Using the contextual model of learning to understand visitor learning ...

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 theoretical 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, orientation, advance organizers, architecture, and exhibition design affect visitor learning. All of these factors were shown to individually influence learning outcomes, but no single factor was capable of adequately explaining visitor learning outcomes across all visitors. The framework provided by the Contextual Model of Learning proved useful for understanding how complex combinations of factors influenced visitor learning. These effects were clearerest when visitors were segmented by entry conditions such as prior knowledge and C 2005 Wiley Periodicals, Inc. Sci Ed 89:744 – 778, 2005 interest.

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

2005 Wiley Periodicals, Inc.

VISITOR LEARNING FROM A SCIENCE CENTER EXHIBITION

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INTRODUCTION Few activities will be more important to 21st century free-choice science education institutions in general and science museums1 in particular than meaningfully understanding the learning they facilitate. Whereas only a few years ago it could be fairly stated that it was 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 document the range of learning that museums afford (cf. Falk, 1999; Leinhardt, Crowley & Knutson, 2002; Rennie & McClafferty, 1996). However, a full understanding of the complexities of the processes of learning that occurs during a visit to a free-choice setting remains elusive. Historically, much of the research on learning in museums was a-theoretical. This is changing; currently a variety of theoretical frameworks have been proposed for understanding 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 for organizing the complexities of learning within free-choice settings.” The Contextual Model of Learning is not a model in its truest sense; it does not purport to make predictions other than 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 learning embodied in this framework is that learning can be conceptualized as a contextually driven effort to make meaning in order to survive and prosper within the world; an effort that is best viewed as a continuous, never-ending dialogue between the individual and his or her physical and sociocultural environment. The Contextual Model of Learning portrays this contextually driven dialogue as the process/product of the interactions between an individual’s (hypothetical) personal, sociocultural, and physical contexts over time. None of these three contexts are ever stable or constant; all are changing across the lifetime of the individual. As the museum examples described below help to clarify, the Contextual Model of Learning draws from constructivist, cognitive, as well as sociocultural theories of learning. The key feature of this framework is the emphasis on context; a framework for thinking 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 individual carries with him/her into a learning situation. Building upon constructivist theories of learning, the influences of prior knowledge and experience on museum learning have been widely 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 of a visitor’s motivations, or “agenda”, for visiting a museum has also been shown to significantly influence the visitor’s learning outcomes (e.g., Falk, 1983; Falk, Moussouri, & Coulson, 1998; Graburn, 1977; Hood, 1983). More recently, it has been appreciated that the degree of choice and control over learning also affects visitor learning (e.g., Griffin, 1 In this paper we use the term “museum” to generically refer to museum-like institutions including science 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 expect new 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, culturally defined experience. One should expect learning to be highly personal and strongly influenced by an individual’s past knowledge, interests and beliefs. One should expect learning to be influenced by an individual’s desire to both select and control his/her own learning. Humans are extremely social creatures. We are all products of our culture and social relationships (Ogbu, 1995; Wertsch, 1985). Hence, one should expect museum learning to always be socioculturally situated. Factors affecting learning have been hypothesized to include 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, empirical evidence for these impacts are difficult to find. However, considerable research now exists which shows that visitors to museums are strongly influenced by the interactions and collaborations they have with individuals within their own social group (Borun et al., 1997; Crowley & Callanan, 1998; Ellenbogen, 2002; Schaubel et al., 1996). Research has also shown that the quality of interactions with others outside the visitor’s own social group, for example museum explainers, guides, demonstrators, performers or even other visitor groups, 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 a dialogue with that physical environment. Thus, one should expect visitors to museums to react to the physical context of the museum itself; which includes both the large-scale properties of space, lighting, and climate as well as the smaller scale aspects such as the exhibitions and objects contained within. Since museums are typically free-choice learning settings, the experience is generally voluntary, nonsequential, and highly reactive to what the setting affords (Falk & Dierking, 2000). As such, visitor learning has been shown to be strongly 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 complex three-dimensional environment turns out to be highly correlated with what and how much an individual learns. Similarly, intellectual navigation, as supported by quality advance organizers (Anderson & Lucas, 1997; Falk, 1997), has been shown to affect visitor learning from museums. Research has also shown that a myriad of architectural design factors such as lighting, crowding, color, sound, and space subtly influence visitor learning (Coe, 1985: Evans, 1995; Hedge, 1995; Ogden, Lindburg & Maple, 1993). Considerable research has focused on the exhibitions and labels themselves since they are designed to be the primary teaching tool within museums. Not surprisingly then, ample evidence exists that exhibition design features influence learning, in particular the sequencing, positioning, and content of exhibitions and labels (Bitgood & Patterson, 1995; Falk, 1993; Serrell, 1996), as well as how many exhibit elements a visitor attends to, and for how long (Bitgood, Serrell, & Thompson, 1994; Serrell, 1998). Finally, less well documented, but theoretically compelling is the expectation that learning from museums will not only rely on the confirmation and enrichment of previously known intellectual constructs but will equally depend upon what happens subsequently in the learner’s environment since learning is not an instantaneous phenomenon, but rather a cumulative process of acquisition and consolidation (Anderson, 1999; Bransford, Brown, & Cocking, 1999; Medved, 1998). Thus, experiences occurring after the visit frequently play an important role in determining, in the long term, what is actually “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 to organize information on learning. Inside the framework hang the details. These details are myriad. The total number of factors that directly and indirectly influence learning from museums probably number in the hundreds, if not thousands. Some of these factors are 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 be important. After considering the findings from hundreds of research studies including the ones cited above, 12 key factors, or more accurately suites of factors, emerged as influential for museum learning experiences. These 12 factors are Personal context 1. 2. 3. 4. 5.

Visit motivation and expectations Prior knowledge Prior experiences Prior interests Choice and control

Sociocultural context 6. Within group social mediation 7. Mediation by others outside the immediate social group Physical context 8. 9. 10. 11. 12.

Advance organizers Orientation to the physical space Architecture and large-scale environment Design and exposure to exhibits and programs 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 particular visitors and venues (e.g., science centers, natural history museums, zoos, planetaria, nature center, etc.). While there exists evidence that each of these factors influences learning, we do 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 these factors being THE critical variable influencing learning from museums. Arguably, all are important, but are one or two of these factors more important than the others, particularly when they are not studied in isolation since little is known about the combined effect of these variables or the relative significance and importance of each factor when measured simultaneously? Or, alternatively, do none of these factors, individually, satisfactorily explain visitor science learning from a science exhibition as would be hypothesized by the Contextual 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 two questions:

• •

How do specific independent variables individually contribute to learning outcomes when 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 factors described above; each representing suites of variables assumed to effect learning from museums. We were interested in determining which of the factors, when directly compared with one another, was important and for which type of visitor. Another, closely related question---How do collections of independent variables contribute to learning outcomes?--will be addressed elsewhere (Storksdieck & Falk, in preparation). The 12th factor, the role of 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 and behavioral measures obtained through unobtrusive tracking of all respondents throughout the 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 California Science Center, Los Angeles. This large, permanent exhibition was designed to communicate the overall message that all life, whether composed of a single cell or many specialized cells, must perform certain life processes to survive. The five basic life processes described in the exhibition are living things all: (1) take in energy, (2) take in supplies and get rid of wastes, (3) react to the world around them, (4) defend themselves, and (5) reproduce

TABLE 1 Summary of Repeated Measures Element of Study Mean duration Measures

Entry Interview 17 min • Personal meaning mapping • Open-ended, focused questions • Multiple-choice questions • Self-report items

Tracking 47 min • Unobtrusive observation (tracking) • Running commentary

Exit Interview 16 min • Personal meaning mapping • Open-ended, focussed questions • Multiple-choice questions • Self-report items


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and pass on genetic information to their offspring (Combs, 2001). In addition to exhibits that 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 convey the importance of keeping a metabolic balance (homeostasis) under varying external conditions, and to communicate the idea that organs in our body work together to maintain homeostasis. Both front-end and formative evaluations were completed during the exhibition’s development, and in 1998 a complete summative evaluation was conducted to determine how successfully these messages were conveyed (Falk & Amin, 1998). For the summative evaluation, 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 understanding of life processes and the relationship of humans to other life forms, both prior and subsequent to their visit to the World of Life. The results revealed a significant change in public understanding of the overarching message and conceptual change in understanding relative to four out of the five life process areas. We determined that this exhibition would lend itself well to an investigation of science center learning because it is a popular, well-liked exhibition, combining a mix of media and presentation styles, which demonstrably facilitates significant short-term learning. Equally important, the results of the earlier evaluations showed that visitors to the exhibition evidenced a range of learning outcomes which would afford the variability necessary to test the assumptions of the study. Sample Between December 2000 and April 2001, a nonbiased sample of 217 adults visiting the science center alone or as part of a family group participated in the study. The study involved 7 dependent (learning measures) and 63 independent measures (including multiple strategies for measuring aspects of each of the 11 of the 12 factors described above, plus 12 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 population of the California Science Center (see Table 2). TABLE 2 Comparison Between This Sample and CSC Visitors

Sexb Female Male Race/ethnicityc Caucasian Latino African American Asian/Pacific Islander Other a

Total N was not available. Chi-square = .53; p = .47. c Chi-square = 1.86; p = .76. b

CSC Visitor Statistics (1998)a (%)

This Study (n = 190) (%)

60 40

52.9 47.1

48 21 16 10 5

48.9 23.7 15.3 5.8 6.3

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Methodology The basic research design included face-to-face interviews before and after the gallery visit and unobtrusive tracking of visitors during the visit. The method for selecting visitors was designed to be unbiased and broadly representative of the typical visiting public of the CSC. An imaginary line was drawn in the hallway leading to the 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; organized groups such as school or summer-camp groups were excluded. Upon approaching the group, the investigator randomly selected one adult in the group and invited that individual to participate in an investigation to “help us learn more about our visitors.” If they agreed to participate, the full procedure was implemented, if they refused, the method was repeated again. Eighty-one percent of visitors asked to participate agreed to take part in the investigation. Upon receiving permission, the researcher carried out a previsit interview, then requested the individual to consider participating in a second interview following the visit, and informed the individual that s/he would be followed through the exhibition, with the primary purpose of noting the time of exit. The entry interview consisted of three parts. Using a method called personal meaning mapping (PMM), a derivation of concept mapping, respondents were first asked to brainstorm about the term “living things”; answers were used to probe deeper into the person’s understanding (see below for more details). After the PMM, respondents were asked two related, open-ended questions about processes that all living things have in common amongst themselves and with humans. Subsequently, respondents were asked to choose the best of four answer options to three multiple-choice questions on basic life processes. Subsequent rating 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 visiting the science center (3 items), had respondents rate their museum visiting strategy (from randomly perusing to highly organized selecting), and asked respondents to describe their previous experience with the setting (1 item). Tracking individuals within the World of Life involved following them closely enough to observe their social interactions and gauge their level of interaction with specific exhibit elements. Given the popularity, size, and spatial complexity of the exhibition, it was possible to remain sufficiently distant from the visitor to be almost totally unobtrusive. In addition to noting the visitor’s engagement with each exhibit element in the WoL, and marking for each exhibit the type of social interaction, the observer also documented the visitor’s apparent control over his/her visit (e.g., were they determining what to look at or were others in the group such as children driving the visit), the overall degree of social interaction within the social group and with staff and other visitors outside their group, the degree to which the visitor 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 and noted additional information about the visit. When a subject exited the World of Life, the researcher again approached him/her and asked to conduct a postvisit interview. The refusal rate was around 12%. Visitors refused exit interviews mostly because they were late for an IMAX show for which they had purchased tickets or their parking meters were running out. The exit interview repeated the knowledge portion of the entry interview (PMM, open-ended, and multiple-choice questions). Additionally, respondents were asked to rate on a scale from 1 to 6 the architecture and interior space of the building, their perceived level of control over the visit, the quality of choices presented by the exhibition, and the quality of interaction with staff members. Individuals who agreed to participate in a postvisit interview were asked to provide contact information

Overall Strategy.


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for a follow-up interview at a later date (refusal rate = 15%). Visitors who asked about the true nature of the study were told and were so noted; others were not informed, so as to avoid 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 be changes in an individual’s understanding of life science. We were interested in determining whether individuals acquired new or enhanced understanding of facts and/or concepts about biology 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 of learning outcomes; this particular study only focused on one kind of learning---changes in an individual’s knowledge of life science---and obviously, only those changes measurable within the constraints of a free-choice learning setting where individuals are only willing to provide a limited amount of time and attention to the assessment process. We did not specifically distinguish between various forms of knowledge and understanding (awareness, recall of latent knowledge, acquisition of new factual knowledge, conceptual change) for our learning measures; the measures provided opportunity for visitors to demonstrate change in any of these forms. Our technique for capturing these cognitive changes was based upon a series of repeated measures using three very different learning assessment tools. The nearly identical pre- and post-visit interviews each involved asking visitors to share their knowledge of life science by responding to (1) three multiple-choice questions, (2) two open-ended questions, and (3) personal meaning mapping. These were administered through both paper and pencil instruments and semistructured interviews and incorporated both traditional, positivist approaches to measuring knowledge and conceptual change (change in the number of correct answers) and more constructivist measures that allowed visitors to define or choose the topics of conversation while researchers would still assign quantitative measures for the quality or correctness of the visitors’ answers, like the breadth, depth, or mastery with which visitors were able to express themselves in their chosen area of life science. The multiple-choice questions provided a general measure of visitor understanding of basic 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 selected from a widely used high school biology textbook entitled Biology: The Dynamics of Life (Biggs et al, 1998), a text endorsed by the National Science Teacher Association in their 2000 Instructional Resources Catalogue. During a pilot study at the California Science Center, 20 visitors answered the multiple-choice questions both before and after their visit. The three questions which revealed the greatest percent change from incorrect to correct responses were included on the final instrument (see Appendix). One question tested respondents’ understanding of the need for energy to drive life processes, the second question tested respondents’ awareness of the principle of homeostasis, and the third question tested whether respondents could identify the need for all organisms to respond to outside stimuli. The open-ended questions intended to measure whether visitors comprehended the overall message of World of Life. The first question stated, “There are certain things that living things do. Do you know anything about these things?” The question was generally modulated by a subsequent clarifying question “What is it that all living things have in common?” The second question asked, “Do you think there are any characteristics that are common between humans and other living things?” Again, the original question was usually reinforced by a 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 each given answer. The researcher wrote down verbatim the visitor’s responses. Answers to both questions were pooled and thus treated as if they were answers to one question: “What is it that all livings things, including humans do/have in common?” The question basically probed for visitors’ understanding of basic life processes like reproduction, metabolism, defense, response to stimuli, etc., though visitors expanded the range of possible answers into ecological, evolutionary, and spiritual dimensions. Visitor responses were coded in two dimensions---the number of conceptual categories with which visitors were able to map the scope of the question and the depth with which they were able to provide explanations within the conceptual categories they provided. Coding categories emerged from data and were refined 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 responses defined as the number of conceptual categories (out of a total of 21 nonoverlapping categories generated 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 provided within the conceptual categories a respondent provided to the two questions. Inter-rater reliability for the breadth score of the open-ended questions was r = .94, and r = .83 for the depth score. Personal meaning mapping (PMM) is a relatively new instrument similar to concept mapping (cf. Falk et al., 1998; Falk, 2003) designed to assess changes in an individual’s conceptualization of a topic over time. Like the open-ended questions, PMM allowed respondents to describe their knowledge of living things, with the researcher probing for more detail following each item. The approach involves asking individuals to write down on a blank 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 cueing word or phrase. In this study, the cueing phrase displayed at the center of the page was living things. The words, ideas, images, phrases, or thoughts written down in response to the initial cue form the basis for an open-ended interview. Individuals were encouraged to explain why they wrote down what they did, e.g., “You wrote down animals. Tell me, what do animals have to do with living things?” and to expand on their thoughts or ideas relative to the cueing phrase. The goal was to “unpack” the individual’s conceptual framework for the idea(s) represented in the cueing phrase, using their own words as prompts. The individual’s responses were recorded verbatim on the same piece of paper by the researcher. To permit discrimination between unprompted and prompted responses, the follow-up interview data was recorded in a different color ink than were the initial words, phrases, etc. recorded by the individual themselves. Following the educational intervention---in this study, the World of Life exhibition---individuals were asked to review their previous PMM and invited to add, delete, modify, or change their responses. These changes were noted in a third color ink. Finally, these additional comments or thoughts formed the basis of a second openended follow-up interview. The results of this interview were recorded in a fourth color ink. Personal meaning maps are designed to measure change in an individual’s conceptualization of the prompt along four dimensions---extent, breadth, depth, and mastery. Extent refers to changes in the number of appropriate words the subject used to describe the prompt since one measure of increased understanding is an increase in the vocabulary an individual has available for describing a concept or phenomenon. (Note: Since PMM measures each individual’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 concept or 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 concept; extent is designed to capture this dimension of learning. Breadth measures changes in 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 understood in more than one way. For instance, before entering the World of Life exhibition a visitor in response to the prompt living things might have talked about spiritual and ecological aspects of life; after their visit they might have now included aspects of physiology and genetics. As with the breadth score for the open-ended question, coding categories emerged from data and were refined in an iterative process until two independent researchers agreed on them (Mayring, 1989). Depth measures the changes in degree of understanding within each breadth category and is therefore a measure of conceptual understanding. Increased depth 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 of a specific conceptual category. Finally, the fourth dimension is mastery, a holistic assessment which measures the changes in individual’s overall understanding. Mastery can thus be 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 to gauge where an individual falls along a continuum between novice and expert relative to the specific concept or phenomenon represented by the prompt. The four constructs extent, breadth, depth, and mastery were designed to be independent and complementary measures of learning, capturing different aspects of cognitive gain in a free-choice learning environment. Inter-rater reliability was established for each of the four PMM measures and found to be high for extent (r = .96) and for breadth (r = .88), satisfactory for depth (r = .76) and mastery (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-ended question (breadth and depth), and one measure derived from the three multiple-choice questions. It was assumed that each of the three methods we used was biased; each excluded some important aspects of science learning. For example, we know that since we interviewed visitors as individuals at the beginning and end of their experience we lost the opportunity to tap into the distributed learning that was likely present during the learning process. We also know that much of what was likely learned had not yet been consolidated into memory and was thus unavailable to the visitors; this we attempted to capture through subsequent follow-up investigations, the results of which will be reported elsewhere. We also know that there was a possible cueing bias because of our repeated measures approach. Although a previous investigation in a comparable setting using many of the same methods, including such highly intrusive methods as interviews and personal meaning mapping, found no evidence that pre-experience interventions significantly enhanced learning amongst museum visitors (Adelman et al, 2001), we cannot rule out the possibility that such an impact occurred. In this study, we did not include a control group, but we did develop a variable called “interviewer effect” to try and assess our impact. This variable was created by scaling the time the researcher spent talking with a visitor prior to their exhibition experience; presumably, the longer the intervention, the greater the likelihood of impact. There was no significant overall correlation for this variable with changes in visitor science learning on any of our seven learning measures. There was no evidence (statistical or anecdotal) that these research interventions significantly impacted the science learning of the majority of visitors. As was argued by us previously (Adelman et al., 2001), visitors to free-choice learning settings are there for a myriad of reasons, but pleasing researchers is not one of them. In both school and laboratory settings, research subjects have good reason to believe that they will be rewarded for doing “well,” “well” as defined by the

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experimenter. Free-choice learning settings appear to be very different research contexts than either schools or laboratories. In the free-choice learning context, doing “well” generally means satisfying intrinsic rather than extrinsic agendas. This is, in part, why one in five visitors refused to participate in this investigation and 15% of those who initially agreed to 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 these very different ways of capturing changes in visitor life science knowledge provided us with measures of short-term science learning sufficiently robust to answer the questions we set out to investigate. Independent Variables. Independent variables create an impact on outcome or dependent variables. For example, the main independent variable in this study was the educational intervention---the visitor’s experience in the World of Life exhibition. However, as described by Falk and Dierking’s (2000) Contextual Model of Learning, the visitor’s experience is actually a complex of independent variables (factors) such as design, setting, advance organizers, orientation, and subsequent reinforcing experiences. Within the Contextual Model of 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, prior interest, visit motivations and expectations, and choice and control, and sociocultural contextual variables such as interactions within one’s own social group and interactions with individuals outside one’s own social group. From the elements in the model, hypotheses were 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 interview guide (Foddy, 1993; Kvale, 1996; Stangor, 1998). As described earlier, each of the major factors under investigation was, in itself, a multidimensional construct. Therefore, it was not possible to fully measure factors such as “interest” or “motivation and expectations.” What we did attempt to measure were dimensions of these constructs; dimensions we believed to be significant components of these factors. Since most of these factors had been previously investigated in comparable settings, 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 as gender, race/ethnicity, time of day, day of week, etc. Our goal was to have three semiindependent ways for measuring each of the 11 factors described above; if we could not find 3 existing measures, we created additional measures. All measures were developed in consultation with a Research Methodology Committee and pilot tested on 20 science center visitors. Summaries of each measure are briefly described below and are summarized in Table 3. Visit Motivations and Expectations. We first asked each individual to describe, without cueing, their reasons for coming to the science center that day. Previous research (cf. Falk et 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 to 6 how important to them each of these three reasons were for them on this particular trip to the 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 knowledge of life science as part our dependent measures (i.e., multiple-choice questions, openended questions, and PMM). In addition, each visitor was asked to self-rate their knowledge of biology and provide an explanation for that rating.


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TABLE 3 Independent Variables (Factors Hypothesized to Influence Learning) Variable Suite 1. Motivation and expectation

2. Prior knowledge

Measure • • • •

4. Prior interest

• • • •

5. Choice and control

•

3. Prior experiences

• 6. Within group social mediation

• •

7. Facilitated mediation by others

• •

8. Advance organizers

• •

9. Orientation to the physical space • • 10. Physical environment • 11. Design of exhibits • (quality and exposure) • • • •

Interviewer effect

•

Intent to learn about the world Intent to have a good time Intent to entertain family/friends Visitor self-rating of biology knowledge Measured knowledge (pre score) Visited CSC before Visited World of Life before Combined prior interest scales (interest in biology and in watching a documentary on child development) Researcher rating of visitors’ ability to choose exhibits Visitor self-rating of ability to choose exhibits Researcher’s rating of intensity of internal social interaction Number of adult social interactions (adult/adult, adult/child) Researcher’s rating intensity of external social interaction Visitors’ rating of staff interaction usefulness Total engagement with advance organizer exhibits Movement pattern through clustered exhibits Researcher judgment of orientation Visitor judgment of orientation Degree of crowding Quality: average engagement score for top 10% exhibits Quality: average engagement score for top 24% exhibits Exposure: Total length of stay in World of Life Exposure: Hit rate (Percent of total exhibits visited) Exposure: Overall average intensity (average engagement score for all exhibits) Length of entry interview

Scale 1–6 1–6 1–6 1–6 Various Yes/No Yes/No 2 – 12

1–6 1–6 1–6 0 – 39 1–6 1–6 0 – 12 1–4 1–6 1–6 1–6 0–4 0–4 10 – 122 min 0 – 100% 0–4

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);

756


3. The approximate number of years since their last visit to the California Science Center/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 selfreport items on a scale from 1 – 6, and two open-ended questions that provided background information on the rating scales: 1. Ratings of interest in biology as a general subject and reasons for why they gave the rating; 2. Description of specific topics in biology considered interesting to them and why; and 3. 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 create a more broadly based interest score. Choice and Control. Visitors’ perceived choice and control was measured both by selfreport and by external observation and rating. During the postinterview, visitors were asked to rate on a scale from 1 – 6 the degree to which they perceived that they, rather than someone else in their social group were in control of the visit. Visitors also self-rated on a scale from 1 –6 the degree of choice they experienced while in the gallery and were asked to provide justifications for their ratings. (This variable was later judged by us to indicate visitors’ overall satisfaction with the exhibition, rather than their perception of the degree with which they 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 subjects appeared to be making their own exhibit choices and be in control of their visit experience. This rating was largely based on whether the individuals appeared to be controlling their own movements or appeared to be manipulated by other members of their visiting group. Social Interaction. By virtue of being a destination for social outings, the science center naturally facilitates learning via social interactions, which can be divided into two categories: (a) interactions amongst members of one’s own social group, and (b) external social interactions---interactions with staff and others outside of one’s own social group. We did not employ techniques that would have allowed us to directly and thus reliably monitor actual visitor conversations, so all ratings were based on second-hand sources---either observed or self-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 interview, visitors were asked to self-report the extent and nature of their social interactions and describe the outcomes of those interactions. The social interaction rating scales ranged from 1 (no social interaction) to 6 (high social interaction) for the overall degree of social interaction within the group and with persons outside the immediate social group, as rated by the researcher. Respondents were asked to rate the usefulness of external social interactions on a scale from 1 (the staff member was not useful at all) to 6 (the staff member was very helpful/useful). Finally we used the number of social interactions (adult – adult and child – adult) in the exhibition as observational data for the degree of within-group social interaction. Advance Organizers. An advance organizer is anything that provides “intellectual navigation” for a subsequent learning experience. In the World of Life, four separate exhibit elements could conceivably act as advance organizers: (1) the overview sign at the gallery


757

entrance, (2) the Life Tunnel introductory panel, (3) the Life Tunnel itself, and (4) the Cell Theater video. The researcher recorded whether a visitor interacted with one or more of these exhibit elements and recorded an engagement score for each interaction (0 – 4, from no engagement to full interaction). Engagement scores for the Life Tunnel panel, the Life Tunnel itself, and the cell theater video were added for a potential maximum score of 12 on the advance organizer measure. Engagement scores for the overview sign at the entrance of the exhibition were not included as it was determined that they could not be assigned reliably. In addition, each of the five major conceptual themes of the exhibition was clustered into a discreet area; each area contained an introductory exhibit and then a group of similarly themed exhibit components in close proximity. Theoretically, visitors who viewed the exhibits systematically within a cluster, starting with the introductory exhibit, acquired an advance organizer for this theme. Visitors were given a score of 1 to 4 for this use of the exhibition 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 strategically 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 observed and 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 were asked to indicate whether they felt oriented during their stay in the World of Life exhibition. Physical Environment (Architecture and Large-Scale Environment). Visitors were asked during 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 was generally 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. The researcher recorded, for each visitor, the degree of crowding in the exhibition on a scale from 1 (not at all crowded) to 6 (very crowded). Exhibit Design. We attempted to measure two important dimensions of the exhibit design factor. The first relates to the extent of use of exhibits (what has often been referred to in the museum literature as “holding” and “attracting” power (Screven, 1974); we call this exhibit exposure). The second important dimension relates to the actual quality of the exhibit; Does the exhibit actually afford learning? For example, is the interface user-friendly, are the ideas presented 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 the overall message the exhibit designers and curators were interested in conveying? All of our measures were researcher coded, they were A. 1. 2. 3. B. 4. 5.

Exhibit exposure Total length of stay in the gallery (time); Percent of total exhibit elements visited (hit rate or coverage); Average intensity of engagement with exhibit elements visited (intensity); Exhibit quality Engagement scores with the top 10% exhibits; Engagement scores with the top 24% of exhibits.

758


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

• • • •

How does the core message of the exhibit contribute to the overall message of the WoL? How does the core message of the exhibit contribute to the submessage of its topic area? 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 the top 10% and top 24% of exhibits. Data Analysis. Data were transcribed from the original interview guides, tabulated in

word-processing spreadsheet programs, scored in a variety of ways, and analyzed using appropriate parametric and nonparametric statistics, including analysis of variance, Student’s t-tests, chi-squares (contingency table analysis), regression analyses (Pearson product moment correlation coefficient and its nonparametric equivalent, the Spearman rank correlation coefficient), stepwise multiple regression, and factor analysis (principal component analysis). Statistical analyses were conducted using SPSS 10.0 and 12.0 for Windows statistical software package. RESULTS AND DISCUSSION Science Learning The first result of the investigation was that, overall, visitors to the World of Life exhibition did, indeed, show evidence of positive improvement in their science learning, independently of how we measured it. In fact, there was a significant improvement, on average, shown by each of the seven different measures used for assessing change (see Table 4). Relative percent changes pre to post ranged from about 5% for the PMM depth score to more than 70% for the PMM extent measure. Depending on the measure for cognitive change that was employed, between 33% and 91% of visitors surveyed in this study exited the World of Life exhibition with a measurably enhanced understanding of science. Only 1 in 3 visitors improved their multiple-choice scores, roughly half of visitors showed improvement on the open-ended questions, and large majorities of visitors demonstrated improvement across the various PMM measures. A factor analysis tested the relationship of the seven dependent learning outcome measures (Table 5). The rotated component matrix suggests that the seven measures form at least four semi-independent factors or variables: the multiple-choice questions, the open-ended questions 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.


759

TABLE 4 Pre-Post Comparisons of Learning Outcomes

Measure of Learning Sum of three multiplechoice questionsa Breadth score for openended questionsb Depth score for openended questionsc Extent score for PMMd Breadth score for PMMe Depth score for PMMf Mastery score for PMMg

Percent Respondents Mean Mean Change with Increased Prevalue Postvalue Difference Pre to Post Scores 2.05

2.38

−.332

16.1%∗

33.2

4.94

7.09

−2.150

43.5%∗

90.5

1.34

1.53

−.199

14.2%∗

71.7

5.98 4.25 1.62 2.00

10.21 5.64 1.71 2.23

−4.230 −1.390 −.085 −.231

70.7%∗ 32.7%∗ 5.2%∗ 11.6%∗

85.9 76.4 53.4 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 lowered to p < .007. T-test results: a t = −5.27; df = 189; p = .000. b t = −21.89; df = 190; p = .000; d t = −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 seven measures. Each of the seven methods for assessing short-term learning gains appeared to measure a somewhat different dimension of learning. There was no evidence that any of the three approaches to measure learning, by themselves, totally captured the change in science understanding of visitors. We thus proceeded to keep the seven measures separate and conduct seven separate initial correlation analyses between learning measures and factors that may influence learning in and from museums. Factors Influencing Learning A total of 24 independent measures, representing the 11 variables, were correlated using Spearman’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 for learning, 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 significantly influenced learning. Neither did time of day or day of week. Also showing no significant effects were the size and nature of the visitor’s social group. All 11 factors or independent variables emerged as having a significant, though often small, correlation with change in science learning on at least some of our seven learning measures (Table 6). The most important independent variable was visitor’s prior knowledge. Prescores correlated negatively and moderately with learning. In other words, the more a visitor knew about life science when they entered the exhibit, the less they tended to gain cognitively from the visit, at least in the short term. Conversely, individuals with the least prior knowledge showed the greatest gains. This was true across all seven measures of

.10 .56 .15 .53 .37 .52 .78 1.63 23.3%

0 −.20 0 .37 .81 −.67 0 1.29 18.5%

Factor 2 −.43 .54 .78 −.20 0 −.29 −.14 1.24 17.6%

Factor 3 .88 0 .34 −.25 0 0 0 .97 13.9%

Factor 4 0 .34 −.18 .21 −.28 .89 .62 1.44 20.6%

Factor 1 0 .10 0 .68 .83 −.13 .48 1.42 20.2%

Factor 2

0 .72 .84 0 0 0 .15 1.26 18.0%

Factor 3

Rotated Component Matrix: Factors and Their Loadings#

.98 −.11 0 −.10 .13 0 0 1.01 14.4%

Factor 4

.97 .65 .75 .52 .80 .81 .63

FE∗

Rotated component matrix, rotation converged after five iterations; extraction method: principal component analysis with Kaiser normalization; varimax rotation, four factors were forced after the extraction rule eigenvalue >1.0 resulted in the deletion of a factor with an eigenvalue of .97 (which represented the multiple choice questions). ∗ FE = Final estimate/communality. Total matrix sampling adequacy (Kaiser – Meyer – Olkin measure) = .45.

#

Sum of three multiple-choice questions Breadth score for open-ended questions Depth score for open-ended questions Extent score for PMM Breadth score for PMM Depth score for PMM Mastery score for PMM Eigenvalues Variance explained

Factor 1

Original Component Matrix: Factors and Their Loadings#

TABLE 5 Factor Analysis of Seven Learning Outcome Measures (Variables)

760 FALK AND STORKSDIECK

1. Motivation and expectation Intent to learn about the world Intent to have a good time Intent to entertain family/friends 2. Prior knowledge Visitor self-rating of biology knowledge Measured knowledge (prescore) 3. Prior experiencesa Visited CSC before Visited World of Life before 4. Prior interest Combined prior interest scales (interest in biology and in watching a documentary on child development) 5. Choice and control Researcher rating of visitors’ ability to choose exhibits Visitor self-rating of ability to choose exhibits 6. Within group social mediation Researcher’s rating of intensity of internal social interaction Number of adult social interactions (adult/ adult, adult/ child) .084 −.597***

−.032 −.453*** −.199*** −.228***

.052 −.578*** −.092 −.115

.032 .062

.123† −.011 −.009 .139†

.010 −.002 −.020 −.055

.063 .154*

.051

−.095

.173*

−.016 −.023

.048 .089 −.048

.008 −.048 −.110

.092 −.026 −.004

−.055 .060

.087 .039

−.022 −.125† .015 .111

.006

−.049 −.064

−.009 −.394***

.037 −.119† .038

−.002

.004 .008

−.033 −.197**

.052 .011 .126†

−.027 .123†

.005 .008

−.024

−.153** −.157**

−.149* −.398***

−.005 −.006 −.046

Continued

−.008 .126†

−.034 .050

.028

−.116 −.120

−.173* −.364***

.039 −.180* .009

Change in Change in Change in Change in Change in Change in Change in Multiple Breadth Depth Extent Breadth Depth Mastery Factors Hypothesized to Influence Learning (Independent Variables) Choice (OE) (OE) (PMM) (PMM) (PMM) (PMM)

Learning Measures (Dependent Variables)

TABLE 6 Simple Correlation Analysis (Spearman Rho) Between Factors Hypothesized to Influence Learning From Museums and Various Measures of Learning

VISITOR LEARNING FROM A SCIENCE CENTER EXHIBITION 761

Learning Measures (Dependent Variables)

.212** .204** .165* .157* .191** .103

.192** .036

−.080

−.060 .142* .166* .047 .213**

.072 −.036

−.170* .011

.032 .006

.151* .088

.117† .148* .095 .174*

.037

.115 −.099

.219** −.090

.245*** −.091

.026 −.190†

.144† −.020

.060 .045

.190** .006

.186** .193** .122† .181*

−.066

.026 .125

.160* −.072

−.015 −.039

.078 −.0904

.086 .053 .032 .086

.191**

.075 .042

.067 −.024

−.085 .008

.125† .117

.146* .099 .164* .135†

.174*

−.054 .175*

.136† −.098

−.031 .072

reported are eta values (correlation between nominal and interval data) since the dependent variable was dualistic (yes/no). The direction (±) was determined by the Pearson R.Significance levels: † p < .1. * p < .05. ** p < .01. *** p < .001.

a

7. Facilitated mediation by others Researcher’s rating intensity of external social interaction .024 Visitors’ rating of staff interaction usefulness −.093 8. Advance organizers Total engagement with advance organizer exhibits −.122† Movement pattern through clustered exhibits .212** 9. Orientation to the physical space Researcher judgment of orientation .125 Visitor judgment of orientation −.144 10. Architecture and large-scale environment Degree of crowding −.016 11. Design of exhibits (quality and exposure) Quality: average engagement score for top 10% exhibits −.063 Quality: average engagement score for top 24% exhibits −.083 Exposure: Total length of stay in World of Life .095 Exposure: Hit rate (Percent of total exhibits visited) −.056 Exposure: Overall average intensity (average engagement score for all exhibits) −.082 Interviewer effect (length of entry interview) .079

Change in Change in Change in Change in Change in Change in Change in Multiple Breadth Depth Extent Breadth Depth Mastery Factors Hypothesized to Influence Learning (Independent Variables) Choice (OE) (OE) (PMM) (PMM) (PMM) (PMM)

TABLE 6 (Continued)



763

learning, though two measures (the depth score for the open-ended questions with rho = −.597, p < .001 and the change in correct answers to the three multiple-choice questions with rho = −.578, p < .001) exhibited a fairly strong inverse association between prior knowledge and knowledge gain, and one (extent with rho = −.197 and p < .01) exhibited a weak negative correlation. The other four measures of learning correlated moderately with their prescores (between rho = −.364 and rho = −.453, p < .001). Few other independent variables exhibited significant correlation coefficients across the various measures for learning, and all of the ones that did were related to exhibit quality or exposure---total length of stay in the WoL (rhomax = .165, p < .05), the percent of total exhibits 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 factors most closely associated with learning outcomes in museums (exposure to and quality of 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-choice questions and the PMM depth score. Thus, while most of the measures that were hypothesized to influence learning from museums affected at least some measures of learning, none did so to any considerable degree. The number of adult social interactions, adult – adult and adult – child, significantly correlated with four measures of visitor science learning (maximum rhomax = .154, p < .05). Two of the advance organizer measures resulted in significant correlations; total number of advance organizer exhibits engaged with correlated with five learning measures (rhomax < .245, p < .001) and tendency to systematically utilize the exhibit clusters as clusters correlated significantly only with changes in correct answers to the three multiple-choice questions (rho = .212, p < .01). Astonishingly, prior experiences with the World of Life exhibition resulted in slightly lower cognitive gain (r = −.228, p < .001) with the breadth measure 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 most important finding is that no variable, including the eleven we focused on, significantly influenced all measures of science learning used in this study for the entire sample of respondents. Each of the 24 measures and 11 variables significantly influenced only a subset 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 least by the various independent measures; three independent measures, representing two factors, prior interest and advance organizers, correlated significantly with changes in correct answers to the multiple-choice questions. In contrast, 12 measures, representing 8 independent variables or factors, correlated significantly with PMM mastery. The seven dependent measures of learning were thus not equal in capturing factors that may lead to cognitive gain in a free-choice setting. Conversely, not all of the 11 factors or variables hypothesized to influence learning in free-choice settings emerged as equally important. The quality of exhibit elements, exposure to exhibits, advance organizers, within group social mediation, prior experiences, and prior knowledge emerged as the most important independent factors across the board (or across the seven measures of learning). However, none of the factors alone emerged as THE variable that could explain much of the learning we observed and

764


measured in this study. Furthermore, whether and to what degree a factor might influence learning from museums differed depending on how learning was measured. Thus, for the sake 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 broaden and 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 Knowledge and Prior Interest One impediment to understanding the impact of the various factors on learning was the great heterogeneity of the visiting population we sampled. Theoretically, there are many ways to segment a group of visitors such as we investigated. It makes most sense to segment visitors on attributes they possessed entering the experience, thus possible variables include age, gender, race/ethnicity, prior interest, expectations and motivations, and prior knowledge. Previous research (Falk & Adelman, 2003) suggested that prior interest and prior knowledge would be particularly important variables to consider. Given what we know about the constructive, cumulative nature of learning, it is not surprising that what someone knew upon entering the World of Life exhibition strongly influenced what they knew when they exited. Those with the most knowledge upon entering were still those with the most knowledge upon exiting. However, this did not mean those with 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 visitors into two “prior knowledge” groups based upon their previsit PMM breadth scores; those with 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 determined by a composite measure of visitors’ interest in biology and their interest in watching a television show related to biology. Both items were measured on a scale from 1 – 6; the overall prior interest score, thus, ranged from 2 to 12, with a median score of 9. Visitors were grouped into a “below median prior interest group” (n = 77) and a “median and above median prior interest group” (n = 111). There were no significant differences in the previsit PMM breadth scores (t(2 − tailed) = −.46; d f = 186; p = .65). While prior interest did not influence the degree of learning, it did influence which of the 11 factors mediated the learning that occurred (see below). Table 7 shows that depending upon an individual’s prior knowledge 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 things and also possessed the lowest entering interest levels in biology---they were generally not very interested in the topic. Only two factors emerged as significantly affecting changes in conceptual understanding of life science---motivations and expectations and advance organizers. For this group of visitors, NOT wanting to have a good time, in other words not being strongly motivated by the need for entertainment, increased the likelihood that they would gain knowledge of biology (rho = −.299, p < .05). Advance organizers, in particular attending to the various signage and exhibit elements that explained what the exhibition was about, increased learning for the visitors in this group (rho = .244, p < .1).

1. Motivation and expectation Intent to learn about the world Intent to have a good time Intent to entertain family/friends 2. Prior knowledge Visitor self-rating of biology knowledge Measured knowledge (pre PMM breadth score)) 3. Prior experiences (*) Visited CSC before Visited World of Life before 4. Prior interest Combined prior interest scales (interest in biology and in watching a documentary on child development) 5. Choice and control Researcher rating of visitors’ ability to choose exhibits Visitor self-rating of ability to choose exhibits 6. Within group social mediation Researcher’s rating of intensity of internal social interaction Number of adult social interactions (adult/adult, adult/child) 7. Facilitated mediation by others Researcher’s rating intensity of external social interaction Visitors’ rating of staff interaction usefulness

Factors Hypothesized to Influence Learning (Independent Variables)

.018 .023 −.051 −.013 −.056 −.051k

−.141 .059 −.167 .344* −.279 −.137j

.196 .032 −.085 .174 .013 .125j

Continued

.104 −.638*i

−.103 −.117

.207 .196

.093

−.100

.153

.098 −.231

.142

−.477** −.404*

−.102 .010

−.038 −.312*

.349* .060 .007

Above Median Prior Knowledge N = 48

−.081 .062

.145 −.057

−.190 −.141

.085 .018 .100†

Below Median Prior Knowledge N = 63

Median or above median prior interest

.045 −.119

.006 −.195 .158


−.077 −.299* −.141


Below median prior interest

TABLE 7 Simple Correlation Analysis (Spearman rho) Between Factors Hypothesized to Influence Learning From Museums and Changes in PMM Breadth Scores by Levels of Prior Knowledge and Prior Interest VISITOR LEARNING FROM A SCIENCE CENTER EXHIBITION 765

.492** .049

.196 .178

.096 .003

.049 .205 .185

.235† .130 .179

.480** .089 .508**

.208 .208 .188

−.212

−.070

−.165

.016

.269† .121

.134h .252i

−.102†f .250g†

−.221c .379*d

.137a .050b

.007 −.076


.002 .237†


.336* .289†


Median or above median prior interest

.244† −.028


Below median prior interest

Levels of prior knowledge are based on the pre-score (mean = 4.25; median = 4). “Below median” includes the median score. Levels of prior interest are based on the combined interest scale: interest in biology, and interest in watching a documentary on child development on TV. Both were measured on a 6-point scale (1 = no interest; 6 = very high interest). The two items were added into one scale (2 – 12). The median and mode were 9. † p < 1. ∗ p < .05. ∗∗ p < .01. a n = 39, b n = 34, c n = 29, d n = 27, e n = 41, f n = 56, g n = 50, h n = 40, i n = 22, j n = 18, k n = 26, l n = 14.

8. Advance organizers Total engagement with advance organizer exhibits Movement pattern through clustered exhibits 9. Orientation to the physical space Researcher judgment of orientation Visitor judgment of orientation 10. Architecture and large-scale environment Degree of crowding 11. Design of exhibits (quality and exposure) Quality: average engagement score for top 10% exhibits Quality: average engagement score for top 24% exhibits Exposure: Hit rate (percent of total exhibits visited) Exposure: Overall average intensity (average engagement score for all exhibits) Interviewer effect (length of entry interview)

Factors Hypothesized to Influence Learning (Independent Variables)

TABLE 7 (Continued)



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 but expressed a relatively low interest in the topic; admittedly a strange combination. Interestingly though, the visitors in this subgroup were influenced by a large number of contextual factors. Prior experiences, social mediation, advance organizers, orientation, and exhibit quality and quantity all influenced these visitors’ learning. Specifically, this group of visitors was the only subgroup who showed an influence on 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 previous visits to the World of Life exhibition (rho = −.404, p < .05). Also, only within this subgroup was social mediation a significant influence on learning. The number of social interactions in 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 the exposure, the greater the learning (rho = .336, p < .05). This group of visitors’ self-reported judgments on how oriented they were also emerged as important: the higher the perceived orientation, 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 exhibit quality 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 of museum experts. They also positively benefited by seeing more exhibit elements; those that 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 they were supposed to do with an exhibit, the greater the learning (rho = .492, p < .01). For this group, the expected outcome that seeing more, high quality exhibits improved learning was true, though as we will see, this was the only group for whom this was true. However, the more 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 of the four subgroups of visitors. Low Knowledge/High Interest. This was the largest of the four subgroups, comprising

more than a third of visitors (34%). This group is the most frequently represented population at most museums (Falk & Dierking, 2000). Four factors correlated significantly with change 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 with a 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 positively correlated with changes in learning (rho = .250, p < .1). Also, at the margins of significance, there was a correlation between seeing more of the top 10% of exhibits and learning (rho = .235, p < .1). The strongest correlation we found in this group was a negative correlation with prior knowledge (rho = −.312, p < .05), which suggests that this group was not as homogeneous with regard to prior knowledge as the other groups---individuals with lower knowledge within this cohort showed greatest gains. High Knowledge/High Interest. The final subgroup representing about a quarter of the

visitors we sampled (26%) had above median knowledge and above median interest in life science 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, the first of these is the most interesting. Not everyone in this group of knowledgeable, interested

768


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

Post

Sex

Mean

SD

Mean

SD

N

Percent Changea

Male Female Total

4.03 4.45 4.25

1.53 1.63 1.59

5.41 5.84 5.64

1.85 1.86 1.86

90 101 191

43.4 40.2 41.7

a

Percent change is determined on the sum of the individual percent changes rather than the percent change of the mean pre and post values. The percent changes are not different between 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 significant for p < .05 (F = 3.18; df = 1; p = .076).

individuals learned equally, those who were consciously and specifically motivated to learn during their visit learned the most (rho = .349, p < .05). The individuals in this group who deliberately engaged with more exhibits had greater learning gains, but only moderately so (rho = .269, p < .1). And then there was the somewhat bizarre finding that interactions with science center staff, particularly when they were 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 sample of 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 asking 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 prior knowledge and interest. Visitors with below median prior interest and above median prior knowledge were affected by far more of the 11 factors than those who exhibited median or above median prior interest, or below median prior interest and knowledge. Interestingly, 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 levels rather than by the other 10 independent factors and 24 independent measures used in this study; although this result did not emerge when interest alone was used as an independent variable. 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 9 Post-Pre Comparisons of Learning Outcomes (PMM Breadth Scores) for Visitor Social Group Type of Visitor

N

Percent Change

Alone Family group All adult group Total

12 143 34 189

61.3 38.8 47.1 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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TABLE 10 Post-Pre Comparisons of Learning Outcomes (PMM Breadth Scores) for Ethnicity Ethnicity White Black Latino Asian Other Total

N

Percent Change

93 29 45 11 12 190

39.0 39.5 52.8 34.4 34.5 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 learning in 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. It asked 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 the California Science Center’s World of Life exhibition did learn science. Our sample included a very diverse range of visitors, in large part due to the fact that the California Science Center has one of the most socio-economically and racially/ethnically diverse visiting publics of any 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, with a wide range of prior knowledge of biology. Our sample included individuals with only the most rudimentary knowledge of life sciences, as well as individuals with graduate degrees in biology working in life sciences careers. The vast majority of visitors surveyed in this study exited the World of Life exhibition at the California Science Center with a measurably enhanced 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 measurements to capture the learning of all these different visitors; any one measure alone would have missed changes in some percentage of the visitors sampled. If only one measure had been used (say correct answers on the multiple choice questions), far fewer visitors would have exhibited signs of cognitive change. Since the measures were somewhat independent of one another, together they were able to describe the “learning” more comprehensively. This result suggests that assessment or measurement of free-choice learning needs to employ a broad set of measures rather than a focused one if researchers seek to capture the entire range of potential cognitive change that may have occurred as a result of a museum visit or other free-choice learning activity. Also noteworthy, consistent with other studies (e.g., Falk & Adelman, 2003), these results would suggest that science museums are particularly useful for facilitating science learning amongst the least knowledgeable citizens; the less

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visitors to the California Science Center knew about life science, the more they learned from the World of Life exhibition. The data support the contention made by a variety of investigators, as cited in the Introduction, that a range of factors, including prior knowledge, motivation, and expectations, within group social interaction, advance organizers, and exhibition design strongly affected visitor learning, while the other factors investigated---prior experience, prior interest, choice and control, between group social interaction, orientation and architecture---also impacted learning 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 attributable to our abilities to appropriately measure them. In fact, validly and reliably operationalizing and measuring all of these factors remains a challenge. Still, it was possible through this investigation 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. In other words, this study supports the idea that many of these factors were somehow important, but suggests that no single factor was capable of adequately explaining visitor learning outcomes across all visitors. The key to better teasing out individual effects of the 11 proposed factors on learning from museums was through meaningfully segmenting visitor groups. Traditional demographic categories like age, gender, and race/ethnicity had limited usefulness in this respect. Prior knowledge and prior interest, however, emerged as powerful ways to divide up the population. Since we lacked the insight to create these segmented groups a priori, our ability to create them a posteriori diminished our analytical powers. The more narrowly we defined subsets of visitors, the smaller our sample size. This suggests that in the future, we should collect 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, we lacked 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 constraints of the free-choice learning environment. We are currently conducting investigations to test these assumptions and remedy these problems. Finally, we sought to answer the question of whether or not the Contextual Model of Learning provides a useful framework for understanding learning from museums. We would argue that the results of this study appear to support the value of the Contextual Model of Learning as an operational framework, with some caveats. The study reinforced what most already know that learning from museums is highly complex. The exact nature of the life science learning that occurred in the World of Life exhibition varied considerably between 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 unravel the complexities of learning from a science center. As the results reported here so clearly revealed, depending upon who the visitor was, what they knew, why they came, and what they actually saw and did, the outcomes of the museum experience were dramatically affected. The framework provided by this model allowed us to begin to unravel these complex interactions and relationships between the visitor’s personal, sociocultural, and physical contexts; relationships likely to be missed if we had only focused on one or the other of these contexts or their embedded variables. That said, even at best, the individual factors within the Contextual Model of Learning allowed us to explain only a small portion of the learning that we were able to record. Some of the changes in the learning that resulted from visitors experiences in the museum were almost certainly a direct consequence of random events. We believe that learning in science centers (or any other free-choice learning for that matter) does, in fact, depend upon 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 over time through interactions with both predictable and unpredictable events. The collective interactions, rather than just the initial state, determine the outcomes. In addition, random events influence not only which factors come into play as important, but also modulate the relative amplitude of impact those factors have on learning; stochastic factors influence both the quality and quantity of learning that results. Translated into the vernacular of a museum visit, we would envision a visitor entering the museum with a likely learning trajectory; a trajectory determined by that visitor’s specific prior 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 also strongly interact). We can presume that these factors, collectively, predispose the learner to interact with the setting in relatively predictable ways. However, once in the setting, the visitor is affected by a whole series of additional factors, some of which are under the control of the institution (e.g., advance organizers, good and bad exhibits, presence or absence of orientation 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 outside of 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 that exhibit, a bright light catches the visitor’s attention, an accompanying child needs to go to the toilet, a volunteer “randomly” selects the visitor to be part of a demonstration, a text panel 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 strongly influence learning and significantly diminish the predictability of any outcome. In other words, the nature of the science center visitor’s learning experience depends in part on things 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 independent measures did not correlate stronger with our learning outcome measures. Although we believe the current Contextual Model of Learning to be an excellent first step in describing the complexity of museum learning experiences, we are willing to believe that it is not yet a mature or complete framework. As we continue to better understand the nature of free-choice learning, we believe we will be able to continue to refine and improve upon this framework. We believe that studies such as the one summarized here provide the beginnings of a more conceptually based and empirical approach to understanding learning from settings like 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-refined model of learning from museums. We believe that this study has demonstrated that learning from 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 better research. Historically, both practitioners and researchers have treated visitor populations as homogeneous groups, influenced by the same set of variables. Investigations such as this reinforce the importance of embracing a more complex model of the museum experience; complex but still manageable. If more valid, reliable, and realistic (i.e., sensitive to the temporal, logistical, and ethical constraints of free-choice learning settings) tools for segmenting visitor populations can be developed, practitioners should be better able to facilitate learning as they will be better able to customize experiences and measurements to 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 instruments to entering states and document salient conditions during the museum experience. Environments developed to support real-world learning such as museums, are not mere backdrops for 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 real take-away message of this article is that simple, reductionist, linear approaches to affecting and understanding learning from museums will simply not suffice. An awareness of this reality has begun to creep into school-based learning research as well, most notably under the banner of “design research” (cf. Brown, 1992; Cobb et al., 2003; Collins, 1999). Only by appreciating, and accounting for the true complexities of the museum experience will improved facilitation and understanding of learning from museums emerge. APPENDIX Research Protocols ‘‘Pre’’ Living Things 1. Energy is important to organisms because a. b. c. d.

.

it enables them to regulate their internal environment living things adapt to their environments it provides the heat they need to stay warm it powers life processes

2. Your heart beats more quickly and you breathe more rapidly after exercising. This characteristic of life is . a. b. c. d.

reproduction growth and development maintenance of homeostasis response to a stimulus

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

.

homeostasis evolution an adaptation a response to a stimulus

4. Everybody knows a little bit about biology. On a scale of 1 to 6 (1 you know absolutely nothing about biology and 6 you’re an expert in biology), how would you rate your knowledge 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 these things? [Probe: What are these processes? Tell me more about them?] 6. Do you think there are any characteristics that are common between humans and other living things? Can you tell me more about them? [Probe: Do you think there are any similarities between humans and other living things? Can you tell me more about these similarities?] 7. Is there a topic(s) in biology you find particularly interesting? What is it? On a scale of 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 you rate 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 “Child Development” in a couple of months. On a scale of 1 to 6 (1 being you don’t care how bored you are that day you still won’t watch the program and 6 being if you knew it was on you would try to watch it no matter what even if you had a busy schedule that 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 that conclusion?] 10. Here are a few reasons that people gave us as to why they came to the California Science 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 much do you agree with each of those statements: a. I came to the California Science Center to find out more about the world I live 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 the museum and there are some people who just go along on a totally “whim” basis. On a 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 rate yourself? ‘‘During’’ Tracking Guide Time of day: 1. Researcher should observe and document if visitors are reading the sign when they enter World of Life, which reads “Welcome to the World of Life. From apple trees to 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), how crowded was World of Life? 5. The researcher will also record the percentage of exhibits that are functioning on a daily basis. 6. The researcher will qualitatively rate whether or not the visitor was controlling which exhibit(s) he/she wanted to look at and when on a scale of 1 to 6 (1 having no control and 6 having total control). (e.g., Was a child or partner determining which exhibit 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 was recorded]

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Visitor Tracking Protocol Rates the number of exhibit elements visited, which exhibit elements were visited and the level of interaction at those exhibits. 0 = Walked right by 1 = Pauses, glances around but does not really engage 2 = Stops, views exhibition elements in a cursory way, engages only a little 3 = Stops, views over half of the exhibition elements, appears fairly engaged and focused 4 = Stops, views nearly all of the components in the exhibition, viewing everything/interacting/reading text quite thoroughly as if they intend to look at the whole thing 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 intensity of the social interaction. Rating scale for quality of social interaction 1 = No interaction with others 2 = Interaction with others, but most unrelated to the exhibition 3 = Low interaction with others---much related to the exhibition 4 = Moderate interaction with others---much related to the exhibition 5 = High interaction with others---most related to the exhibition 6 = 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 intensity of the social interaction (same scale as above). ‘‘Post’’ Living Things 1. Energy is important to organisms because e. f. g. h.

.

it enables them to regulate their internal environment living things adapt to their environments it provides the heat they need to stay warm it powers life processes

2. Your heart beats more quickly and you breathe more rapidly after exercising. This characteristic of life is . i. reproduction j. growth and development


775

k. maintenance of homeostasis l. response to a stimulus 3. A dog barks at a mail carrier. This is an example of m. n. o. p.

.

homeostasis evolution an adaptation a response to a stimulus

4. There are certain things that living things do. Do you know anything about these things? [Probe: What are these processes? Tell me more about them?] 5. Do you think there are any characteristics that are common between humans and other living things? Can you tell me more about them? [Probe: Do you think there are any similarities between humans and other living things? Can you tell me more about these similarities?] 6. On a scale of 1 – 6 (1 being you did not like the building at all/get me out of here and 6 being you loved the building), how did you like the building in terms of was it too cold, too hot, too many people? Was there something/anything about the environment that 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.) Did you have a set plan here at World of Life? Did you have a good sense as to where you were going? [Probe: Were there any specific exhibit elements that you wanted to see first, second, etc? Why?] Did you have a set plan here at World of Life? Did you have a good sense as to where you were going? [Probe: Were there any specific exhibit 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 see here at the World of Life or was it your child/partner/friend making those decisions for you. On a scale of 1 to 6 (1 being you were NOT the one who got to decide where to go, when to go, and what to see and 6 being you were the one who got to decide where 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 you felt that the choices here weren’t good for you and 6 being the choices of exhibits you had fit your needs perfectly/were good for you). [Probe: Give an example such as “Some people say that even though I have 100 cable channels at home, none of the choices are good for me. Some other people say, that I have 100 different cable channels at home and I don’t know which are the ones I want to watch, because they are ALL so good.] [Probe: Why?] 10. Did you have any interactions with any staff member(s) (other than me)? If yes, how would you rate that interaction on a scale of 1 to 6 (1 being the staff member was not useful 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 Work Best time to call: E-mail: [The following information was recorded during the tracking and/or as part of the postscript] 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 from informal “conversation” with the visitor over the course of the interview] Gender: M F Age: 8 – 11 yrs 12 – 15 yrs 30 – 50 yrs [True age was estimated and recorded] Race/ethnicity: Caucasian African American Latino Asian American Other Social Group: Alone Family All child/teen group All adult group Been 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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