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A driving factor in designing interactive museum exhibits to support simultaneous users is that visitors learn from one another, via both observation and conversation. Such collaborative interactions among museum-goers are typically analyzed through manual coding of live- or video-recorded exhibit use. We sought to determine how log data from an interactive multi-user exhibit could indicate patterns in visitor interactions that could shed light on informal collaborative constructivist learning. We characterized patterns from log data generated by an interactive tangible tabletop exhibit using factors like "pace of activity" and the timing of “success events." Here we describe processes for parsing and visualizing log data and explore what these processes revealed about individual and group interactions with interactive museum exhibits. Using clustering techniques to categorize museum-goer behavior and heat maps to visualize patterns in the log data, we found that there were distinct trends in how users approached solving the exhibit: some players seemed more reflective while others seemed more achievement oriented. We also found that the most productive sessions occurred when all four areas of the table were occupied, suggesting that the activity design had a desired outcome to promote collaborative activity.
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Alfredo, V., Félix, C., & Àngela, N. (2010). Clustering Educational Data. In C. Romero, S. Ventura, M. Pechenizkiy & R. S. J. d. Baker (Eds.), Handbook of Educational Data Mining (pp. 75-92). Boca Raton, FL: CRC Press. https://doi.org/10.1201%2Fb10274-8
Amershi, S., & Conati, C. (2009). Combining unsupervised and supervised classification to build user models for exploratory learning environments. JEDM| Journal of Educational Data Mining, 1(1), 18-71.
Berland, M., Martin, T., Benton, T., Petrick Smith, C., & Davis, D. (2013). Using learning analytics to understand the learning pathways of novice programmers. Journal of the Learning Sciences, 22(4), 564-599. https://doi.org/10.1080%2F10508406.2013.836655
Blikstein, P. (2013, April). Multimodal learning analytics. In Proceedings of the third international conference on learning analytics and knowledge (pp. 102-106). ACM. https://doi.org/10.1145%2F2460296.2460316
Block, F., Hammerman, J., Horn, M., Spiegel, A., Christiansen, J., Phillips, B., ... & Shen, C. (2015, April). Fluid grouping: Quantifying group engagement around interactive tabletop exhibits in the wild. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems (pp. 867-876). ACM. https://doi.org/10.1145%2F2702123.2702231
Bowers, A. J. (2010). Analyzing the longitudinal K-12 grading histories of entire cohorts of students: Grades, data-driven decision making, dropping out and hierarchical cluster analysis. Practical Assessment, Research, and Evaluation, 15(1), 7.
Davis, P., Horn, M. S., Schrementi, L., Block, F., Phillips, B., Evans, E. M., ... & Shen, C. (2013). Going deep: Supporting collaborative exploration of evolution in natural history museums. In Proceedings of 10th International Conference on Computer Supported Collaborative Learning.
Evans, A. C., Wobbrock, J. O., & Davis, K. (2016, February). Modeling collaboration patterns on an interactive tabletop in a classroom setting. In Proceedings of the 19th ACM Conference on Computer-Supported Cooperative Work & Social Computing (pp. 860-871).
Lee, J. E., Recker, M., Bowers, A., & Yuan, M. (2016, June). Hierarchical Cluster Analysis Heatmaps and Pattern Analysis: An Approach for Visualizing Learning Management System Interaction Data. In EDM (pp. 603-604).
Long, D., McKlin, T., Weisling, A., Martin, W., Guthrie, H., & Magerko, B. (2019). Trajectories of physical engagement and expression in a co-creative museum installation. In Proceedings of the 2019 on Creativity and Cognition (pp. 246-257). https://doi.org/10.1145%2F3325480.3325505
Lyons, L. (2014). Exhibiting data: Using body-as-interface designs to engage visitors with data visualizations. In Learning Technologies and the Body (pp. 197-212). Routledge.
Lyons, L., Tissenbaum, M., Berland, M., Eydt, R., Wielgus, L., & Mechtley, A. (2015, June). Designing visible engineering: supporting tinkering performances in museums. In Proceedings of the 14th International Conference on Interaction Design and Children (pp. 49-58). https://doi.org/10.1145%2F2771839.2771845
Martin, K., Horn, M., & Wilenksy, U. (2018). Ant Adaptation: A complex interactive multitouch game about ants designed for museums. In Constructionism Conference.
Martínez Maldonado, R., Kay, J., & Yacef, K. (2010, November). Collaborative concept mapping at the tabletop. In ACM International Conference on Interactive Tabletops and Surfaces (pp. 207-210). ACM. https://doi.org/10.1145%2F1936652.1936690
Roberts, J., Banerjee, A., Hong, A., McGee, S., Horn, M., & Matcuk, M. (2018, April). Digital exhibit labels in museums: promoting visitor engagement with cultural artifacts. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems (pp. 1-12). https://doi.org/10.1145%2F3173574.3174197
Roberts, J., & Lyons, L. (2017). The value of learning talk: applying a novel dialogue scoring method to inform interaction design in an open-ended, embodied museum exhibit. International Journal of Computer-Supported Collaborative Learning, 12(4), 343-376. https://doi.org/10.1007%2Fs11412-017-9262-x
Romesburg, H. C. (1984). Cluster analysis for researchers. Lifetime Learning Publications.
Snibbe, A. C. (2006). Drowning in data. Stanford Social Innovation Review, 4(3), 39-45.
Tissenbaum, M., Berland, M., & Lyons, L. (2017). DCLM framework: understanding collaboration in open-ended tabletop learning environments. International Journal of Computer-Supported Collaborative Learning, 12(1), 35-64. https://doi.org/10.1007%2Fs11412-017-9249-7
Tissenbaum, M., Kumar, V., & Berland, M. (2016). Modeling Visitor Behavior in a Game-Based Engineering Museum Exhibit with Hidden Markov Models. International Educational Data Mining Society.
Upton, K., & Kay, J. (2009, June). Narcissus: group and individual models to support small group work. In International Conference on User Modeling, Adaptation, and Personalization (pp. 54-65). Springer, Berlin, Heidelberg. https://doi.org/10.1007%2F978-3-642-02247-0_8
Wilkinson, L., & Friendly, M. (2009). The History of the Cluster Heat Map. The American Statistician, 63(2), 179-184. https://doi.org/10.1198%2Ftas.2009.0033
Yoon, S. A., Elinich, K., Wang, J., Steinmeier, C., & Tucker, S. (2012). Using augmented reality and knowledge-building scaffolds to improve learning in a science museum. International Journal of Computer-Supported Collaborative Learning, 7(4), 519–541. https://doi.org/10.1007%2Fs11412-012-9156-x