning draws from constructivist, cognitive, as well as sociocultural theoriesof learning. The key feature of this framework is the emphasis on context; a framework forthinking about learning that has also been emphasized by others (e.g., Ceci 1996; Ceci &Bronfenbrenner, 1985; Sternberg & Wagner, 1996).

The personal context represents the sum total of personal and genetic history that an indi-vidual carries with him/her into a learning situation. Building upon constructivist theories oflearning, the influences of prior knowledge and experience on museum learning have beenwidely described and documented (Dierking & Pollock, 1998; Falk & Adelman, 2003;Gelman, Massey, & McManus, 1991; Hein, 1998; Roschelle, 1995; Silverman, 1993);so, too, the role of prior interest (e.g., Adelman et al., 2001; Adelman, Falk, & James,2000; Csikzentmihalyi & Hermanson, 1995; Falk & Adelman, 2003). The exact nature ofa visitor’s motivations, or “agenda”, for visiting a museum has also been shown to sig-nificantly influence the visitor’s learning outcomes (e.g., Falk, 1983; Falk, Moussouri, &Coulson, 1998; Graburn, 1977; Hood, 1983). More recently, it has been appreciated thatthe degree of choice and control over learning also affects visitor learning (e.g., Griffin,

1In this paper we use the term “museum” to generically refer to museum-like institutions includingscience centers, museums of science and industry, natural history museums, etc.

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1998; Lebeau, et al., 2001). Thus, from the personal context perspective, one should expectnew learning to be scaled to the realities of an individual’s motivations and expectations,which in the case of museums normally involve a brief, usually leisure-oriented, cultur-ally defined experience. One should expect learning to be highly personal and stronglyinfluenced by an individual’s past knowledge, interests and beliefs. One should expectlearning to be influenced by an individual’s desire to both select and control his/her ownlearning.

Humans are extremely social creatures. We are all products of our culture and socialrelationships (Ogbu, 1995; Wertsch, 1985). Hence, one should expect museum learning toalways be socioculturally situated. Factors affecting learning have been hypothesized toinclude such large-scale influences as the cultural value placed upon free-choice learning(Ogbu, 1995) as well as the cultural context of the museum within society (Bal, 1996;Bennett, 1995; Hooper-Greenhill, 1992); although this is almost certainly true, empiricalevidence for these impacts are difficult to find. However, considerable research now existswhich shows that visitors to museums are strongly influenced by the interactions and col-laborations they have with individuals within their own social group (Borun et al., 1997;Crowley & Callanan, 1998; Ellenbogen, 2002; Schaubel et al., 1996). Research has alsoshown that the quality of interactions with others outside the visitor’s own social group,for example museum explainers, guides, demonstrators, performers or even other visitorgroups, can make a profound difference in visitor learning (Crowley & Callanan, 1998;Koran et al., 1988; Wolins, Jensen, & Ulzheimer, 1992).

Finally, learning always occurs within the physical environment, in fact is always adialogue with that physical environment. Thus, one should expect visitors to museumsto react to the physical context of the museum itself; which includes both the large-scaleproperties of space, lighting, and climate as well as the smaller scale aspects such as theexhibitions and objects contained within. Since museums are typically free-choice learningsettings, the experience is generally voluntary, nonsequential, and highly reactive to whatthe setting affords (Falk & Dierking, 2000). As such, visitor learning has been shown to bestrongly influenced by how successfully visitors are able to orient within the space (e.g.,Evans, 1995; Falk & Balling, 1982; Falk, Martin, & Balling, 1978; Kubota & Olstad, 1991;Hayward & Brydon-Miller, 1984); being able to confidently navigate within a complexthree-dimensional environment turns out to be highly correlated with what and how muchan individual learns. Similarly, intellectual navigation, as supported by quality advanceorganizers (Anderson & Lucas, 1997; Falk, 1997), has been shown to affect visitor learningfrom museums. Research has also shown that a myriad of architectural design factors suchas lighting, crowding, color, sound, and space subtly influence visitor learning (Coe, 1985:Evans, 1995; Hedge, 1995; Ogden, Lindburg & Maple, 1993). Considerable research hasfocused on the exhibitions and labels themselves since they are designed to be the primaryteaching tool within museums. Not surprisingly then, ample evidence exists that exhibitiondesign features influence learning, in particular the sequencing, positioning, and contentof exhibitions and labels (Bitgood & Patterson, 1995; Falk, 1993; Serrell, 1996), as wellas how many exhibit elements a visitor attends to, and for how long (Bitgood, Serrell, &Thompson, 1994; Serrell, 1998). Finally, less well documented, but theoretically compellingis the expectation that learning from museums will not only rely on the confirmation andenrichment of previously known intellectual constructs but will equally depend upon whathappens subsequently in the learner’s environment since learning is not an instantaneousphenomenon, but rather a cumulative process of acquisition and consolidation (Anderson,1999; Bransford, Brown, & Cocking, 1999; Medved, 1998). Thus, experiences occurringafter the visit frequently play an important role in determining, in the long term, what isactually “learned” in the museum. Recent longitudinal studies show that the learning that

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results from a museum experience does change over time, and not always just by declining(Anderson, 1999; Adelman et al., 2001; Falk et al., 2004; Goldman et al., 2001; Medved,1998).

The Contextual Model of Learning provides the large-scale framework with which toorganize information on learning. Inside the framework hang the details. These detailsare myriad. The total number of factors that directly and indirectly influence learningfrom museums probably number in the hundreds, if not thousands. Some of these factorsare apparent and have been summarized above and in previous publications (cf. Falk &Dierking, 2000), others are either not apparent or are not currently perceived by us to beimportant. After considering the findings from hundreds of research studies including theones cited above, 12 key factors, or more accurately suites of factors, emerged as influentialfor museum learning experiences. These 12 factors are

Personal context

1. Visit motivation and expectations2. Prior knowledge3. Prior experiences4. Prior interests5. Choice and control

Sociocultural context

6. Within group social mediation7. Mediation by others outside the immediate social group

Physical context

8. Advance organizers9. Orientation to the physical space

10. Architecture and large-scale environment11. Design and exposure to exhibits and programs12. Subsequent reinforcing events and experiences outside the museum

Research has shown that these 12 factors contribute to the quality of a museum experience,though the relative importance of any one of these factors may vary between particularvisitors and venues (e.g., science centers, natural history museums, zoos, planetaria, naturecenter, etc.). While there exists evidence that each of these factors influences learning, wedo not currently know to what extent each of these factors contributes to learning outcomes,in what ways, and for whom.

At various times, the above cited authors, and others, have made a case for one of thesefactors being THE critical variable influencing learning from museums. Arguably, all areimportant, but are one or two of these factors more important than the others, particularlywhen they are not studied in isolation since little is known about the combined effect ofthese variables or the relative significance and importance of each factor when measuredsimultaneously? Or, alternatively, do none of these factors, individually, satisfactorily ex-plain visitor science learning from a science exhibition as would be hypothesized by theContextual Model of Learning?

This research study was intended as a first attempt toward answering these questions,and to our knowledge, the first attempt to systematically investigate all of these factors

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simultaneously within a single study. Specifically, this study sought to answer twoquestions:

• How do specific independent variables individually contribute to learning outcomeswhen not studied in isolation?

• Does the Contextual Model of Learning provide a useful framework for understanding(the complexity of) learning from museums?

In order to answer these two questions, we compared in one study 11 of the 12 factorsdescribed above; each representing suites of variables assumed to effect learning frommuseums. We were interested in determining which of the factors, when directly comparedwith one another, was important and for which type of visitor. Another, closely relatedquestion---How do collections of independent variables contribute to learning outcomes?---will be addressed elsewhere (Storksdieck & Falk, in preparation). The 12th factor, the roleof subsequent reinforcing experiences, will also be addressed in a separate article (Falk &Storksdieck, in preparation).

MATERIALS AND METHODS

Design

The study was based on a repeated measure design that included pre/post interviews(closed and open-ended questions, self-report items, and test items) and observational andbehavioral measures obtained through unobtrusive tracking of all respondents throughoutthe duration of their science center exhibition experience (Table 1).

Setting and Content

The site for this investigation was the World of Life (WoL) exhibition at the CaliforniaScience Center, Los Angeles. This large, permanent exhibition was designed to communi-cate the overall message that all life, whether composed of a single cell or many specializedcells, must perform certain life processes to survive. The five basic life processes describedin the exhibition are living things all: (1) take in energy, (2) take in supplies and get ridof wastes, (3) react to the world around them, (4) defend themselves, and (5) reproduce

TABLE 1Summary of Repeated Measures

Element of Study Entry Interview Tracking Exit Interview

Mean duration 17 min 47 min 16 minMeasures • Personal meaning

mapping• Unobtrusive

observation(tracking)

• Personal meaningmapping

• Open-ended,focused questions

• Runningcommentary

• Open-ended,focussed questions

• Multiple-choicequestions

• Multiple-choicequestions

• Self-report items • Self-report items

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and pass on genetic information to their offspring (Combs, 2001). In addition to exhibitsthat provided examples of the five basic life processes within animals, plants, and humans,the WoL included a 15-min BodyWorks-presentation. This production combined a 15 m(50 foot)-long female android called Tess with an animated cartoon character to conveythe importance of keeping a metabolic balance (homeostasis) under varying external con-ditions, and to communicate the idea that organs in our body work together to maintainhomeostasis.

Both front-end and formative evaluations were completed during the exhibition’s devel-opment, and in 1998 a complete summative evaluation was conducted to determine howsuccessfully these messages were conveyed (Falk & Amin, 1998). For the summative eval-uation, over a hundred visitors to the exhibition were tracked, observed, and interviewed(within the museum). Visitors were asked a series of questions related to their understandingof life processes and the relationship of humans to other life forms, both prior and subse-quent to their visit to the World of Life. The results revealed a significant change in publicunderstanding of the overarching message and conceptual change in understanding relativeto four out of the five life process areas. We determined that this exhibition would lenditself well to an investigation of science center learning because it is a popular, well-likedexhibition, combining a mix of media and presentation styles, which demonstrably facili-tates significant short-term learning. Equally important, the results of the earlier evaluationsshowed that visitors to the exhibition evidenced a range of learning outcomes which wouldafford the variability necessary to test the assumptions of the study.

Sample

Between December 2000 and April 2001, a nonbiased sample of 217 adults visitingthe science center alone or as part of a family group participated in the study. The studyinvolved 7 dependent (learning measures) and 63 independent measures (including multiplestrategies for measuring aspects of each of the 11 of the 12 factors described above, plus12 demographic variables such as age, gender, time of day, day of week, race/ethnicity,residence, etc.). Of the independent measures, 34 were scaled and 29 categorical or nominal.

The visitor sample used in this study was representative of the overall visiting populationof the California Science Center (see Table 2).

TABLE 2Comparison Between This Sample and CSC Visitors

CSC Visitor Statistics This Study(1998)a (%) (n = 190) (%)

Sexb

Female 60 52.9Male 40 47.1

Race/ethnicityc

Caucasian 48 48.9Latino 21 23.7African American 16 15.3Asian/Pacific Islander 10 5.8Other 5 6.3

aTotal N was not available.bChi-square = .53; p = .47.cChi-square = 1.86; p = .76.

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Methodology

Overall Strategy. The basic research design included face-to-face interviews before andafter the gallery visit and unobtrusive tracking of visitors during the visit. The methodfor selecting visitors was designed to be unbiased and broadly representative of the typ-ical visiting public of the CSC. An imaginary line was drawn in the hallway leading tothe WoL exhibition. Every fifth group of individuals crossing that line were approached.Groups could include one individual or several, only adults or adults with children; orga-nized groups such as school or summer-camp groups were excluded. Upon approachingthe group, the investigator randomly selected one adult in the group and invited that indi-vidual to participate in an investigation to “help us learn more about our visitors.” If theyagreed to participate, the full procedure was implemented, if they refused, the method wasrepeated again. Eighty-one percent of visitors asked to participate agreed to take part in theinvestigation. Upon receiving permission, the researcher carried out a previsit interview,then requested the individual to consider participating in a second interview following thevisit, and informed the individual that s/he would be followed through the exhibition, withthe primary purpose of noting the time of exit.

The entry interview consisted of three parts. Using a method called personal meaningmapping (PMM), a derivation of concept mapping, respondents were first asked to brain-storm about the term “living things”; answers were used to probe deeper into the person’sunderstanding (see below for more details). After the PMM, respondents were asked two re-lated, open-ended questions about processes that all living things have in common amongstthemselves and with humans. Subsequently, respondents were asked to choose the best offour answer options to three multiple-choice questions on basic life processes. Subsequentrating questions (1–6) asked respondents to assess their knowledge of biology (1 item),rate their interest in biology and child development (2 items), asked for reasons for visitingthe science center (3 items), had respondents rate their museum visiting strategy (fromrandomly perusing to highly organized selecting), and asked respondents to describe theirprevious experience with the setting (1 item).

Tracking individuals within the World of Life involved following them closely enoughto observe their social interactions and gauge their level of interaction with specific exhibitelements. Given the popularity, size, and spatial complexity of the exhibition, it was possibleto remain sufficiently distant from the visitor to be almost totally unobtrusive. In addition tonoting the visitor’s engagement with each exhibit element in the WoL, and marking for eachexhibit the type of social interaction, the observer also documented the visitor’s apparentcontrol over his/her visit (e.g., were they determining what to look at or were others in thegroup such as children driving the visit), the overall degree of social interaction within thesocial group and with staff and other visitors outside their group, the degree to which thevisitor seemed oriented, and the average crowdedness of exhibition during the visitor’s stay.Researchers recorded on a detailed map of the exhibition the visitor’s exact movements andnoted additional information about the visit.

When a subject exited the World of Life, the researcher again approached him/her andasked to conduct a postvisit interview. The refusal rate was around 12%. Visitors refused exitinterviews mostly because they were late for an IMAX show for which they had purchasedtickets or their parking meters were running out. The exit interview repeated the knowledgeportion of the entry interview (PMM, open-ended, and multiple-choice questions). Addi-tionally, respondents were asked to rate on a scale from 1 to 6 the architecture and interiorspace of the building, their perceived level of control over the visit, the quality of choicespresented by the exhibition, and the quality of interaction with staff members. Individualswho agreed to participate in a postvisit interview were asked to provide contact information

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for a follow-up interview at a later date (refusal rate = 15%). Visitors who asked about thetrue nature of the study were told and were so noted; others were not informed, so as toavoid tainting any interviews that might be conducted at a later date.

Development of Instrument.

Dependent Variables. The dependent measure for this investigation was deemed to bechanges in an individual’s understanding of life science. We were interested in determiningwhether individuals acquired new or enhanced understanding of facts and/or concepts aboutbiology after spending time within this life science exhibition. As has been argued before(cf. Falk & Dierking, 2000; Dierking et al., 2002), science centers support a wide range oflearning outcomes; this particular study only focused on one kind of learning---changes inan individual’s knowledge of life science---and obviously, only those changes measurablewithin the constraints of a free-choice learning setting where individuals are only willingto provide a limited amount of time and attention to the assessment process. We did notspecifically distinguish between various forms of knowledge and understanding (awareness,recall of latent knowledge, acquisition of new factual knowledge, conceptual change) for ourlearning measures; the measures provided opportunity for visitors to demonstrate changein any of these forms.

Our technique for capturing these cognitive changes was based upon a series of repeatedmeasures using three very different learning assessment tools. The nearly identical pre- andpost-visit interviews each involved asking visitors to share their knowledge of life scienceby responding to (1) three multiple-choice questions, (2) two open-ended questions, and(3) personal meaning mapping. These were administered through both paper and pencilinstruments and semistructured interviews and incorporated both traditional, positivist ap-proaches to measuring knowledge and conceptual change (change in the number of correctanswers) and more constructivist measures that allowed visitors to define or choose thetopics of conversation while researchers would still assign quantitative measures for thequality or correctness of the visitors’ answers, like the breadth, depth, or mastery withwhich visitors were able to express themselves in their chosen area of life science.

The multiple-choice questions provided a general measure of visitor understanding ofbasic physiology. During pilot testing of this instrument, 10 topic-relevant standardized,multiple-choice questions related to the content of the World of Life exhibition were se-lected from a widely used high school biology textbook entitled Biology: The Dynamicsof Life (Biggs et al, 1998), a text endorsed by the National Science Teacher Associationin their 2000 Instructional Resources Catalogue. During a pilot study at the CaliforniaScience Center, 20 visitors answered the multiple-choice questions both before and aftertheir visit. The three questions which revealed the greatest percent change from incorrect tocorrect responses were included on the final instrument (see Appendix). One question testedrespondents’ understanding of the need for energy to drive life processes, the second ques-tion tested respondents’ awareness of the principle of homeostasis, and the third questiontested whether respondents could identify the need for all organisms to respond to outsidestimuli.

The open-ended questions intended to measure whether visitors comprehended the overallmessage of World of Life. The first question stated, “There are certain things that living thingsdo. Do you know anything about these things?” The question was generally modulated bya subsequent clarifying question “What is it that all living things have in common?” Thesecond question asked, “Do you think there are any characteristics that are common betweenhumans and other living things?” Again, the original question was usually reinforced bya subsequent clarifying statement “What is it that humans have in common with all other

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living things?” The researcher prompted the respondents to elaborate further after eachgiven answer. The researcher wrote down verbatim the visitor’s responses. Answers to bothquestions were pooled and thus treated as if they were answers to one question: “What isit that all livings things, including humans do/have in common?” The question basicallyprobed for visitors’ understanding of basic life processes like reproduction, metabolism,defense, response to stimuli, etc., though visitors expanded the range of possible answersinto ecological, evolutionary, and spiritual dimensions. Visitor responses were coded in twodimensions---the number of conceptual categories with which visitors were able to map thescope of the question and the depth with which they were able to provide explanations withinthe conceptual categories they provided. Coding categories emerged from data and wererefined in an iterative process until two independent researchers agreed on them (Mayring,1997). The original answers were coded in two dimensions: (1) The breadth of responsesdefined as the number of conceptual categories (out of a total of 21 nonoverlapping categoriesgenerated by the total pool of visitors) used by an individual in answering the two questions;and (2) the depth of responses defined as the detail and sophistication of answers providedwithin the conceptual categories a respondent provided to the two questions. Inter-raterreliability for the breadth score of the open-ended questions was r = .94, and r = .83 forthe depth score.

Personal meaning mapping (PMM) is a relatively new instrument similar to conceptmapping (cf. Falk et al., 1998; Falk, 2003) designed to assess changes in an individual’sconceptualization of a topic over time. Like the open-ended questions, PMM allowed re-spondents to describe their knowledge of living things, with the researcher probing for moredetail following each item. The approach involves asking individuals to write down on ablank piece of paper (with the cuing word, phrase or image printed in the middle of the page)as many words, ideas, images, phrases or thoughts as come to mind related to the cueingword or phrase. In this study, the cueing phrase displayed at the center of the page was livingthings. The words, ideas, images, phrases, or thoughts written down in response to the initialcue form the basis for an open-ended interview. Individuals were encouraged to explainwhy they wrote down what they did, e.g., “You wrote down animals. Tell me, what doanimals have to do with living things?” and to expand on their thoughts or ideas relative tothe cueing phrase. The goal was to “unpack” the individual’s conceptual framework for theidea(s) represented in the cueing phrase, using their own words as prompts. The individual’sresponses were recorded verbatim on the same piece of paper by the researcher. To permitdiscrimination between unprompted and prompted responses, the follow-up interview datawas recorded in a different color ink than were the initial words, phrases, etc. recorded bythe individual themselves. Following the educational intervention---in this study, the Worldof Life exhibition---individuals were asked to review their previous PMM and invited toadd, delete, modify, or change their responses. These changes were noted in a third colorink. Finally, these additional comments or thoughts formed the basis of a second open-ended follow-up interview. The results of this interview were recorded in a fourth colorink.

Personal meaning maps are designed to measure change in an individual’s conceptual-ization of the prompt along four dimensions---extent, breadth, depth, and mastery. Extentrefers to changes in the number of appropriate words the subject used to describe the promptsince one measure of increased understanding is an increase in the vocabulary an individualhas available for describing a concept or phenomenon. (Note: Since PMM measures eachindividual’s change score, the differences in “verbosity” between individuals factors out.)Extent thus measures the most basic aspect of an individual’s understanding of a conceptor topic, the degree to which they can generate words to describe their understanding.Often an exhibition experience in a museum does not change an individual’s conceptual

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understanding, but it does enrich those concepts by providing concrete examples of the con-cept; extent is designed to capture this dimension of learning. Breadth measures changesin the number of conceptual categories an individual uses to describe the prompt. Breadth,thus, measures a fundamental aspect of learning that an idea or phenomenon can be under-stood in more than one way. For instance, before entering the World of Life exhibition avisitor in response to the prompt living things might have talked about spiritual and ecolog-ical aspects of life; after their visit they might have now included aspects of physiology andgenetics. As with the breadth score for the open-ended question, coding categories emergedfrom data and were refined in an iterative process until two independent researchers agreedon them (Mayring, 1989). Depth measures the changes in degree of understanding withineach breadth category and is therefore a measure of conceptual understanding. Increaseddepth occurs as individuals are able to provide not only more examples within a concept,but also better examples and demonstrate a deeper, more sophisticated understanding ofa specific conceptual category. Finally, the fourth dimension is mastery, a holistic assess-ment which measures the changes in individual’s overall understanding. Mastery can thusbe seen as a more traditional measure of learning, it is designed to be a holistic measure,taking into account all of things an individual said during the PMM process in order togauge where an individual falls along a continuum between novice and expert relative tothe specific concept or phenomenon represented by the prompt. The four constructs extent,breadth, depth, and mastery were designed to be independent and complementary measuresof learning, capturing different aspects of cognitive gain in a free-choice learning environ-ment. Inter-rater reliability was established for each of the four PMM measures and foundto be high for extent (r = .96) and for breadth (r = .88), satisfactory for depth (r = .76) andmastery (r = .78).

Thus for each individual, we collected seven separate measures of life science learning---four measures from PMM (extent, breadth, depth, and mastery), two from the open-endedquestion (breadth and depth), and one measure derived from the three multiple-choicequestions. It was assumed that each of the three methods we used was biased; each excludedsome important aspects of science learning. For example, we know that since we interviewedvisitors as individuals at the beginning and end of their experience we lost the opportunityto tap into the distributed learning that was likely present during the learning process. Wealso know that much of what was likely learned had not yet been consolidated into memoryand was thus unavailable to the visitors; this we attempted to capture through subsequentfollow-up investigations, the results of which will be reported elsewhere.

We also know that there was a possible cueing bias because of our repeated measuresapproach. Although a previous investigation in a comparable setting using many of thesame methods, including such highly intrusive methods as interviews and personal mean-ing mapping, found no evidence that pre-experience interventions significantly enhancedlearning amongst museum visitors (Adelman et al, 2001), we cannot rule out the possibilitythat such an impact occurred. In this study, we did not include a control group, but we diddevelop a variable called “interviewer effect” to try and assess our impact. This variablewas created by scaling the time the researcher spent talking with a visitor prior to theirexhibition experience; presumably, the longer the intervention, the greater the likelihood ofimpact. There was no significant overall correlation for this variable with changes in visitorscience learning on any of our seven learning measures. There was no evidence (statisticalor anecdotal) that these research interventions significantly impacted the science learningof the majority of visitors. As was argued by us previously (Adelman et al., 2001), visitorsto free-choice learning settings are there for a myriad of reasons, but pleasing researchersis not one of them. In both school and laboratory settings, research subjects have goodreason to believe that they will be rewarded for doing “well,” “well” as defined by the

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754 FALK AND STORKSDIECK

experimenter. Free-choice learning settings appear to be very different research contextsthan either schools or laboratories. In the free-choice learning context, doing “well” gener-ally means satisfying intrinsic rather than extrinsic agendas. This is, in part, why one in fivevisitors refused to participate in this investigation and 15% of those who initially agreedto participate felt no compunction in refusing to take part in the postexperience measures.Thus, despite the limitations of our dependent measures, we believe that collectively thesevery different ways of capturing changes in visitor life science knowledge provided us withmeasures of short-term science learning sufficiently robust to answer the questions we setout to investigate.

Independent Variables. Independent variables create an impact on outcome or depen-dent variables. For example, the main independent variable in this study was the educationalintervention---the visitor’s experience in the World of Life exhibition. However, as describedby Falk and Dierking’s (2000) Contextual Model of Learning, the visitor’s experience isactually a complex of independent variables (factors) such as design, setting, advance orga-nizers, orientation, and subsequent reinforcing experiences. Within the Contextual Modelof Learning, Falk and Dierking place these variables within the physical context. However,the Contextual Model of Learning posits that a range of other independent variables (factors)not directly associated with the educational intervention may also affect learning outcomes.These include personal context variables such as prior knowledge, prior experience, priorinterest, visit motivations and expectations, and choice and control, and sociocultural con-textual variables such as interactions within one’s own social group and interactions withindividuals outside one’s own social group. From the elements in the model, hypotheseswere derived from which concept variables and then measured variables were developed.The measured variables formed the basis of questions for a pre- and post-visit interviewguide (Foddy, 1993; Kvale, 1996; Stangor, 1998).

As described earlier, each of the major factors under investigation was, in itself, a mul-tidimensional construct. Therefore, it was not possible to fully measure factors such as“interest” or “motivation and expectations.” What we did attempt to measure were dimen-sions of these constructs; dimensions we believed to be significant components of thesefactors. Since most of these factors had been previously investigated in comparable set-tings, we began by assembling as many pre-existing measures as we could. In addition,we collected previous measures for a range of traditional independent variables such asgender, race/ethnicity, time of day, day of week, etc. Our goal was to have three semi-independent ways for measuring each of the 11 factors described above; if we could notfind 3 existing measures, we created additional measures. All measures were developed inconsultation with a Research Methodology Committee and pilot tested on 20 science centervisitors. Summaries of each measure are briefly described below and are summarized inTable 3.

Visit Motivations and Expectations. We first asked each individual to describe, withoutcueing, their reasons for coming to the science center that day. Previous research (cf. Falket al., 1998) had identified three major reasons leisure visitors come to science centers---learning, entertainment, and social “bonding.” We asked visitors to rank on a scale of 1 to6 how important to them each of these three reasons were for them on this particular trip tothe science center. We asked each respondent to explain the rationale behind their ratings.In addition we asked visitors directly about their plans for that day’s visit.

Prior Knowledge. We collected extensive previsit data from each visitor on their knowl-edge of life science as part our dependent measures (i.e., multiple-choice questions, open-ended questions, and PMM). In addition, each visitor was asked to self-rate their knowledgeof biology and provide an explanation for that rating.

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VISITOR LEARNING FROM A SCIENCE CENTER EXHIBITION 755

TABLE 3Independent Variables (Factors Hypothesized to Influence Learning)

Variable Suite Measure Scale

1. Motivation and expectation • Intent to learn about the world 1–6• Intent to have a good time 1–6• Intent to entertain family/friends 1–6

2. Prior knowledge • Visitor self-rating of biologyknowledge

1–6

• Measured knowledge (pre score) Various3. Prior experiences • Visited CSC before Yes/No

• Visited World of Life before Yes/No4. Prior interest • Combined prior interest scales

(interest in biology and in watching adocumentary on child development)

2–12

5. Choice and control • Researcher rating of visitors’ abilityto choose exhibits

1–6

• Visitor self-rating of ability to chooseexhibits

1–6

6. Within group social mediation • Researcher’s rating of intensity ofinternal social interaction

1–6

• Number of adult social interactions(adult/adult, adult/child)

0–39

7. Facilitated mediation by others • Researcher’s rating intensity ofexternal social interaction

1–6

• Visitors’ rating of staff interactionusefulness

1–6

8. Advance organizers • Total engagement with advanceorganizer exhibits

0–12

• Movement pattern through clusteredexhibits

1–4

9. Orientation to the physical space • Researcher judgment of orientation 1–6• Visitor judgment of orientation 1–6

10. Physical environment • Degree of crowding 1–611. Design of exhibits

(quality and exposure)• Quality: average engagement score

for top 10% exhibits0–4

• Quality: average engagement scorefor top 24% exhibits

0–4

• Exposure: Total length of stay inWorld of Life

10–122 min

• Exposure: Hit rate (Percent of totalexhibits visited)

0–100%

• Exposure: Overall average intensity(average engagement score for allexhibits)

0–4

Interviewer effect • Length of entry interview 7–32 min.

Prior Experience. Prior experience was assessed with multiple self-report items:

1. Whether they had previously visited the World of Life;2. Whether they had previously visited the California Science Center or its predecessor,

the California Museum of Science in Industry (prior to 1998);

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756 FALK AND STORKSDIECK

3. The approximate number of years since their last visit to the California ScienceCenter/California Museum of Science and Industry;

4. Whether they had previously visited other science museums/centers.

Prior Interest. We assessed visitor’s prior interest in the topic of life science using two self-report items on a scale from 1–6, and two open-ended questions that provided backgroundinformation on the rating scales:

1. Ratings of interest in biology as a general subject and reasons for why they gave therating;

2. Description of specific topics in biology considered interesting to them and why; and3. Ratings of interest in viewing a television documentary on child development and

explanations for given ratings.

We pooled the scores of the two interest ratings (biology and child development) to createa more broadly based interest score.

Choice and Control. Visitors’ perceived choice and control was measured both by self-report and by external observation and rating. During the postinterview, visitors were askedto rate on a scale from 1–6 the degree to which they perceived that they, rather than someoneelse in their social group were in control of the visit. Visitors also self-rated on a scale from1–6 the degree of choice they experienced while in the gallery and were asked to providejustifications for their ratings. (This variable was later judged by us to indicate visitors’ over-all satisfaction with the exhibition, rather than their perception of the degree with whichthey were able to choose according to their own interest and preferences.) Independently,the researcher also rated each visitor on a scale from 1–6 on the degree to which subjectsappeared to be making their own exhibit choices and be in control of their visit experi-ence. This rating was largely based on whether the individuals appeared to be controllingtheir own movements or appeared to be manipulated by other members of their visitinggroup.

Social Interaction. By virtue of being a destination for social outings, the science centernaturally facilitates learning via social interactions, which can be divided into two cate-gories: (a) interactions amongst members of one’s own social group, and (b) external socialinteractions---interactions with staff and others outside of one’s own social group. We did notemploy techniques that would have allowed us to directly and thus reliably monitor actualvisitor conversations, so all ratings were based on second-hand sources---either observed orself-reported. The researcher recorded every instance they observed of social interaction.They noted who it was with and rated it on an intensity scale. In the postexperience in-terview, visitors were asked to self-report the extent and nature of their social interactionsand describe the outcomes of those interactions. The social interaction rating scales rangedfrom 1 (no social interaction) to 6 (high social interaction) for the overall degree of socialinteraction within the group and with persons outside the immediate social group, as ratedby the researcher. Respondents were asked to rate the usefulness of external social interac-tions on a scale from 1 (the staff member was not useful at all) to 6 (the staff member wasvery helpful/useful). Finally we used the number of social interactions (adult–adult andchild–adult) in the exhibition as observational data for the degree of within-group socialinteraction.

Advance Organizers. An advance organizer is anything that provides “intellectual nav-igation” for a subsequent learning experience. In the World of Life, four separate exhibitelements could conceivably act as advance organizers: (1) the overview sign at the gallery

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VISITOR LEARNING FROM A SCIENCE CENTER EXHIBITION 757

entrance, (2) the Life Tunnel introductory panel, (3) the Life Tunnel itself, and (4) the CellTheater video. The researcher recorded whether a visitor interacted with one or more ofthese exhibit elements and recorded an engagement score for each interaction (0–4, fromno engagement to full interaction). Engagement scores for the Life Tunnel panel, the LifeTunnel itself, and the cell theater video were added for a potential maximum score of 12 onthe advance organizer measure. Engagement scores for the overview sign at the entranceof the exhibition were not included as it was determined that they could not be assignedreliably.

In addition, each of the five major conceptual themes of the exhibition was clusteredinto a discreet area; each area contained an introductory exhibit and then a group of simi-larly themed exhibit components in close proximity. Theoretically, visitors who viewed theexhibits systematically within a cluster, starting with the introductory exhibit, acquired anadvance organizer for this theme. Visitors were given a score of 1 to 4 for this use of theexhibition based upon the researcher-generated map of their movements.

Orientation. In this study, measures we used to assess visitor orientation were

1. visitors’ self-reported rating of how strongly they orient themselves and visit strate-gically on a scale from 1 (visit on a “whim,” no map use) to 6 (visit highly organized,almost compulsive map use), with corresponding explanation;

2. visitors’ map use and apparent orientation in the World of Life gallery, as observedand rated by the researcher on the same scale from 1 to 6;

3. visitors were asked whether they had a set plan or path for their visit, and wereasked to indicate whether they felt oriented during their stay in the World of Lifeexhibition.

Physical Environment (Architecture and Large-Scale Environment). Visitors were askedduring the exit interview to rate the building on a scale from 1 (do not like building) to 6(love the building), and were encouraged to base their opinion on a range of factors (e.g.,architecture, lighting, smell, comfort, etc.). The new California Science Center design wasgenerally liked by visitors, and the measure was ultimately too skewed to be useful. However,we also attempted to rate a social aspect of the physical environment---crowdedness. Theresearcher recorded, for each visitor, the degree of crowding in the exhibition on a scalefrom 1 (not at all crowded) to 6 (very crowded).

Exhibit Design. We attempted to measure two important dimensions of the exhibit designfactor. The first relates to the extent of use of exhibits (what has often been referred to in themuseum literature as “holding” and “attracting” power (Screven, 1974); we call this exhibitexposure). The second important dimension relates to the actual quality of the exhibit; Doesthe exhibit actually afford learning? For example, is the interface user-friendly, are the ideaspresented in a comprehensible manner, is the exhibit engaging/fun, and, most importantly,would full engagement with the exhibit likely contribute to a better understanding of theoverall message the exhibit designers and curators were interested in conveying? All of ourmeasures were researcher coded, they were

A. Exhibit exposure1. Total length of stay in the gallery (time);2. Percent of total exhibit elements visited (hit rate or coverage);3. Average intensity of engagement with exhibit elements visited (intensity);B. Exhibit quality4. Engagement scores with the top 10% exhibits;5. Engagement scores with the top 24% of exhibits.

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758 FALK AND STORKSDIECK

An additional explanation is useful for these last two items. We enlisted a panel of threescience museum experts2 who analyzed the 62 individual exhibit components within Worldof Life. Each expert rated, on a scale of 1 (not at all) to 6 (very much), the effectiveness ofeach exhibit element on the basis of

• How does the core message of the exhibit contribute to the overall message of theWoL?

• How does the core message of the exhibit contribute to the submessage of its topicarea?

• How effective is the exhibit in conveying its stated message,– if visitors read the text?– if visitors do not read the text?

• How much ”fun” is this exhibit?

Experts also selected 10 exhibits they considered “best educational exhibits in the WoL”and 10 they considered “worst.” These experts’ ratings were collectively used to select thetop 10% and top 24% of exhibits.

Data Analysis. Data were transcribed from the original interview guides, tabulated inword-processing spreadsheet programs, scored in a variety of ways, and analyzed using ap-propriate parametric and nonparametric statistics, including analysis of variance, Student’st-tests, chi-squares (contingency table analysis), regression analyses (Pearson product mo-ment correlation coefficient and its nonparametric equivalent, the Spearman rank correlationcoefficient), stepwise multiple regression, and factor analysis (principal component analy-sis). Statistical analyses were conducted using SPSS 10.0 and 12.0 for Windows statisticalsoftware package.

RESULTS AND DISCUSSION

Science Learning

The first result of the investigation was that, overall, visitors to the World of Life exhibitiondid, indeed, show evidence of positive improvement in their science learning, independentlyof how we measured it. In fact, there was a significant improvement, on average, shownby each of the seven different measures used for assessing change (see Table 4). Relativepercent changes pre to post ranged from about 5% for the PMM depth score to more than70% for the PMM extent measure. Depending on the measure for cognitive change thatwas employed, between 33% and 91% of visitors surveyed in this study exited the World ofLife exhibition with a measurably enhanced understanding of science. Only 1 in 3 visitorsimproved their multiple-choice scores, roughly half of visitors showed improvement on theopen-ended questions, and large majorities of visitors demonstrated improvement acrossthe various PMM measures.

A factor analysis tested the relationship of the seven dependent learning outcome mea-sures (Table 5). The rotated component matrix suggests that the seven measures form at leastfour semi-independent factors or variables: the multiple-choice questions, the open-endedquestions and two PMM dimensions extent and breadth, and PMM depth and mastery.

2 Experts included a university professor who teaches informal science education, a senior scientist(biologist) at the California Science Center who was hired after completion of the World of Life exhibition,and a senior museum educator at the neighboring LA County Museum of Natural History.

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VISITOR LEARNING FROM A SCIENCE CENTER EXHIBITION 759

TABLE 4Pre-Post Comparisons of Learning Outcomes

PercentRespondents

Mean Mean Change with IncreasedMeasure of Learning Prevalue Postvalue Difference Pre to Post Scores

Sum of three multiple-choice questionsa

2.05 2.38 −.332 16.1%∗ 33.2

Breadth score for open-ended questionsb

4.94 7.09 −2.150 43.5%∗ 90.5

Depth score for open-ended questionsc

1.34 1.53 −.199 14.2%∗ 71.7

Extent score for PMMd 5.98 10.21 −4.230 70.7%∗ 85.9Breadth score for PMMe 4.25 5.64 −1.390 32.7%∗ 76.4Depth score for PMMf 1.62 1.71 −.085 5.2%∗ 53.4Mastery score for PMMg 2.00 2.23 −.231 11.6%∗ 47.6

∗ p < .0001. To reflect the increased probability of a type I error due to multiple comparisons,the significance level for the two-tailed t-test and the Wilcoxon signed ranks test was loweredto p < .007.

T-test results: at = −5.27; df = 189; p = .000. bt = −21.89; df = 190; p = .000; dt =−17.23; df = 190; p = .000.

Wilcoxon signed ranks results: c Z = −8.365; n = 191; p = .000. f Z = −4.666; n = 191;p = .000. g Z = −5.026; n = 189; p = .000.

However, the initial component matrix indicates strong independence between all sevenmeasures. Each of the seven methods for assessing short-term learning gains appeared tomeasure a somewhat different dimension of learning.

There was no evidence that any of the three approaches to measure learning, by them-selves, totally captured the change in science understanding of visitors. We thus proceededto keep the seven measures separate and conduct seven separate initial correlation anal-yses between learning measures and factors that may influence learning in and frommuseums.

Factors Influencing Learning

A total of 24 independent measures, representing the 11 variables, were correlated usingSpearman’s rho against the seven dependent learning measures (see Table 6). In addition,a range of demographic variables were correlated with the seven dependent measures forlearning, or used as category variables in ANOVAs and Student’s t-tests (data not shown).None of the standard demographic variables such as age, gender, and race/ethnicity sig-nificantly influenced learning. Neither did time of day or day of week. Also showing nosignificant effects were the size and nature of the visitor’s social group.

All 11 factors or independent variables emerged as having a significant, though oftensmall, correlation with change in science learning on at least some of our seven learningmeasures (Table 6). The most important independent variable was visitor’s prior knowl-edge. Prescores correlated negatively and moderately with learning. In other words, themore a visitor knew about life science when they entered the exhibit, the less they tendedto gain cognitively from the visit, at least in the short term. Conversely, individuals with theleast prior knowledge showed the greatest gains. This was true across all seven measures of

Page 17: The Contextual Model of Learning

760 FALK AND STORKSDIECK

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Page 18: The Contextual Model of Learning

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Page 19: The Contextual Model of Learning

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Page 20: The Contextual Model of Learning

VISITOR LEARNING FROM A SCIENCE CENTER EXHIBITION 763

learning, though two measures (the depth score for the open-ended questions withrho = −.597, p < .001 and the change in correct answers to the three multiple-choice ques-tions with rho = −.578, p < .001) exhibited a fairly strong inverse association between priorknowledge and knowledge gain, and one (extent with rho = −.197 and p < .01) exhibited aweak negative correlation. The other four measures of learning correlated moderately withtheir prescores (between rho = −.364 and rho = −.453, p < .001).

Few other independent variables exhibited significant correlation coefficients across thevarious measures for learning, and all of the ones that did were related to exhibit qualityor exposure---total length of stay in the WoL (rhomax = .165, p < .05), the percent of totalexhibits visited (rhomax = .213, p < .001), overall engagement score with exhibits visited(rhomax = .192, p < .01), engagement with the top 10% of exhibits (rhomax = .212, p < .01),engagement with the top 24% of exhibits (rhomax = .204, p < .01). Hence, even those fac-tors most closely associated with learning outcomes in museums (exposure to and qualityof exhibit elements), individually, at most correlated weakly with any measure of learning,and none correlated significantly with the number of correct answers to the multiple-choicequestions and the PMM depth score. Thus, while most of the measures that were hypothe-sized to influence learning from museums affected at least some measures of learning, nonedid so to any considerable degree.

The number of adult social interactions, adult–adult and adult–child, significantly cor-related with four measures of visitor science learning (maximum rhomax = .154, p < .05).Two of the advance organizer measures resulted in significant correlations; total num-ber of advance organizer exhibits engaged with correlated with five learning measures(rhomax < .245, p < .001) and tendency to systematically utilize the exhibit clusters as clus-ters correlated significantly only with changes in correct answers to the three multiple-choicequestions (rho = .212, p < .01). Astonishingly, prior experiences with the World of Life ex-hibition resulted in slightly lower cognitive gain (r = −.228, p < .001) with the breadthmeasure for the open-ended questions and the PMM depth score (r = −.157, p < .01).Visitors’ sense of orientation exhibited a positive correlation with the PMM mastery score(rho = .175, p < .05).

Of the original 11 factors investigated, prior interest, choice and control, orientation,and architecture had little to no apparent effect on learning. However, arguably the mostimportant finding is that no variable, including the eleven we focused on, significantlyinfluenced all measures of science learning used in this study for the entire sample ofrespondents. Each of the 24 measures and 11 variables significantly influenced only asubset of learning outcome measures in the total sample of 191 visitors.

Differences in Learning Measures

The changes in correct answers to multiple-choice questions was influenced the leastby the various independent measures; three independent measures, representing two fac-tors, prior interest and advance organizers, correlated significantly with changes in correctanswers to the multiple-choice questions. In contrast, 12 measures, representing 8 indepen-dent variables or factors, correlated significantly with PMM mastery. The seven dependentmeasures of learning were thus not equal in capturing factors that may lead to cognitivegain in a free-choice setting. Conversely, not all of the 11 factors or variables hypothesizedto influence learning in free-choice settings emerged as equally important. The quality ofexhibit elements, exposure to exhibits, advance organizers, within group social mediation,prior experiences, and prior knowledge emerged as the most important independent factorsacross the board (or across the seven measures of learning). However, none of the factorsalone emerged as THE variable that could explain much of the learning we observed and

Page 21: The Contextual Model of Learning

764 FALK AND STORKSDIECK

measured in this study. Furthermore, whether and to what degree a factor might influencelearning from museums differed depending on how learning was measured. Thus, for thesake of simplicity, the remainder of this article will focus on only one measure for learning,the PMM breadth score. Since this particular exhibition was specifically designed to broadenand change visitor’s conceptualization of similarities and differences amongst living things,we deemed that this measure best captured this dimension of learning.

Knowledge Change as a Function of Prior Knowledgeand Prior Interest

One impediment to understanding the impact of the various factors on learning wasthe great heterogeneity of the visiting population we sampled. Theoretically, there aremany ways to segment a group of visitors such as we investigated. It makes most sense tosegment visitors on attributes they possessed entering the experience, thus possible variablesinclude age, gender, race/ethnicity, prior interest, expectations and motivations, and priorknowledge. Previous research (Falk & Adelman, 2003) suggested that prior interest andprior knowledge would be particularly important variables to consider.

Given what we know about the constructive, cumulative nature of learning, it is notsurprising that what someone knew upon entering the World of Life exhibition stronglyinfluenced what they knew when they exited. Those with the most knowledge upon enteringwere still those with the most knowledge upon exiting. However, this did not mean thosewith the most knowledge upon entering the exhibition learned the most. As stated previously,in general those visitors with the lowest previsit scores showed the greatest relative gains,visitors with the highest previsit scores showed the least relative gains. We divided visitorsinto two “prior knowledge” groups based upon their previsit PMM breadth scores; thosewith below median scores (n = 107) and those with above median scores (n = 81).

Next we segmented visitors as a function of their prior interest. Prior interest was deter-mined by a composite measure of visitors’ interest in biology and their interest in watchinga television show related to biology. Both items were measured on a scale from 1–6; theoverall prior interest score, thus, ranged from 2 to 12, with a median score of 9. Visitorswere grouped into a “below median prior interest group” (n = 77) and a “median and abovemedian prior interest group” (n = 111). There were no significant differences in the pre-visit PMM breadth scores (t(2 − tailed) = −.46; d f = 186; p = .65). While prior interest didnot influence the degree of learning, it did influence which of the 11 factors mediated thelearning that occurred (see below). Table 7 shows that depending upon an individual’s priorknowledge and interest, different factors influenced their learning.

Low Knowledge/Low Interest. This group represented roughly one-quarter of the vis-itors (23%) in our sample. As compared to the entire sample of visitors we interviewed,this group possessed the lowest understanding of life science---for all intents and purposes,this group knew very little about biology and the life processes that influence living thingsand also possessed the lowest entering interest levels in biology---they were generally notvery interested in the topic. Only two factors emerged as significantly affecting changesin conceptual understanding of life science---motivations and expectations and advanceorganizers.

For this group of visitors, NOT wanting to have a good time, in other words not be-ing strongly motivated by the need for entertainment, increased the likelihood that theywould gain knowledge of biology (rho = −.299, p < .05). Advance organizers, in particularattending to the various signage and exhibit elements that explained what the exhibitionwas about, increased learning for the visitors in this group (rho = .244, p < .1).

Page 22: The Contextual Model of Learning

VISITOR LEARNING FROM A SCIENCE CENTER EXHIBITION 765

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Page 24: The Contextual Model of Learning

VISITOR LEARNING FROM A SCIENCE CENTER EXHIBITION 767

High Knowledge/Low Interest. This was the smallest (18%) of the four groups of vis-itors in our sample. They possessed a relatively high understanding of life science butexpressed a relatively low interest in the topic; admittedly a strange combination. Interest-ingly though, the visitors in this subgroup were influenced by a large number of contextualfactors. Prior experiences, social mediation, advance organizers, orientation, and exhibitquality and quantity all influenced these visitors’ learning.

Specifically, this group of visitors was the only subgroup who showed an influenceon learning of previous experiences. Prior experiences correlated negatively in this group(rho = −.477, p < .01) for previous visits to the California Science Center and for previousvisits to the World of Life exhibition (rho = −.404, p < .05). Also, only within this subgroupwas social mediation a significant influence on learning. The number of social interactionsin this groups correlated positively and significantly with learning (rho = .344, p < .05).Exposure to the exhibition’s advance organizers also influenced this group; the greater theexposure, the greater the learning (rho = .336, p < .05). This group of visitors’ self-reportedjudgments on how oriented they were also emerged as important: the higher the perceivedorientation, the greater the learning as measured by the PMM breadth scores (rho = .379,p < .05).

Finally, this group of visitors was influenced by virtually all of our measures of exhibitquality and exhibit exposure. This group of visitors benefited by seeing more of the top 24%(rho = .524, p < .01) and top 10% (rho = .480, p < .01) of exhibits as rated by a group ofmuseum experts. They also positively benefited by seeing more exhibit elements; thosethat saw the most exhibit elements had the greatest learning gains (rho = .508, p < .01).Also, the more they engaged with these exhibits, in other words actually did what theywere supposed to do with an exhibit, the greater the learning (rho = .492, p < .01). For thisgroup, the expected outcome that seeing more, high quality exhibits improved learning wastrue, though as we will see, this was the only group for whom this was true. However, themore course-grained measure of total time in the exhibition did not emerge as significant.In fact, overall time in the exhibition did not correlate significantly with learning for any ofthe four subgroups of visitors.

Low Knowledge/High Interest. This was the largest of the four subgroups, comprisingmore than a third of visitors (34%). This group is the most frequently represented popula-tion at most museums (Falk & Dierking, 2000). Four factors correlated significantly withchange in learning, but all relatively weakly---motivation and expectations, prior knowledge,orientation, and exhibit quality.

In this group, a strong desire to visit the museum in order to share the experience witha friend or family member resulted in significant learning; in most cases this was a child(rho = .100, p < .1). In this group, visitor’s judgment that they were well oriented pos-itively correlated with changes in learning (rho = .250, p < .1). Also, at the margins ofsignificance, there was a correlation between seeing more of the top 10% of exhibits andlearning (rho = .235, p < .1). The strongest correlation we found in this group was a nega-tive correlation with prior knowledge (rho = −.312, p < .05), which suggests that this groupwas not as homogeneous with regard to prior knowledge as the other groups---individualswith lower knowledge within this cohort showed greatest gains.

High Knowledge/High Interest. The final subgroup representing about a quarter of thevisitors we sampled (26%) had above median knowledge and above median interest in lifescience as a topic. This group showed effects as a function of motivation and expectation,social interactions with individuals outside their group, and exhibit exposure. Arguably, thefirst of these is the most interesting. Not everyone in this group of knowledgeable, interested

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768 FALK AND STORKSDIECK

TABLE 8Post-Pre Comparisons of Learning Outcomes (PMM Breadth Scores) forMale and Female Visitors

Pre Post

Sex Mean SD Mean SD N Percent Changea

Male 4.03 1.53 5.41 1.85 90 43.4Female 4.45 1.63 5.84 1.86 101 40.2Total 4.25 1.59 5.64 1.86 191 41.7

aPercent change is determined on the sum of the individual percent changes rather thanthe percent change of the mean pre and post values. The percent changes are not differentbetween male and female visitors for the PMM breadth scores (F = .174; df = 1; p = .68).The difference between male and female visitors’ PMM breadth scores was not significantfor p < .05 (F = 3.18; df = 1; p = .076).

individuals learned equally, those who were consciously and specifically motivated to learnduring their visit learned the most (rho = .349, p < .05).

The individuals in this group who deliberately engaged with more exhibits had greaterlearning gains, but only moderately so (rho = .269, p < .1). And then there was the some-what bizarre finding that interactions with science center staff, particularly when theywere rated by visitors as helpful interactions, resulted in decreased learning (rho = −.638,p < .05). Two explanations are required here. First, there was a relatively small sampleof individuals who reported such interactions (n = 14), but clearly for them it was salient.Anecdotal observations suggest that the main interactions that occurred with staff were ask-ing for directions to the restrooms or food service, rather than assistance with interpretation;in other words, positive interactions with staff resulted in visitors exiting the exhibition.

In summary, which variables influenced visitors depended upon the nature of their priorknowledge and interest. Visitors with below median prior interest and above median priorknowledge were affected by far more of the 11 factors than those who exhibited medianor above median prior interest, or below median prior interest and knowledge. Interest-ingly, highly interested visitors, regardless of their entering knowledge, seemed to learn.This group of visitor’s learning success seemed to be dominated by their interest levelsrather than by the other 10 independent factors and 24 independent measures used in thisstudy; although this result did not emerge when interest alone was used as an independentvariable.

In addition to interest and prior knowledge, we also attempted to segment visitors by sex(Table 8), social group type (Table 9), and race/ethnicity (Table 10). However, none of these

TABLE 9Post-Pre Comparisons of Learning Outcomes (PMM Breadth Scores) forVisitor Social Group

Type of Visitor N Percent Change

Alone 12 61.3Family group 143 38.8All adult group 34 47.1Total 189 41.7

The percent changes are not different between type of visitors for the PMM breadth scores(F = 1.24; df = 2; p = .29).

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VISITOR LEARNING FROM A SCIENCE CENTER EXHIBITION 769

TABLE 10Post-Pre Comparisons of Learning Outcomes (PMM Breadth Scores) forEthnicity

Ethnicity N Percent Change

White 93 39.0Black 29 39.5Latino 45 52.8Asian 11 34.4Other 12 34.5Total 190 41.8

The percent changes are not different between ethnic groups for the PMM breadth scores(F = .70; df = 4; p = .59).

traditional demographic variables significantly influenced the degree of observed learningin this study when learning was assessed by using the PMM breadth measures.

CONCLUSIONS

This study set out to gain a deeper understanding into the nature of museum learning. Itasked two questions:

• How do specific independent variables individually contribute to learning outcomes?• Does the Contextual Model of Learning provide a useful framework for understanding

learning from museums?

Above and beyond these two questions, though, it is important to note that visitors to theCalifornia Science Center’s World of Life exhibition did learn science. Our sample includeda very diverse range of visitors, in large part due to the fact that the California Science Centerhas one of the most socio-economically and racially/ethnically diverse visiting publics ofany large science center in the United States. The sample included visitors of all ages,incomes, occupations, levels of education, and of particular importance to this study, witha wide range of prior knowledge of biology. Our sample included individuals with only themost rudimentary knowledge of life sciences, as well as individuals with graduate degreesin biology working in life sciences careers. The vast majority of visitors surveyed in thisstudy exited the World of Life exhibition at the California Science Center with a measurablyenhanced understanding of life science on one, if not multiple measures of cognitive change.It is essential to note, though, that it took three very different types of learning measurementsto capture the learning of all these different visitors; any one measure alone would havemissed changes in some percentage of the visitors sampled. If only one measure had beenused (say correct answers on the multiple choice questions), far fewer visitors would haveexhibited signs of cognitive change. Since the measures were somewhat independent ofone another, together they were able to describe the “learning” more comprehensively. Thisresult suggests that assessment or measurement of free-choice learning needs to employ abroad set of measures rather than a focused one if researchers seek to capture the entirerange of potential cognitive change that may have occurred as a result of a museum visitor other free-choice learning activity. Also noteworthy, consistent with other studies (e.g.,Falk & Adelman, 2003), these results would suggest that science museums are particularlyuseful for facilitating science learning amongst the least knowledgeable citizens; the less

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770 FALK AND STORKSDIECK

visitors to the California Science Center knew about life science, the more they learnedfrom the World of Life exhibition.

The data support the contention made by a variety of investigators, as cited in the Intro-duction, that a range of factors, including prior knowledge, motivation, and expectations,within group social interaction, advance organizers, and exhibition design strongly affectedvisitor learning, while the other factors investigated---prior experience, prior interest, choiceand control, between group social interaction, orientation and architecture---also impactedlearning but not as strongly for this particular group of visitors at this particular exhibition.Certainly it is possible that both the effects we saw and those we did not were attributableto our abilities to appropriately measure them. In fact, validly and reliably operationalizingand measuring all of these factors remains a challenge. Still, it was possible through thisinvestigation to show that numerous factors do seem to affect learning, or more accurately,that dimensions of these factors appear to affect learning amongst a subset of visitors. Inother words, this study supports the idea that many of these factors were somehow impor-tant, but suggests that no single factor was capable of adequately explaining visitor learningoutcomes across all visitors.

The key to better teasing out individual effects of the 11 proposed factors on learning frommuseums was through meaningfully segmenting visitor groups. Traditional demographiccategories like age, gender, and race/ethnicity had limited usefulness in this respect. Priorknowledge and prior interest, however, emerged as powerful ways to divide up the popu-lation. Since we lacked the insight to create these segmented groups a priori, our ability tocreate them a posteriori diminished our analytical powers. The more narrowly we definedsubsets of visitors, the smaller our sample size. This suggests that in the future, we shouldcollect specific data for a very homogeneous subset of visitors, increase the sample size,improve our tools for discriminating visitors, or all of the above. Prior to this study, welacked the data on which to intelligently define truly homogeneous subsamples of visitors,and arguably, even with this data, we lack the tools to do this well within the constraintsof the free-choice learning environment. We are currently conducting investigations to testthese assumptions and remedy these problems.

Finally, we sought to answer the question of whether or not the Contextual Model ofLearning provides a useful framework for understanding learning from museums. We wouldargue that the results of this study appear to support the value of the Contextual Modelof Learning as an operational framework, with some caveats. The study reinforced whatmost already know that learning from museums is highly complex. The exact nature ofthe life science learning that occurred in the World of Life exhibition varied considerablybetween visitors and was shown to have been influenced by a wide range of variables.The Contextual Model of Learning provided a useful framework for beginning to unravelthe complexities of learning from a science center. As the results reported here so clearlyrevealed, depending upon who the visitor was, what they knew, why they came, and whatthey actually saw and did, the outcomes of the museum experience were dramaticallyaffected. The framework provided by this model allowed us to begin to unravel thesecomplex interactions and relationships between the visitor’s personal, sociocultural, andphysical contexts; relationships likely to be missed if we had only focused on one or theother of these contexts or their embedded variables. That said, even at best, the individualfactors within the Contextual Model of Learning allowed us to explain only a small portionof the learning that we were able to record.

Some of the changes in the learning that resulted from visitors experiences in the museumwere almost certainly a direct consequence of random events. We believe that learning inscience centers (or any other free-choice learning for that matter) does, in fact, dependupon a range of “contextual” factors. However, the underlying model of learning may more

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closely resemble a stochastic model. A stochastic model assumes that “initial states”, e.g.,prior knowledge, motivation, interest, social group, are important, but these change overtime through interactions with both predictable and unpredictable events. The collectiveinteractions, rather than just the initial state, determine the outcomes. In addition, randomevents influence not only which factors come into play as important, but also modulatethe relative amplitude of impact those factors have on learning; stochastic factors influenceboth the quality and quantity of learning that results.

Translated into the vernacular of a museum visit, we would envision a visitor entering themuseum with a likely learning trajectory; a trajectory determined by that visitor’s specificprior knowledge and motivation for the visit as well as presumably their prior experience,interest, group composition, etc. (many of these so-called independent variables may alsostrongly interact). We can presume that these factors, collectively, predispose the learnerto interact with the setting in relatively predictable ways. However, once in the setting, thevisitor is affected by a whole series of additional factors, some of which are under the controlof the institution (e.g., advance organizers, good and bad exhibits, presence or absence oforientation tools, and mediation provided by trained staff) and some of which are not (e.g.,social interactions within the visitor’s own group and interactions with other visitors outsideof his/own group). All of these factors are potentially influenced by totally random events,e.g., a crowd of visitors at an important/preferred exhibit causes the visitor to skip thatexhibit, a bright light catches the visitor’s attention, an accompanying child needs to go tothe toilet, a volunteer “randomly” selects the visitor to be part of a demonstration, a textpanel includes information the visitor just happened to have read about the previous day,etc. These events may or may not occur, and yet if and when they do, they can stronglyinfluence learning and significantly diminish the predictability of any outcome. In otherwords, the nature of the science center visitor’s learning experience depends in part onthings the science center can plan for and design, in part upon characteristics of the visitor,and partially upon random events.

However, we believe that random events only partially account for the fact that our inde-pendent measures did not correlate stronger with our learning outcome measures. Althoughwe believe the current Contextual Model of Learning to be an excellent first step in de-scribing the complexity of museum learning experiences, we are willing to believe that itis not yet a mature or complete framework. As we continue to better understand the natureof free-choice learning, we believe we will be able to continue to refine and improve uponthis framework.

We believe that studies such as the one summarized here provide the beginnings of amore conceptually based and empirical approach to understanding learning from settingslike museums. The promise of this research is that further analysis of the data collected here,combined with additional data from similar studies, will begin to yield an ever-more-refinedmodel of learning from museums. We believe that this study has demonstrated that learningfrom museum-like settings is indeed a complex phenomenon. More importantly though,we believe that this study demonstrates that learning from such settings, although complex,is subject to analysis and ultimately this analysis should lead to better practice and betterresearch. Historically, both practitioners and researchers have treated visitor populationsas homogeneous groups, influenced by the same set of variables. Investigations such asthis reinforce the importance of embracing a more complex model of the museum expe-rience; complex but still manageable. If more valid, reliable, and realistic (i.e., sensitiveto the temporal, logistical, and ethical constraints of free-choice learning settings) toolsfor segmenting visitor populations can be developed, practitioners should be better able tofacilitate learning as they will be better able to customize experiences and measurementsto the specific needs and capabilities of their learners. Similarly, researchers should be able

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to more accurately measure visitor learning as they are better able to scale their instrumentsto entering states and document salient conditions during the museum experience. Environ-ments developed to support real-world learning such as museums, are not mere backdropsfor supporting the transmission of knowledge, they are what Barab and Kirshner (2001) call“dynamical learning environments.” As such, these settings are always multidimensional,dynamic, and complex (cf. Brown, 1992; Cobb et al., 2003; Collins, 1999). Thus the realtake-away message of this article is that simple, reductionist, linear approaches to affectingand understanding learning from museums will simply not suffice. An awareness of thisreality has begun to creep into school-based learning research as well, most notably underthe banner of “design research” (cf. Brown, 1992; Cobb et al., 2003; Collins, 1999). Onlyby appreciating, and accounting for the true complexities of the museum experience willimproved facilitation and understanding of learning from museums emerge.

APPENDIX

Research Protocols

‘‘Pre’’

Living Things

1. Energy is important to organisms because .

a. it enables them to regulate their internal environmentb. living things adapt to their environmentsc. it provides the heat they need to stay warmd. it powers life processes

2. Your heart beats more quickly and you breathe more rapidly after exercising. Thischaracteristic of life is .

a. reproductionb. growth and developmentc. maintenance of homeostasisd. response to a stimulus

3. A dog barks at a mail carrier. This is an example of .

a. homeostasisb. evolutionc. an adaptationd. a response to a stimulus

4. Everybody knows a little bit about biology. On a scale of 1 to 6 (1 you know absolutelynothing about biology and 6 you’re an expert in biology), how would you rate yourknowledge about biology? [Probe: Why did they give that specific rating? Education?]

5. There are certain things that living things do. Do you know anything about thesethings? [Probe: What are these processes? Tell me more about them?]

6. Do you think there are any characteristics that are common between humans andother living things? Can you tell me more about them? [Probe: Do you think thereare any similarities between humans and other living things? Can you tell me moreabout these similarities?]

7. Is there a topic(s) in biology you find particularly interesting? What is it? On a scaleof 1 to 6 (1 being you could care less about biology or the interested topic in biology

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and 6 being you love biology so much you can’t get enough from it), how would yourate your interested topic in Biology? [Probe: Why did they give that specific rating?What area in Biology and what kinds of things?]

8. I just found out that Discovery Channel is going to show a documentary on “ChildDevelopment” in a couple of months. On a scale of 1 to 6 (1 being you don’t care howbored you are that day you still won’t watch the program and 6 being if you knewit was on you would try to watch it no matter what even if you had a busy schedulethat day), how would you rate you interest in viewing such a program? [Probe:Ask why?]

9. Do you know what the exhibit is about? [Probe: Ask how they arrived to thatconclusion?]

10. Here are a few reasons that people gave us as to why they came to the CaliforniaScience Center. I’d like you to rate on a scale from 1 (don’t agree) to 6 (highly agree)the following three statements. Please rate them on their own merit; that is, how muchdo you agree with each of those statements:

a. I came to the California Science Center to find out more about the world Ilive in.

b. I came to the California Science Center to have a good time.c. I came to the California Science Center because my family/friends wanted

to come.

11. There are some people who really want to know where they are going through themuseum and there are some people who just go along on a totally “whim” basis. Ona scale from 1 to 6 (1 = enter and go wherever your whim takes you---drifter) to 6(always uses maps and directional signs---almost compulsively), how would you rateyourself?

‘‘During’’

Tracking Guide

Time of day:

1. Researcher should observe and document if visitors are reading the sign when theyenter World of Life, which reads “Welcome to the World of Life. From apple treesto honey bees, we’re more alike than you think.”

2. Researcher should also observe and document if visitors enter the Life Tunnel.3. Observe and document if visitor uses a map and is “oriented.”4. The researcher will record the level of visitor density or “crowdedness” on a daily

basis. On a scale of 1 to 6 (1 being not at all and 6 being very crowded), howcrowded was World of Life?

5. The researcher will also record the percentage of exhibits that are functioning on adaily basis.

6. The researcher will qualitatively rate whether or not the visitor was controllingwhich exhibit(s) he/she wanted to look at and when on a scale of 1 to 6 (1 having nocontrol and 6 having total control). (e.g., Was a child or partner determining whichexhibit to visit, or was it the subject?)

Tracking [A map that featured all exhibits of the World of Life was used for tracking.Movement, time, quality of interaction with exhibit, and quality of social interaction wasrecorded]

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Visitor Tracking Protocol

Rates the number of exhibit elements visited, which exhibit elements were visited and thelevel of interaction at those exhibits.

0 = Walked right by1 = Pauses, glances around but does not really engage2 = Stops, views exhibition elements in a cursory way, engages only a little3 = Stops, views over half of the exhibition elements, appears fairly engaged andfocused4 = Stops, views nearly all of the components in the exhibition, viewing every-thing/interacting/reading text quite thoroughly as if they intend to look at the wholething

Social Interaction

CA = child–adult; CC = child–child; AA = adult–adult; SA = staff–adult; AC = staff–child

Independently deal with the quantity and quality of the interaction on a qualitative level.Record all social interactions that occur and then at the end of the day come up with a scale.Record data any time there is a social interaction. Use a scale of 1 to 6 to rate the intensityof the social interaction.

Rating scale for quality of social interaction

1 = No interaction with others2 = Interaction with others, but most unrelated to the exhibition3 = Low interaction with others---much related to the exhibition4 = Moderate interaction with others---much related to the exhibition5 = High interaction with others---most related to the exhibition6 = High interaction with others---related only to the exhibition

Interaction with Staff and Others

Independently deal with the quantity and quality of the interaction on a qualitative level.Record all social interactions that occur and then at the end of the day come up with a scale.Record data any time there is a social interaction. Use a scale of 1 to 6 to rate the intensityof the social interaction (same scale as above).

‘‘Post’’

Living Things

1. Energy is important to organisms because .

e. it enables them to regulate their internal environmentf. living things adapt to their environmentsg. it provides the heat they need to stay warmh. it powers life processes

2. Your heart beats more quickly and you breathe more rapidly after exercising. Thischaracteristic of life is .

i. reproductionj. growth and development

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k. maintenance of homeostasisl. response to a stimulus

3. A dog barks at a mail carrier. This is an example of .

m. homeostasisn. evolutiono. an adaptationp. a response to a stimulus

4. There are certain things that living things do. Do you know anything about thesethings? [Probe: What are these processes? Tell me more about them?]

5. Do you think there are any characteristics that are common between humans andother living things? Can you tell me more about them? [Probe: Do you think thereare any similarities between humans and other living things? Can you tell me moreabout these similarities?]

6. On a scale of 1–6 (1 being you did not like the building at all/get me out of here and6 being you loved the building), how did you like the building in terms of was it toocold, too hot, too many people? Was there something/anything about the environmentthat bothered you? [Probe: Find out about temperature, lighting, etc.]

7. (I was following you around, it seemed as if you knew where you were going.) Didyou have a set plan here at World of Life? Did you have a good sense as to whereyou were going? [Probe: Were there any specific exhibit elements that you wantedto see first, second, etc? Why?] Did you have a set plan here at World of Life? Didyou have a good sense as to where you were going? [Probe: Were there any specificexhibit elements that you wanted to see first, second, etc? Why?]

8. Did you feel like you were the person who got to go and see what you wanted to seehere at the World of Life or was it your child/partner/friend making those decisionsfor you. On a scale of 1 to 6 (1 being you were NOT the one who got to decide whereto go, when to go, and what to see and 6 being you were the one who got to decidewhere to go, when to go, and what to see) [Probe: Why?]

9. In the World of Life there are a lot of exhibits. On a scale of 1 to 6 (1 being youfelt that the choices here weren’t good for you and 6 being the choices of exhibitsyou had fit your needs perfectly/were good for you). [Probe: Give an example suchas “Some people say that even though I have 100 cable channels at home, none ofthe choices are good for me. Some other people say, that I have 100 different cablechannels at home and I don’t know which are the ones I want to watch, because theyare ALL so good.] [Probe: Why?]

10. Did you have any interactions with any staff member(s) (other than me)? If yes, howwould you rate that interaction on a scale of 1 to 6 (1 being the staff member was notuseful at all and 6 being the staff member was very helpful/useful).

Would you be willing to participate in a follow-up interview 3 months from now?YES NO

Name:Phone Number: Home WorkBest time to call:E-mail:

[The following information was recorded during the tracking and/or as part of thepostscript]

Date: Time of day: Data Collector:

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Visitor Code:Location of Observation / Interview: California Science Center/World of Life

Demographics [were not directly asked, but either visually assessed or gleaned frominformal “conversation” with the visitor over the course of the interview]

Gender: M FAge: 8–11 yrs 12–15 yrs 30–50 yrs [True age was estimated and recorded]Race/ethnicity: Caucasian African American Latino Asian American

OtherSocial Group: Alone Family All child/teen group All adult groupBeen to CSC before? Yes No [when?]If so, have you ever been to the World of Life Exhibition before? [when last?]If not, have you ever been to another science center before? [do you remember

some?]

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