Fluid Grouping: Quantifying Group Engagement around Interactive Tabletop Exhibits in the Wild

CHI, pp. 867-876, 2015.

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Keywords:
group and organization interfacesquantitative methodsmuseumslearningmulti-touch tabletops
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As demonstrated in this paper, a systematic and meaningful definition of what constitutes a group in a naturalistic flow of visitors is crucial for the quantitative analysis of engagement and interaction around interactive tabletops in museums

Abstract:

Interactive surfaces are increasingly common in museums and other informal learning environments where they are seen as a medium for promoting social engagement. However, despite their increasing prevalence, we know very little about factors that contribute to collaboration and learning around interactive surfaces. In this paper we presen...More

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Introduction
  • One of the cornerstones of multi-touch technology is its ability to support simultaneous interaction between colocated users.
  • Several research studies have established that multi-touch technology has the potential to engage visitors in fruitful collaborative learning [14, 15, 16, 25, 31].
  • Many of these studies are based on qualitative analysis.
  • The type of application and its user interface can potentially influence visitor engagement
Highlights
  • One of the cornerstones of multi-touch technology is its ability to support simultaneous interaction between colocated users
  • Several research studies have established that multi-touch technology has the potential to engage visitors in fruitful collaborative learning [14, 15, 16, 25, 31]
  • Little quantitative evidence exists that explains clearly the factors contributing to visitor engagement and learning around interactive surfaces
  • Studies of group engagement require a systematic definition of groups in these fluid settings as the behavior of individuals around an exhibit will be influenced by other people present
  • We present an empirical study of group engagement at the California Academy of Sciences in San Francisco, which receives 2 million annual visitors, and has a very diverse demographic audience
  • A few visitors (10 out of 629) spend extremely long periods of time (15+ minutes) at the exhibit, overlapping with multiple groups. Such visitors, who we refer to as Connectors, are identified as groups of size one by our algorithm as there is no other visitor with whom they have spent more than 50% of their time
  • As demonstrated in this paper, a systematic and meaningful definition of what constitutes a group in a naturalistic flow of visitors is crucial for the quantitative analysis of engagement and interaction around interactive tabletops in museums
Methods
  • Research methodology matters

    Throughout the last two sections the authors have established significant differences between the two study types.
  • In the Video study, the effects on dwell time of group size, age composition, and social engagement were not significant, while more overlap between groups was associated with lower dwell times.
  • These findings are exactly opposite of the Naturalistic datasets.
  • Be cautious about using videotaped data to draw quantitative conclusions about dwell time or engagement
Results
  • If the authors aggregate engagement measures on the group level, the authors need to exclude collaborators as by definition, they spend less than 50% of their time with the group and their experiences are not representative of the group.
  • A few visitors (10 out of 629) spend extremely long periods of time (15+ minutes) at the exhibit, overlapping with multiple groups.
  • Such visitors, who the authors refer to as Connectors, are identified as groups of size one by the algorithm as there is no other visitor with whom they have spent more than 50% of their time.
  • For the Video data, there is a significant negative correlation between Overlap Ratio and dwell time, meaning groups stayed longer the less they overlapped with other groups
Conclusion
  • Even though the Video methodology encourages a natural flow of visitors and interaction around the table by allowing subjects who had given their consent to come and go as they pleased, there were a series of significant differences compared to the Naturalistic data.
  • The authors' algorithm is based on a definition of shared experience as a metric for grouping
  • The authors chose this metric based on formal and informal observations of group engagement the authors conducted throughout the two year development process of both exhibits.
  • The authors hope that the proposed grouping algorithm as well as the analysis of overlap and group consistency will benefit future studies of group interactions in public spaces
Summary
  • Introduction:

    One of the cornerstones of multi-touch technology is its ability to support simultaneous interaction between colocated users.
  • Several research studies have established that multi-touch technology has the potential to engage visitors in fruitful collaborative learning [14, 15, 16, 25, 31].
  • Many of these studies are based on qualitative analysis.
  • The type of application and its user interface can potentially influence visitor engagement
  • Methods:

    Research methodology matters

    Throughout the last two sections the authors have established significant differences between the two study types.
  • In the Video study, the effects on dwell time of group size, age composition, and social engagement were not significant, while more overlap between groups was associated with lower dwell times.
  • These findings are exactly opposite of the Naturalistic datasets.
  • Be cautious about using videotaped data to draw quantitative conclusions about dwell time or engagement
  • Results:

    If the authors aggregate engagement measures on the group level, the authors need to exclude collaborators as by definition, they spend less than 50% of their time with the group and their experiences are not representative of the group.
  • A few visitors (10 out of 629) spend extremely long periods of time (15+ minutes) at the exhibit, overlapping with multiple groups.
  • Such visitors, who the authors refer to as Connectors, are identified as groups of size one by the algorithm as there is no other visitor with whom they have spent more than 50% of their time.
  • For the Video data, there is a significant negative correlation between Overlap Ratio and dwell time, meaning groups stayed longer the less they overlapped with other groups
  • Conclusion:

    Even though the Video methodology encourages a natural flow of visitors and interaction around the table by allowing subjects who had given their consent to come and go as they pleased, there were a series of significant differences compared to the Naturalistic data.
  • The authors' algorithm is based on a definition of shared experience as a metric for grouping
  • The authors chose this metric based on formal and informal observations of group engagement the authors conducted throughout the two year development process of both exhibits.
  • The authors hope that the proposed grouping algorithm as well as the analysis of overlap and group consistency will benefit future studies of group interactions in public spaces
Tables
  • Table1: Differences in experienced engagement behaviors between Video and Naturalistic study (Mann-Whitney U)
  • Table2: Differences in social engagement in the Naturalistic data between children only groups and mixed groups (Mann-Whitney U)
  • Table3: Differences in social engagement in the Naturalistic data between mixed groups and adult only groups (Mann-Whitney U)
  • Table4: Engagement measures that significantly correlate with dwell time across both applications in the Naturalistic study (Spearman’s rho)
Download tables as Excel
Related work
  • Various factors influence group engagement around traditional exhibits. These include the adult and child gender [11, 21, 25], a group’s size and age composition [4, 7, 8, 10, 20], and a group’s strategy for learning [3]. However, it is unclear if any of the observed effects apply to interactive multi-touch exhibits, which may facilitate different forms of group engagement. Previous HCI research has studied group engagement around interactive tabletops in general [1, 13, 18, 27, 28, 29, 30, 31, 33, 35, 36, 37] and in museums in particular [14, 15, 16, 25, 31]. This includes understanding which gestures are used, how visitors approach surfaces in public spaces, transitions between user groups, and physical and verbal interaction between users [13, 14, 16, 18, 30].

    While many “in the wild” studies have assessed engagement with multi-touch technology, the great majority primarily concentrate on qualitative analyses of observational data [1, 13, 14, 15, 16, 18, 25, 27, 31]. Quantitative analyses of group engagement are less common, particularly for studies that have been conducted outside of laboratory or controlled settings. Peltonen et al measured distribution of group size and group overlap in front of a public interactive surface located in a shop window [30]; Horn et al quantified holding times of recruited and non-recruited groups around an interactive game-based exhibit [15]; and Hinrichs et al quantified occurrences of various multi-touch gestures around an interactive museum exhibit [14]. Other quantitative studies either focus on quantifying engagement around traditional, non-digital exhibits [9, 12, 20, 23] or are conducted in the lab with predetermined group sizes [7, 29, 33, 35, 36, 37].
Funding
  • This work is partially supported by the National Science Foundation (DRL1010889)
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