Abstract
Research has shown that supporting tinkering and exploration promotes a wide range of STEM related literacies. However, the open-endedness of tinkering environments makes it difficult to know whether learners’ exploration is productive or not. This is especially true in museum spaces, where dwell times are short and facilitators lack a history of engagement with individual visitors. In response, this study uses telemetry data from Oztoc – an open-ended exploratory tabletop exhibit in which visitors embody the roles of engineers who are tasked with attracting and cataloging newly discovered aquatic creatures by building working electronic circuits. This data is used to build Hidden Markov Models (HMMs) to devise an automated scheme of identifying when a visitor is behaving productively or unproductively. Evaluation of our HMM was shown to effectively discern when visitors were productively and unproductively engaging with the exhibit. Using a Markov model, we identify common patterns of visitor movement from unproductive to productive states to shed light on how visitors struggle and the moves they made to overcome these struggles. These findings offer considerable promise for understanding how learners productively and unproductively persevere in open-ended exploratory environments and the potential for developing real time supports to help facilitators know how and when to best engage with visitors.
| Original language | English (US) |
|---|---|
| Pages | 517-522 |
| Number of pages | 6 |
| State | Published - Jan 1 2016 |
| Event | 9th International Conference on Educational Data Mining, EDM 2016 - Raleigh, United States Duration: Jun 29 2016 → Jul 2 2016 |
Conference
| Conference | 9th International Conference on Educational Data Mining, EDM 2016 |
|---|---|
| Country | United States |
| City | Raleigh |
| Period | 6/29/16 → 7/2/16 |
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Keywords
- Interactive tabletops modeling
- Learning analytics
- Museums
ASJC Scopus subject areas
- Computer Science Applications
- Information Systems
Cite this
Modeling visitor behavior in a game-based engineering museum exhibit with hidden Markov models. / Tissenbaum, Mike; Kumar, Vishesh; Berland, Matthew.
2016. 517-522 Paper presented at 9th International Conference on Educational Data Mining, EDM 2016, Raleigh, United States.Research output: Contribution to conference › Paper
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TY - CONF
T1 - Modeling visitor behavior in a game-based engineering museum exhibit with hidden Markov models
AU - Tissenbaum, Mike
AU - Kumar, Vishesh
AU - Berland, Matthew
PY - 2016/1/1
Y1 - 2016/1/1
N2 - Research has shown that supporting tinkering and exploration promotes a wide range of STEM related literacies. However, the open-endedness of tinkering environments makes it difficult to know whether learners’ exploration is productive or not. This is especially true in museum spaces, where dwell times are short and facilitators lack a history of engagement with individual visitors. In response, this study uses telemetry data from Oztoc – an open-ended exploratory tabletop exhibit in which visitors embody the roles of engineers who are tasked with attracting and cataloging newly discovered aquatic creatures by building working electronic circuits. This data is used to build Hidden Markov Models (HMMs) to devise an automated scheme of identifying when a visitor is behaving productively or unproductively. Evaluation of our HMM was shown to effectively discern when visitors were productively and unproductively engaging with the exhibit. Using a Markov model, we identify common patterns of visitor movement from unproductive to productive states to shed light on how visitors struggle and the moves they made to overcome these struggles. These findings offer considerable promise for understanding how learners productively and unproductively persevere in open-ended exploratory environments and the potential for developing real time supports to help facilitators know how and when to best engage with visitors.
AB - Research has shown that supporting tinkering and exploration promotes a wide range of STEM related literacies. However, the open-endedness of tinkering environments makes it difficult to know whether learners’ exploration is productive or not. This is especially true in museum spaces, where dwell times are short and facilitators lack a history of engagement with individual visitors. In response, this study uses telemetry data from Oztoc – an open-ended exploratory tabletop exhibit in which visitors embody the roles of engineers who are tasked with attracting and cataloging newly discovered aquatic creatures by building working electronic circuits. This data is used to build Hidden Markov Models (HMMs) to devise an automated scheme of identifying when a visitor is behaving productively or unproductively. Evaluation of our HMM was shown to effectively discern when visitors were productively and unproductively engaging with the exhibit. Using a Markov model, we identify common patterns of visitor movement from unproductive to productive states to shed light on how visitors struggle and the moves they made to overcome these struggles. These findings offer considerable promise for understanding how learners productively and unproductively persevere in open-ended exploratory environments and the potential for developing real time supports to help facilitators know how and when to best engage with visitors.
KW - Interactive tabletops modeling
KW - Learning analytics
KW - Museums
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