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Assessing Exhibits for Learning in Science Centers: A Practical Tool


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This paper presents an exhibit assessment tool for science centers that is based on the premise, supported by learning theories and research, that the level to which a visitor is engaged by an exhibit is a direct indicator of the learning taking place. Observable behaviors are used to distinguish three stages or levels of visitor engagement described as initiation, transition, and breakthrough. A distinctive visitor engagement profile can be constructed for an exhibit that can then be used in assessing the effectiveness of subsequent changes made to the exhibit experience. It is suggested that the visitor engagement and exhibit assessment model describes and predicts relationships between exhibits, visitors, and observable learning behaviors in science centers.
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Assessing Exhibits for Learning in Science Centers: A Practical Tool
Chantal Barriault a;David Pearson b
a Science North, Sudbury, Ontario, Canada b Laurentian University, Sudbury, Ontario, Canada
Online publication date: 07 April 2010
To cite this Article Barriault, Chantal andPearson, David(2010) 'Assessing Exhibits for Learning in Science Centers: A
Practical Tool', Visitor Studies, 13: 1, 90 — 106
To link to this Article: DOI: 10.1080/10645571003618824
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Visitor Studies, 2010, 13(1), 90–106
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Visitor Studies Association
ISSN: 1064–5578 print / 1934-7715 online
DOI: 10.1080/10645571003618824
Assessing Exhibits for Learning in Science
Centers: A Practical Tool
by Chantal Barriault1and David Pearson2
1Science North, Sudbury, Ontario, Canada
2Laurentian University, Sudbury, Ontario, Canada
This paper presents an exhibit assessment tool for science centers that is based on
the premise, supported by learning theories and research, that the level to which a
visitor is engaged by an exhibit is a direct indicator of the learning taking place. Ob-
servable behaviors are used to distinguish three stages or levels of visitor engagement
described as initiation, transition, and breakthrough. A distinctive visitor engagement
profile can be constructed for an exhibit that can then be used in assessing the ef-
fectiveness of subsequent changes made to the exhibit experience. It is suggested
that the visitor engagement and exhibit assessment model describes and predicts re-
lationships between exhibits, visitors, and observable learning behaviors in science
Learning is without a doubt the most complex of human activities. From babies
mastering language, to the development of attitudes and beliefs about the world,
investigating and understanding our environment, making music or managing the
political affairs of nations, we are all, in various individual ways, constantly involved
in learning. Scholastic instructional settings where learning is formally organized,
guided by curricula and led by teachers, dominate in the learning of science. However,
science concepts, data, and theories, often embedded in science news stories, are all
around us, quite apart from what is presented in the formal education system.
The media and the Internet provide access to a very wide variety of science news
and opinion. Museums, zoos, aquaria, botanical gardens, and science centers make
science available to the public through programs and exhibits. They are often referred
to as “informal learning settings” where “free-choice learning” occurs (Falk, 2001).
There is a very large, if not total, degree of self-direction and self-selection involved
when people choose to visit one of these settings. Then, once that choice has been
made, there is the freedom to decide on the content of the experience. Falk and
Dierking (2000), leading researchers in the field of informal learning, posited that
“free-choice learning tends to be non-linear, is personally motivated and involves
considerable choice on the part of the learner as to what to learn, as well as where and
when to participate in learning” (p.13).
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Assessing Exhibits for Learning in Science Centers
Of the windows on science that are not available at home through the media or the
Internet, science centers are distinctive because of the participatory experiences they
offer their visitors. Bell, Lewenstein, Shouse, and Feder (2000) described “designed
spaces”—including museums, science centers, zoos, aquariums and environmental
centers—as science learning environments, rich with real-world phenomena, where
people can pursue and develop science interests, engage in science inquiry, and reflect
on their experiences through sense-making conversations. Designing those experi-
ences can be driven by many objectives, but visitor learning of some kind is usually
a priority. However, assessing the learning that results from a visitor spending time at
an exhibit is very challenging. Furthermore, it cannot be accomplished with the same
methods that are used in formal instructional settings where participation is mandatory
and the learning objectives are explicit and strongly focused on cognitive gains. In
science centers, the learning is much more multi-dimensional and any assessment of
the learning experience needs to take into consideration the affective and emotional
impacts, the very personal nature of each experience and the contextualized nature of
that experience. We believe the framework and model we describe is appropriate for
the learning that takes place in science centers and can provide useful information in
the design of visitor experiences.
Evaluating the learning experience of visitors to science centers is increasingly
focused on questions from stakeholders who want to see that their investments are
contributing to effective programs (Stone, 2008), that the science center is having
an impact in the community (Persson, 2000; Rennie & Johnson, 2007) and, at the
very least, shows evidence that visitors are taking away positive experiences (Paris
& Ash, 2000). Some criticisms have been raised in relation to what learning visitors
can accomplish in a science center, as illustrated by a quote from a retired chemistry
professor in a recent study by Rennie and Johnson (2007, p. 168) “and at (the local
science center) all they learn is how to push buttons!”. However, most of the literature
and research in this field clearly shows that the informal setting of the science center is
a rich learning environment that nurtures curiosity, improves motivation and attitudes
toward science, engages the visitors through participation and social interaction and
generates excitement and enthusiasm, all of which are conducive to science learning
and understanding (Bell et al., 2009; Braund & Reiss, 2004; Griffin, 2004; Ramey-
Gassert, Walberg, & Walberg, 1994; Rennie & McClafferty, 1996; Stocklmayer &
Gilbert, 2002).
The benefits of understanding the impact of exhibits on science center visitors
obviously extend beyond the need to provide proof to stakeholders. Science center
program and exhibit staff want visitor feedback in order to improve the visitor ex-
perience and increase the impact of the interaction. Many large science centers and
museums have research and evaluation staff who respond to this need and integrate
their results into the design and development of the visitor experience.
The benefits to the visitor of this research and application loop are increased
opportunities to engage with and learn from the science center’s exhibits and programs.
The Exploratorium’s APE Project (Active Prolonged Engagement) is an example of
the important role research and evaluation of the visitor experience plays in the design
and development of high-quality, engaging science exhibits (Humphrey & Gutwill,
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C. Barriault and D. Pearson
It can be argued, however, that most methodologies used by researchers and external
evaluators require substantial financial and human resources as well as expertise
in investigation methods. Despite having the best intentions, we believe that for
most science centers, the resource demands of research remain a difficult hurdle in
conducting research on the visitor learning experience and exhibit evaluation. What
is needed, on a practical level, is a research methodology or a tool, based on learning
theory, that is feasible for science center practitioners to undertake with relative ease,
producing practical, usable results. Science centers are searching for convenient and
effective ways to assess visitor outcomes. Rennie and Johnson (2007, p.169) noted
that “Although most science centers routinely collect demographic data about their
visitors and often information on their enjoyment, it is not always easy to shape
information about enjoyment into a compelling case for their impact”. Persson (2000)
has commented that such efforts are often limited by the lack of an influential database
of studies that demonstrate impact.
Learning in Science Centers: A Comprehensive Definition
Assessing the learning outcomes in science centers needs to not only focus on
cognitive gains, but must take into account the conditions, the processes, and the
engagement that lead to learning. In a recent review on the nature of learning and its
implications for science center research, Rennie and Johnson (2004) proposed that
measuring a visitor’s actual cognitive gains from an interaction with an exhibit does
not capture the multiple outcomes experienced by the visitor.
Models and frameworks have been proposed to better understand the nature of
the informal science learning experience. Among others, Dierking and Falk (1994)
recommendeded that any definition of learning applied to a science center and museum
environment must take into account the affective experience these institutions provide
for their visitors. Their landmark interactive experience model, which integrated
the personal, social and physical contexts of learning (Falk & Dierking, 1992) was
updated and further developed into the contextual model of learning (Falk & Dierking,
2000). The model stresses the importance of a visitor’s personal agenda, interest, and
motivation, as well as the socio-cultural nature and the physical setting of these
It is now understood that people are not passive recipients of knowledge or empty
vessels to be filled. Learning is an active process, an interaction between ideas that
learners currently hold and newly presented experiences. This description is the
constructivist approach to learning, which is primarily interested in how individuals
engage in the making of meaning from experience. In other words, learning involves
the active construction of new knowledge (Hein, 1998). Inevitably, motivation plays
a key role in the constructivist perspective on the learning processes.
Researchers in the field of informal science learning are now often basing their in-
vestigations on constructivist and socio-cultural theoretical frameworks when study-
ing the impact of a science center visit (Rennie, Feher, Dierking, & Falk, 2003;
Rennie & Johnson, 2004). It is well documented that exhibits that encourage social
interaction, build on visitors’ prior knowledge and experience, and respond to visitors’
interest and motivation have been shown to present visitors with the most opportunities
for making meaning and conceptual re-shaping (Csikzentmihalyi & Hermanson, 1995;
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Assessing Exhibits for Learning in Science Centers
Falk & Adelman, 2003; Falk & Storksdieck, 2005; Hein, 1998; Stocklmayer & Gilbert,
Engagement is Key to Learning in Science Centers
Csikszentmihalyi and Hermanson (1995) described the “flow” experience and the
significance of engagement in the learning process. Understanding that engagement
leads to the making of meaning and the construction of knowledge has played a
significant role in the development of theoretical frameworks about the nature of
learning in the science center. For example, in a study examining the effects of a
science center visit on people’s awareness of science and technology, Stocklmayer
and Gilbert (2002) concluded that “the engagement and subsequent interactions with
exhibits. . . . are determined by the prior experience and understanding of the visitor”
(p. 842) and that “the engagement of the individual is the key (p. 856)”. Rennie et al.
(2003) agreed that understanding learning “requires that the precursors to engagement,
as well as engagement itself be investigated” (p.113).
Observable Behaviors and Their Link to Learning
Early research in this field often characterized visitor learning behavior with such
variables as approaching an exhibit, reading signage, asking questions, discussing the
exhibit, and duration of time spent at the exhibit (see Rennie & McClafferty, 1995,
for a review). Researchers in these cases measured the frequency of such learning-
associated behaviors as an indicator of learning. Studies often sought to determine
the relationship between learning-associated behaviors and exhibit styles or exhibit
characteristics. Boisvert and Slez (1995), for example, concluded that the greatest
learning, as determined by time spent at an exhibit, occurred with staffed exhibits
and small, highly interactive but easily understood exhibits. A series of studies by
Borun and her colleagues investigated measures of family learning with respect to
exhibit interactions (Borun, Chambers, & Cleghorn, 1996; Borun, Chambers, Dritsas,
& Johnson, 1997; Borun & Dritsas, 1997). Of particular interest to this article is the
investigation in which Borun et al. (1996) developed measures of learning based on
the learning goals of exhibits used in the study. These learning outcomes were then
correlated to visitor learning behaviors. The findings showed a strong relationship
between learning levels and observed behaviors.
Observing visitors and analyzing their interactions and conversations has become
an accepted methodology in assessing visitors’ learning experiences. At the Ex-
ploratorium in San Francisco, for example, researchers study observable behavior as
a way of measuring the degree of visitor interaction with different types of exhibits
and how different exhibit elements affect visitor learning behaviors (Gutwill & Allen,
2002; Humphrey & Gutwill, 2005). Atkins, Velez, Goudy, and Dunbar (2009) re-
cently investigated the impact of different labels on visitors’ learning experiences by
analyzing observational data and recorded conversations. Analyzing visitor conver-
sations provides a window into understanding how visitors are making meaning from
their experiences and is widely used in family learning research (Ellenbogen, 2002;
Ellenbogen, Luke, & Dierking, 2004).
“We can’t necessarily see that learning has occurred, that new knowledge is gained,
a different opinion is held, or there is a disposition to modify behavior, for example.
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Rather, learning is observable in an individual’s actions, that is, what that person does
or says” (Rennie & Johnson, 2004, p. S6).
Observable Behaviors, Visitor Engagement and Exhibit Assessment
In this article, we present a visitor engagement and exhibit assessment model that
we have developed on the foundation of a framework of observable behaviors and
activities related to engagement that are indicative of learning (Barriault, 1998, 1999).
We believe this model will provide science center practitioners with an assessment
tool that is methodologically rigorous yet feasible to implement and that can generate
real insights into the impact of exhibits on the visitor learning experience. We suggest
that the model will provide science center staff with a comprehensive, far-reaching
learning assessment tool with applications for science center exhibit evaluation, floor
staff training, and exhibit development and improvement.
The visitor engagement and exhibit assessment model consists of
1. a visitor engagement framework (VEF) of observable behaviors;
2. the arrangement of those behaviors into learning related categories;
3. a visual representation of the level of engagement elicited by an exhibit; and
4. a model that indicates where intervention might increase visitor engagement with
an exhibit.
Barriault (1998) developed a visitor-based framework for assessing visitor learning
experiences with exhibits in a science center setting. Ten years of use at Science North
(Sudbury, Canada) have shown that this framework works with a wide variety of
exhibits and is a practical tool for science center staff to easily understand the impact
the exhibits have on visitors’ learning behaviors. As a result of this use, small changes
and additions in the descriptions of behavior and in the types of activities mean that
the framework can accommodate a wider variety of visitor experiences.
The framework consists of seven discrete learning behaviors that occur as part of
a visitor’s interaction with an exhibit. The learning behaviors can be grouped into
three categories that reflect increasing levels of engagement and depth of the learning
experience (initiation behaviors, transition behaviors, and breakthrough behaviors).
These levels of engagement capture the progression in a visitor’s learning experience
(Barriault, 1998).
Initiation Behaviors (Doing the Activity; Spending Time Watching Others Engaging
in the Activity). When visitors demonstrate these learning behaviors, they are taking
the first steps toward a meaningful learning experience. Even though they are not yet
completely involved in the experience, they are gaining some level of information
through the interaction which, in turn, could lead to more learning. Above all else,
visitors need to feel comfortable about committing themselves to engagement with
an exhibit. Initiation behaviors enable them to test the waters with minimum personal
risk and provide an entry point into further learning opportunities offered by the
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Assessing Exhibits for Learning in Science Centers
Transition Behaviors (Repeating the Activity; Expressing Positive Emotional Re-
sponses in Reaction to Engaging in the Activity). Smiles and outbursts of enjoyment
along with repetition indicate that a level of comfort has been achieved and that visitors
are willing, and even eager, to engage more thoroughly in the activity. Regardless of
whether the activity is repeated in order to better understand it, to master the functions,
or to observe different outcomes, the net outcome is a more committed and motivated
learning behavior.
Breakthrough Behaviors (Referring to Past Experiences while Engaging in the Ac-
tivity; Seeking and Sharing Information with Others; Engaged and Involved). Each
of these behaviors acknowledges the relevance of the activity, and the learning gained
from the activity, to the individual’s everyday life. The learning behaviors in this
category reflect a commitment on the part of the visitor to gaining information and
knowledge and to further exploring the ideas being presented. By referring to past
experiences, seeking and sharing information, and becoming engaged and involved,
a visitor’s interaction with an exhibit becomes a meaningful learning experience that
takes full advantage of the exhibit’s learning opportunities. It becomes evident that
the visitor is making meaning, building his or her own understanding of the concepts
through prior knowledge, prior experience, and further inquiry. Labeling these behav-
iors as part of the learning process is consistent with the constructivist literature on
learning (Hein, 1998), as well as with Falk and Dierking’s (2000) contextual model of
learning. Breakthrough involves engagement that clearly moves beyond short-lived,
purely physical interaction.
Examples of types of activities that characterize each of the learning behaviors are
outlined in Table 1. Through our experience in applying this framework, we have
found these examples to be useful reference points for practitioners when applying
the exhibit assessment and modification tool in the science center. It is important to
note that although the seven learning behaviors tend to occur sequentially, that is not
always the case. In fact, the behaviors can occur in a variety of sequences. A rich
learning experience means that many or most of these behaviors occur during an
interaction with an exhibit.
Observing Visitors in the Science Center
Science center staff at Science North, Sudbury, Canada are using this framework
as a tool to better understand the impact their exhibits are having on visitor learning
experiences. After initial training and guided practice using the VEF, science center
staff observe visitors as they interact with an exhibit and record the learning behaviors
they see using an observation sheet (see Figure 1). Visitors are either observed live
on the floor or recorded with a video camera and observed later. Information about
initiation and transition behaviors can be acquired by simple visual observation of
visitor-exhibit interactions. However, to collect information about breakthrough be-
haviors, we often need to hear and analyze the conversations between the observed
visitor and others, be they friends, family, or staff. In both live and video recorded
observations, consideration must be given to the influence the person or the camera
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Table 1. Types of activities that characterize learning behaviours
Learning behaviour Type of activity and exhibit examples
Initiation behaviors
1. Doing the activity In passing, not done completely
Doing the activity somewhat completely
Doing the activity without further exploration or testing of variables
2. Spending time watching
others engaging in
activity or observing
the exhibit
Looking at the exhibit working, or someone doing the activity
Watching the exhibit or person using exhibit with expressed interest in
the activity (facial expression or verbal)
Interested in learning outcome or in learning the activity; visitor does the
activity after observing.
Transition behaviors
3. Repeating the activity Doing the activity two to three times to attain desired outcome, to master
the exhibit’s function.
Enjoyment of outcome
Changing the variables once looking for a difference in outcome;
becoming involved/engaged
4. Expressing positive
emotional response in
Smiling, pleased with exhibit
Stronger signs of enjoyment such as laughter; verbal references to
Obvious signs of eagerness to participate; excited disposition;
Breakthrough behaviors
5. Referring to past
experiences while
engaging in the activity
Reference to past experience with exhibit or science centre
Simple reference to comparable experience in visitor’s life
Reference to comparable experience in their life as well as making
comparisons and deductions based on observations of similarities and
6. Seeking and sharing
Calling someone over to look at exhibit, or to ask them to explain an
exhibit; asking a question to staff or family member without lengthy
discussion or exploration of topic.
Reading signage; having conversations about exhibit and related science
with staff or family member
Sharing experience and information with others by explaining the exhibit
to them, giving them details about gained information and
observations; discussions and questions about exhibit with staff or
family member/friend
7. Engaged and Involved:
testing variables,
making comparisons,
using information
gained from activity
Engaging in inquisitive behaviour, exploratory actions such as repeating
the activity several times, reading signage, asking questions; remaining
on task for 2–3 minutes
Concentration and motivation are obvious; doing the activity as a means
to an end, or meeting a challenge; length of interaction significant, 3 to
5 minutes; outcome or result of activity important
Experimenting, testing different variables, looking for different
outcomes; engages in discussion with others (visitors or staff) about
the various outcomes; experience— ‘flow’; involved in activity for
long period of time i.e. more than 5 minutes
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Visitor Learning Behaviors Observation Plan
YC = young child (0-5)
Time: C = child (6-10) A = adult (20-64)
T = teen (14-19)
Observer: PT = preteen (11-13) S = senior (65+
Number Subject w/ Description Age
Group GenderDoing
Positive Emotiona
Refering to Past
Seek / Share
Involved /
Initiation Behaviours Transition Behaviours Breakthrough BehavioursVisitor
Figure 1. Visitors Learning Behavior Observation Plan.
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C. Barriault and D. Pearson
may have on the visitor’s behaviors. Proper protocols are also necessary with respect
to ethical practices as they apply to studying human subjects. These protocols are
consistent with Gutwill’s (2003) pioneering work on implicit consent.
The observational data are quantified and plotted to produce what we refer to as
the exhibit’s Visitor Engagement Profile (VEP).
Plotting Behaviors: Visitor Engagement Profiles
The levels of engagement shown by visitors at a particular exhibit can be depicted in
VEPs (Figure 2). Each of the three Engagement Level categories, Initiation, Transition
and Breakthrough, is represented by a bar showing the percentage of visitors who show
one or more of the behaviors characteristic of a category. The baseline for a VEP is
the number of visitors who approach an exhibit and pay attention to it. Visitors who
do not stop to interact with an exhibit are not included in a VEP. In other words, the
attracting power of an exhibit is not assessed when using this tool. Instead, the VEP
focuses our attention on the learning behaviors demonstrated by visitors once they
have made the commitment to engage with the exhibit. The argument can be made
that it is very challenging to know the reasons why a particular visitor does not stop
to interact with the exhibit being assessed. The popularity of the exhibit, for example,
may make it difficult for visitors to find an opening to interact with it. Alternatively,
visitors may be members of the science center and have interacted with the exhibit
many times in the past. Therefore, for the purposes of assessing the impact of the
exhibit on the visitor learning experience, we have chosen to include only those who
interact with it.
Visitor Engagement Profile
See Your Pupil Reaction
Initiation Transition Breakthrough
Learning Behaviours
Percentage of Visitors
Figure 2. An example of a Visitor Engagement Profile with an engagement curve for the Pupil
Reaction exhibit at Science North.
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Assessing Exhibits for Learning in Science Centers
Visitor Engagement Profile
Genetic Traits
Initiation Transition Breakthrough
Learning Behaviours
Percentage of Visitors
Figure 3. An example of a Visitor Engagement Profile for the Genetic Traits exhibit at Science North
showing an engagement curve with a low slope.
The line connecting the mid-point of the summit of the bars is the engagement curve
(see Figure 2). Exhibits that bring out transition and breakthrough behaviors in a high
proportion of visitors have engagement curves with a low slope (Figure 3). Those at
which many visitors become quickly disengaged and only a few show transition and
breakthrough behaviors, have a steep, rapidly declining engagement curve (Figure 4).
Visitor Engagement Profile
Initiation Transition Breakthrough
Learning Behaviours
Percentage of Visitors
Figure 4. An example of a Visitor Engagement Profile for the Test your Rowing Skills exhibit at
Science North showing an engagement curve with a rapidly declining slope.
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Visitor Engagement Profile
Grip Strength
Initiation Transition Breakthrough
Learning Behaviours
Percentage of Visitors
Figure 5. An example of a Visitor Engagement Profile for the Measure Your Grip Strength exhibit at
Science North showing a “U” shaped engagement curve.
It is important to note that some exhibits, such as videos or computer interactives,
can elicit high breakthrough behaviors without intermediary transition behaviors. In
those cases the engagement curve has a “U” shape (Figure 5).
For science center practitioners, compiling the observation data to produce VEP
graphs immediately provides a visual level of information about the impact or the
outcome of visitors’ interactions with a particular exhibit, based on engagement
behaviors that indicate learning. If one is interested in plotting more details about
individual exhibits, one could graph each learning behavior within each engagement
level. In addition, if the age of visitors, or whether or not they are visiting in a group,
is of interest, that information can also be plotted if recorded in the observation sheet.
For example, a question we can ask is whether or not visitors in groups engage in
more breakthrough behaviors than visitors who are interacting with the exhibit by
As a next step in the development of a practical and usable tool, we propose a model
that shows relationships between exhibit impact, visitor engagement levels, and exhibit
modifications and improvements that would provide science center professionals with
a comprehensive overview of the visitor-exhibit interaction, learning experience, and
exhibit design consideration. What follows is a description of that model and how
we see it working as an exhibit evaluation and modification tool for science center
The VEF and the VEPs described in the previous sections form the first two
components of a relational model we are calling the Visitor Engagement and Exhibit
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Assessing Exhibits for Learning in Science Centers
Figure 6. The Visitor Engagement and Exhibit Assessment Model.
Assessment Model (VEEAM; Figure 6). The model outlines the process for (a)
analyzing the engagement behaviors elicited by an exhibit and then (b) using the
analysis to modify the visitor experience of that exhibit. It is, in essence, a visitor
behavior-based method of formative evaluation.
As previously shown, the levels of engagement observed at exhibits are quantified
in VEPs (Figures 2–5) that show the relative percentages of visitors in the initiation,
transition and breakthrough categories. Modification of the visitor experience through
experience analysis and experience modification so as to elevate engagement is the
final component of the model.
Quantitative data drawn from the observations of visitor behaviors and depicted
in the VEPs are the starting point for analyzing visitor-exhibit interactions. Exhibits
with low transition and breakthrough categories might well be judged ineffective,
depending on whether learning is a major part of their purpose. In that case a detailed
review of the engagement behaviors noted in the observation phase can stimulate ways
of modifying the visitor experience. Such changes could be to encourage interaction
with an on-the-floor explainer or to alter the exhibit itself so as to bring about more
of those behaviors that were originally sparse. Providing context and everyday life
examples in the exhibits for example, may increase the likelihood that visitors will
make reference to past experiences or prior knowledge and engage more deeply with
the exhibit’s learning opportunities. To encourage visitors to share information or their
experience with others, one might consider adding a social or collaborative component
to the exhibits such as a cooperation exercise. Adding testable variables to an exhibit
would likely increase the percentage of visitors who become engaged and involved,
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Visitor Engagement Profile
Sprint Track Human Machine 2004
Initiation Transition Breakthrough
Learning Behaviours
Percentage of Visitors
Figure 7. The Visitor Engagement Profile for the Sprint Track exhibit in 2004, before changes were
made to improve the visitor learning experience.
like having different sized gears at a car-building exhibit, or many tools to build
dams in a sand-and-water exhibit. Open-ended questions as labels might increase the
occurrence of breakthrough learning behaviors.
Improvements to the level of engagement provided by individual exhibits can
then be demonstrated as the VEP changes. An example from a study conducted at
Science North (Barriault & Kneller, 2004; Waltenbury, 2005), will help illustrate the
application of the model in improving an exhibit’s impact on the visitor learning
Applying the Model to Improve Exhibits and Visitor Learning Experiences
Experience Analysis
The Sprint Track at Science North is an exhibit that was first introduced in the
Human Machine special exhibition in 2004. It is a 10-meter sprint track with starting
blocks where visitors are timed when they reach the end of the track. A video coach
and a replay video of the visitor encourage participants to improve their exit out of
the starting blocks, like professional athletes would do in an actual race. The VEP
(Figure 7) for the 2004 Sprint Track showed good levels of transition behaviors
as visitors clearly showed high levels of enjoyment (positive emotional responses)
and repeated the activity often. The breakthrough behavior levels were not as high
as expected, suggesting that visitors were not taking full advantage of the learning
opportunities presented by the exhibit. For example, although the video coach and
the play back option were intended to encourage visitors to make changes to their
positioning and test variables to improve their time out of the starting blocks, these
exhibit features were not used by visitors as often as hoped.
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Assessing Exhibits for Learning in Science Centers
Visitor Engagement Profile
Sprint Track in Body Zone 2005
Initiation Transition Breakthrough
Learning Behaviours
Percentage of Visitors
Figure 8. The visitor engagement profile for the Sprint Track exhibit in 2005, after changes were
made to improve the visitor learning experience.
Experience Modification
In 2005, the Sprint Track exhibit was redesigned and moved to its permanent home
in the Body Zone Lab at Science North. Based on observations of visitor interactions,
constructivist learning approaches and physical design considerations, changes were
made to the exhibit and new observational data were collected to create a new VEP.
In short, the video coach and play back monitors were repositioned for easier access,
and changes to the labels encouraged visitors to test different starting positions.
Figure 8 shows the VEP for the Sprint Track after changes were made to improve the
learning opportunities presented by the exhibit. These changes proved beneficial as
both transition and breakthrough behaviors increased.
Additional Considerations
There are other questions having to do with the role of exhibits that must be born in
mind when interpreting VEPs. Some exhibits may be designed and placed simply to
draw people to a certain area of an exhibit gallery; some may be designed for a certain
age group, or for one gender rather than another; some may be icons. The cumulative
impact of a group of exhibits in an exhibit gallery is more subtle and complex than
simply the sum of the engagement profiles. However, other exhibits may well have
been intentionally designed to provide deeper learning opportunities. A steeper than
expected engagement curve on the VEP of such an exhibit will alert staff in the science
center that something is missing and modifications to the exhibit experience need to
be considered.
In assessing engagement it may also be useful to distinguish between circumstances
when on the floor staff (“explainers”) are available to interact with visitors and when
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C. Barriault and D. Pearson
they are not. In that case the engagement curve will reflect the role of on the floor
staff in encouraging a higher level of engagement. Sponsors of an exhibit may well be
convinced that the added engagement and learning is worth the cost of well-trained
staff who understand how to encourage and motivate visitors.
Some of the behaviors characteristic of the transition and breakthrough levels in-
volve conversation among individuals. If a high number of groups of visitors such
as families are at an exhibit when it is assessed then “seeking and sharing informa-
tion” behaviors may be very evident. For visitors, interacting alone with an exhibit
“testing variables and exploratory behaviors” are the most observable evidence of
In applying the assessment tool we have described, it is important to avoid reduc-
tionist thinking and only paying attention to individual exhibits. Exhibits are, indeed,
the smallest unit of a visitor’s experience and lend themselves to analysis but it is
the cumulative or whole experience that makes a visit memorable. The social con-
text, seeing and hearing the positive emotional responses of other visitors, creates an
ambience in which breakthrough is encouraged. Take an exhibit out of its context
and it may not be as successful in eliciting the behaviors that occurred elsewhere.
The characteristics of an exhibit that encourage connections to a visitor’s everyday
experience or knowledge can also be part of the context that helps an exhibit hall be
more than just the sum of its parts.
A description of the overall visitor experience of an exhibition consisting of many
exhibits could be usefully summarized with a median VEP derived from a significant
random sample of the exhibits (rather than an average which could be skewed by a
few highly engaging exhibits).
Much has been discovered about the nature of the learning experience in the science
center setting and researchers are responding accordingly with innovative research
methodologies that address the uniqueness of this experience. Examples range from
the personal meaning mapping technique (Falk & Storksdeick, 2005) to recording
and analyzing visitor conversations (Leinhardt, Crowley, & Knutson, 2002; Rennie
& Johnson, 2004). Professional development training in the field of visitor studies is
also increasing and the needs of this community of professionals are being addressed
through a variety of programs and reports.
As the field of science center learning research grows, as methods become refined
and as results become integrated in the design of learning experiences, it seems logical
that, beyond professional evaluators and researchers, science center practitioners, from
floor staff to exhibit designers, need a practical research tool that enables them to assess
the effectiveness of an exhibit at engaging visitors in a learning experience.
One can also argue that the development of such a tool needs to be well grounded
in science center research and informal learning theory as opposed to extrapolating
formal education learning theory to the science center setting. As discussed by Rennie
and Johnson (2004) “(p)erhaps now the exporting of theories in the other direction,
that is, from museum research to research in education and psychology, has begun to
address the ‘trade imbalance’ referred to by Paris & Ash (2000)” (p. 12).
104 Visitor Studies, 13(1), 2010
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Assessing Exhibits for Learning in Science Centers
The use and application of the VEF, the VEPs and the VEEAM have proven very
beneficial to Science North in a variety of ways. For the visitor, the result of this
research activity means exhibits that provide more learning opportunities. For the
science center’s staff, the use of these tools has enabled them to make improvements
to the visitor learning experience. It also empowers staff to use data, not just intuition,
to make changes to exhibit design while fostering a research culture that encourages
reflection in developing the visitor experience.
It goes without saying that consistent application of the guidelines for recognizing
and coding learning behaviors is required if the database built over several years in a
science center is to reach its full value. However, the value of a culture of observation
and recognizing the indicators of learning goes beyond the assessment process. It is the
hallmark of a reflective center where visitors are understood and offered opportunities
to engage in the processes of science.
Chantal Barriault thanks Alan Nursall for invaluable insights and contributions during the development of the
Visitor Engagement Framework and its implementation at Science North. We also gratefully acknowledge the
insightful contribution of two anonymous referees.
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Chantal Barriault is Senior Scientist, Research and Evaluation at Science North as well as Co-Director of the Lau-
rentian University/Science North Graduate Program in Science Communication. E-mail:
David Pearson was the founding director of Science North, and is a Professor of Earth Sciences and Co-Director
the Laurentian University/Science North Graduate Program in Science Communication.
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... Yet when set within these contexts, mobile learning research has primarily focused on children and students (Chen et al. 2003;Evans, 2008;Hedberg, 2014), and rarely on the ‗average' adult visitor. Additionally, many of the mobile learning and informal science learning studies have brought up the term engagement in conjunction with science learning, yet seldom investigated the concept of engagement itself, with few exceptions (Rennie et al. 2003;Barriault and Pearson 2010). ...
... This study takes the position that understanding engagement is a fundamental precursor to understanding learning. This has been echoed in the work of a small number of researchers who have argued for the significance of visitor engagement or highlighted a connection between learning outcomes and engagement (Rennie et al. 2003;Rennie and Johnston 2004;Barriault and Pearson 2010). Most influential in this study, has been the work of Barriault and Pearson (2010), who closely examined the meaning of engagement by presenting a framework of seven observable visitor engagement behaviours in science centres, grouped into three levels of engagement leading to learning. ...
... This has been echoed in the work of a small number of researchers who have argued for the significance of visitor engagement or highlighted a connection between learning outcomes and engagement (Rennie et al. 2003;Rennie and Johnston 2004;Barriault and Pearson 2010). Most influential in this study, has been the work of Barriault and Pearson (2010), who closely examined the meaning of engagement by presenting a framework of seven observable visitor engagement behaviours in science centres, grouped into three levels of engagement leading to learning. Modifications, however were necessary for this project in order to address the particular context of the research-a natural history museum instead of a science centre (science centres have more of a focus on interactivity). ...
Conference Paper
Full-text available
Faculty members at an English Language Centre in the Central-North of Saudi Arabia were surveyed on their skills and attitudes using mobile technologies in teaching English as a Foreign Language. Results indicated that Faculty members had a good level of skill and positive attitudes towards the use of mobile devices in EFL teaching. A number of statistically significant effects were identified for the independent variables age and teaching experience. Moderate positive correlations were found between Faculty members‘ level of skill using mobile devices and both Faculty attitudes towards using mobile technology in English language teaching and intention to adopt mobile technology in English language teaching. Future use of ICT was predicted by attitudes towards the use of ICT. This relationship was moderated by a covariate: self-reported skills in ICT usage.
... It has been streamlined to use tracking technologies like point-of-view cameras worn by visitors (Shapiro et al., 2017), mobile devices (Moussouri & Roussos, 2013Schautz et al., 2016), RFID (Lanir et al., 2017), and eye-tracking equipment (Eghbal-Azar & Widlok, 2013;Filippini Fantoni et al., 2013;Magnussen et al., 2017). Timing and tracking studies have been used as a loose proxy for learning, with the assumption being that longer linger times equate to greater opportunities for learning (Barriault & Pearson, 2010). Some tracking technologies primarily streamline collecting visitor location data to make traditional timing and tracking computations more seamless (Schautz et al., 2016), with the additional ability to more easily aggregate visitor data across space and time (Lanir et al., 2017). ...
... To support stronger inferences regarding learning, evaluators and researchers have augmented location-based metrics with behavioural observations like the degree of engagement (Barriault & Pearson, 2010) or more qualitative analyses of visitor talk (e.g., Allen, 2003). It is worth noting that under these schemes collaboration is not often documented for its own sake-rather, the social interactions of visitors are most often used for their potential to reveal clues about why they might choose to approach a location (Eghbal-Azar & Widlok, 2013) or about the state of learners' individual understanding and engagement (e.g., Allen, 2003). ...
... We call this next-generation timing and tracking method a "timing and tracking, together" approach, operating akin to Kuflik and Dim (2013) and Dim and Kuflik (2014), but for large groups. As emphasized above, timing and tracking metrics do not directly measure visitor learning; they assume that when visitors linger longer in front of an exhibit, the opportunity for them to learn increases (Barriault & Pearson, 2010). Similarly, we do not propose to make strong claims about measuring the quality of collaboration via our metric-we seek only to document opportunities for collaboration, as suggested by learners' proximity to one another. ...
Full-text available
Large-group (n > 8) co-located collaboration has not been adequately studied because it demands different conceptual framings than those used to study small-group collaboration, while also posing pragmatic constraints on data collection. Working within these pragmatic constraints, we use video data to devise an indicator of the current possibilities for learner collaboration during large-group co-located interactions. We borrow conceptualizations from proxemics and social network analysis to construct collaborative opportunity networks, allowing us to define the concept of collaborative opportunity temperature (COT) readings: a “snapshot” of the current configuration of the different social subgroup structures within a large group, indicating emergent opportunities for collaboration (via talk or shared action) due to proximity. Using a case study of two groups of people (n = 11, n = 12) who interacted with a multi-user museum exhibit, we outline the processes of deriving COT. We show how to quickly detect differences in subgroup configurations that may result from educational interventions and how COT can triangulate with and complement other forms of data (audio transcripts and activity logs) during lengthier analyses. We also outline how COT readings can be used to supply formative feedback on social engagement to learners and be adapted to other learning environments.
... Many authors and researchers in informal science education recognize that engagement, as influenced by visitors' prior experience and understanding, is key for meaning making and the construction of knowledge in the science centre setting (Kisiel, 2012;Hauan and Kolstø, 2014;Ocampo-Agudelo and Maya, 2021). Barriault and Pearson (2010) for example, developed a framework that links visitor engagement and learning-associated behaviors to the potential learning impact of an exhibit. Their Visitor-Based Learning Framework (VBLF) draws from constructivist and socio-constructivist learning perspectives (Barriault and Pearson, 2010) and provides science centre practitioners with an exhibit assessment tool that is empirically-driven and rooted in science centre visitor observations. ...
... Barriault and Pearson (2010) for example, developed a framework that links visitor engagement and learning-associated behaviors to the potential learning impact of an exhibit. Their Visitor-Based Learning Framework (VBLF) draws from constructivist and socio-constructivist learning perspectives (Barriault and Pearson, 2010) and provides science centre practitioners with an exhibit assessment tool that is empirically-driven and rooted in science centre visitor observations. ...
... For science centre and museum practitioners, assessing the direct impact of a facilitator on visitors' engagement with an exhibit could provide empirical evidence on which to base staffing decisions, with the potential to inform facilitator training. Thus, the purpose of this study is to analyse the impact facilitators have on the level of engagement of visitors as they interact with exhibits using the Visitor-Based Learning Framework (Barriault and Pearson, 2010;Barriault and Rennie, 2019). To complement this investigation, we explore the common patterns of facilitator activity in their interactions with visitors. ...
Full-text available
We studied how interactions with interpretative science centre staff impacts the learning behaviours and engagement levels of visitors who engage with exhibits at Science North (Sudbury, Canada). This study uses the Visitor-Based Learning Framework. The tool consists of seven discrete learning-associated behaviours that visitors show when engaging with exhibits, which are grouped into three categories of engagement: Initiation, Transition, and Breakthrough. These categories reflect increasing levels of engagement and depth of the learning experience. We studied forty-seven Science North exhibits, and 4,835 visitors to analyse the impact of unstructured facilitation in a naturalistic setting. We compared visitor Engagement Levels with and without a facilitator present. We determined that the presence of staff has a statistically significant impact on the percentage of visitors that engage in Breakthrough behaviours. When a facilitator is present, more visitors reach the Breakthrough Level of Engagement ( p < 0.001). In the second phase of the study, we explored what facilitators do and say through thematic analysis to uncover common patterns of facilitator actions and comments. Our findings showed that facilitators employed strategies and methods that can be grouped in four categories or Facilitation Dimensions: Comfort, Information, Reflection, and Exhibit Use. These dimensions encompass different strategies and techniques of facilitation, that are used in a variety of situations and sequences. Our study goes beyond anecdotal evidence to show that staff-visitor interactions have a positive impact on visitor engagement with exhibits and therefore, potentially on visitor learning from exhibits. Our findings can be used to inform not only training programs but also managerial decisions and considerations around resource allocation. We suggest that facilitators are a fundamental asset for institutions that prioritize visitor engagement, one that should be given top priority when considering areas for investing.
... Uma das técnicas mais comuns de medição da aprendizagem proporcionada pela visita a exposições de ciência eram os questionários de teste de conhecimentos aplicados antes e depois das visitas (vide, por exemplo, Falk, 1992, Amiko e Pokorni, 1990. Porém, esta técnica pressupõe uma concepção unívoca e a problemática de conhecimento e de "verdade" científica, em que os indivíduos são avaliados pelas respostas "certas" e "erradas", tal como nos inquéritos à cultura científica (Ávila e Castro, 2002 (Falk, 1983(Falk, , 1992Rahm, 2004;Bamberger e Tal, 2007;Barriault e Pearson, 2010) ou as entrevistas em profundidade (Bowker, 2004;Pedretti, 2004;Bamberger e Tal, 2009 O PMM foi desenhado para medir a forma como uma experiência de aprendizagem específica afecta de forma única a compreensão e a construção de sentido em cada indivíduo. Não se presume aqui que todos os sujeitos de aprendizagem entrem com conhecimento ou experiência comparáveis, nem requer que os indivíduos produzam respostas "certas" específicas como demonstração de aprendizagem. ...
Full-text available
Resumo Este artigo consiste numa abordagem exploratória à avaliação dos efeitos de uma visita a uma exposição de teor científico sobre a aprendizagem dos alunos do ensino básico e secundário, a partir da técnica dos Personal Meaning Maps (PMM). Após uma breve discussão sobre a aprendizagem não formal da ciência em contexto de exposição e uma descrição da técnica dos PMM como forma de avaliação da aquisição de conhecimento construtivista e contextual, que permite apreciar o grau mas também a natureza da mudança, são apresentados os resultados obtidos na aplicação desta técnica a duas turmas de alunos do ensino básico e secundário que visitaram a exposição "A Evolução de Darwin" (Fundação Calouste Gulbenkian, Lisboa, 2009). Se a análise quantitativa dos PMM permite determinar o grau de transformação nos conhecimentos dos alunos proporcionado pela visita à exposição, a análise qualitativa proporciona uma caracterização aprofundada dessa transformação, identificando os temas onde os alunos adquiriram conhecimento novo e aprofundaram ou rectificaram conhecimento já existente. Porém, a forma de aplicação dos PMM apenas permite medir os efeitos imediatos da visita à exposição, não os de longo prazo. Abstract This article consists of an exploratory approach to evaluate the impact of school visits to scientific exhibitions on scientific learning, based on the Personal Meaning Maps (PMM) technique. First, a brief discussion of non-formal science learning in exhibitions, and a description of PMM as a constructivist and contextual tool to assess way of assessing knowledge acquisition and measure the degree and nature of change. Then, the results of the application of this technique to students of on two classes of elementary and secondary school that visited the exhibition "The Evolution of Darwin" (Fundação Calouste Gulbenkian, 2009) are presented. Whereas the quantitative analysis of PMM allows us to assess the degree of transformation in student s' knowledge
Educational games are fun teaching tools prepared in line with the aims of the lessons and facilitate the understanding of the subjects. Due to these features, they can be used in science centers to both discover exhibits and provide understanding of concepts. In this study, it was aimed to determine the opinions about the educational games prepared for the science center. For this purpose, the games prepared for Kocaeli Science Center were played and the opinions of the pre-service teachers who played the games were determined. The study is a phenomenological research in which the opinions of the participants are investigated. The study group consists of 30 pre-service science teachers. The participants played the games prepared in relation to science lesson subjects in groups. After playing the games, their opinions on playing in the science center, the effect of playing in the science center on learning, discovering the science center and exhibits were asked based on their experiences. The data were collected through a form with open-ended questions and observations. The collected data were analyzed using the qualitative content analysis. The pre-service teachers emphasized that they understood the subjects in the exhibits better. They stated that the science center visits supported by educational games will positively affect learning. Based on the results of the research, it can be said that educational games should be among the educational tools that can be used to discover the science center and to understand the exhibits.
Young children have a nascent capacity to understand how to control variables to uncover causal structure. However, they do not always explore their environment in a way that generates appropriate data for causal learning. Here, we consider a potential reason that children do not explore systematically: whether the nature of their first action influences their subsequent play. Four- to 7-year-olds (N = 105) saw ambiguous data about a novel causal system and were then allowed to explore this system through play. The nature of their first action – specifically whether it varied only one potential cause and whether it was efficacious – affected their subsequent exploration, particularly whether they generated the data necessary to learn the causal structure. But generating these data, independent of their first action, related to whether they disambiguated the data they had initially observed. These results suggest that the extent to which children can learn from their own exploration in play might depend on how that play unfolds.
Full-text available
Esta dissertação teve como objetivos caracterizar os inscritos, avaliar a satisfação dos participantes e validar o questionário pós-atividade do “Mais Perto das Estrelas”. Esta atividade do Planetário do Porto – Centro Ciência Viva é composta por uma breve apresentação do céu dentro da cúpula do planetário, seguida de observação com telescópios. Desde o outono de 2018, a atividade passou a ser por inscrição, o que permitiu o envio de questionários de satisfação pós-atividade aos participantes. Com os dados dos inscritos e dos questionários pós-atividade aos participantes, fez-se análise de estatística descritiva, análise fatorial e calculou-se o 𝛂 de Cronbach. A análise às inscrições revelou que o tempo de preenchimento das vagas é curto, com a maioria dos indivíduos a ser do sexo feminino e com a generalidade a ter idades até aos 49 anos. Os dados revelaram que, globalmente, a satisfação dos participantes foi muito boa, embora a satisfação com a componente dentro da cúpula do planetário tenha sido superior à da observação com telescópios. A grande maioria dos participantes avaliou o interesse pela atividade, a clareza desta e a aquisição de novos conhecimentos nas categorias mais altas. As seis questões (relativas à apresentação na cúpula, à observação com telescópios, à apreciação global, ao interesse, à clareza e à aquisição de novos conhecimentos) apresentam estrutura unifatorial e boa fiabilidade. Recorrendo à metodologia de Investigação-Ação, fez-se uma análise crítica e validação por peritos do questionário. O questionário usado foi sujeito a três rondas de avaliação, que levou à criação de um novo questionário validado. Futuramente este questionário validado será disponibilizado em acesso aberto, de forma a que outras instituições com atividades similares possam beneficiar deste instrumento de avaliação. Quando as restrições provocadas pela pandemia da Covid-19 o permitirem, o questionário validado também será aplicado aos participantes do “Mais Perto das Estrelas”. The objective of this work was to characterize the registered individuals, evaluate the satisfaction of the participants and validate the post-activity questionnaire of “Mais Perto das Estrelas” (Closer to the Stars). “Mais Perto das Estrelas” is a Porto Planetarium activity, which consists of a brief night sky presentation inside the planetarium dome, followed by telescope observation. In the Autumn of 2018, this activity became available only by registration, which allowed the sending of post-activity satisfaction surveys to participants. With data collected from the registration and the post-activity questionnaires, a descriptive statistical analysis, a factorial analysis and calculation of Cronbach’s 𝛂 was performed. An analysis of the registration data revealed that the time needed to fill all vacancies is short, with the majority of the individuals being female, and mostly having less than 49 years old. The data revealed that participants' global satisfaction was very good, although their satisfaction with the component inside the dome was higher than that of the telescope observation. Most participants rated in the top categories the activitie’s interest, clarity and the acquisition of new knowledge. These six quantitative questions (about the presentation inside the dome, telescope observation, global appreciation, interest, clarity and acquisition of new knowledge) showed a unifactorial structure and good reliability. A critical analysis and expert validation of the questionnaire was performed using the Action-Research methodology. The questionnaire was subjected to three evaluation rounds, which led to a new, validated questionnaire. In the future, this validated questionnaire will be made available in open access, so other institutions with similar activities can benefit from this evaluation instrument. When the restrictions enforced by the Covid-19 pandemic allow it, the validated questionnaire will also be sent to the participants of “Mais Perto das Estrelas”.
This article presents Active Prolonged Engagement eXpanded (APEX), a framework and toolkit for informing evidence-based decisions about the iterative design of embodied, collaborative museum exhibits. We provide an overview of APEX, a framework that builds on both prior work and experimentally derived data to provide an understanding of how visitors' physical, social, emotional, and intellectual engagement transform during the course of their interaction with an exhibit. We present two case studies demonstrating how to apply APEX in practice, analyzing video recordings of participant interactions with different design iterations of TuneTable-an interactive exhibit for co-creative computational music-making-at both a macro- and micro-level. In the case studies, we explore how APEX reveals important features of participant interaction that suggest implications and directions for design. Finally, we present a toolkit of resources to aid researchers in operationalizing APEX as a framework for video analysis, in-situ observation, and iterative design and evaluation.
This project examines how the order of messaging during parent–child interactions at a museum exhibit affects children’s engagement with the exhibit. Parents and 4-7-year-olds (N = 64) played at a circuit block exhibit. They were first given blocks with descriptive (e.g., “This is a battery.”) or discovery-prompting (e.g., “There is no wrong way to play.”) messages, and after 90 seconds, given more blocks with the other message type. Children who received discovery-prompting messages second – after being allowed to explore the affordances of the circuit blocks with the descriptive messages – played at the exhibit longer, and participated in more circuit-building challenges on their own. Parents were also sensitive to the order of the messaging; it related to the ways in which they interacted with their children at the exhibit. We conclude by considering how the timing of messages families receive at an exhibit relates to the way they engage with the exhibit. Supplemental data for this article is available online at .
In this study, 178 groups of visitors were interviewed and recorded during their visits to museums. Three clusters of elements were shown to influence learning: the identity of the visitors, their response to the learning environment, and their explanatory engagement during the visit. A structural equation model using these variables fit well. Further examination revealed that not all conversational behavior was supportive of learning; some actions, such as making frequent personal connections, were detrimental to learning; additionally, silent contemplation was modestly associated with learning. This paper discusses these findings through the experiences of four couples whose outcome measures placed them at the extreme high or low end of the learning distribution.
The use of the phrase 'the public understanding of science' has been under attack for some time because of its incompatibility with modern theories of learning. In an attempt to find a more acceptable model, interviews were conducted at Questacon, the Australian National Science and Technology Centre, with visitors of a wide range of ages who had used the interactive exhibits there. The study showed that, when using an exhibit, a visitor has a reminding of a similar experience that forms the basis for interpretating the exhibit. An individual's existing 'personal awareness of science and technology' (PAST) draws on this prior experience to produce an understanding of the exhibit and, to some extent, an understanding of the underlying scientific model. A model for PAST embracing these factors is proposed and is used to interpret the learning resulting from interaction with such exhibits and other kinds of science-based experience.