Uncovering Patterns in Constructionist Collaborative Learning Activities via Cluster Analysis of Museum Exhibit Log Files

Main Article Content

Natalie Jorion
Jessica Roberts
Alex Bowers
Mike Tissenbaum
Leilah Lyons
Vishesh Kumar
Matthew Berland

Abstract

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. 

Article Details

How to Cite
Jorion, N., Roberts, J., Bowers, A., Tissenbaum, M., Lyons, L., Kumar, V., & Berland, M. (2020). Uncovering Patterns in Constructionist Collaborative Learning Activities via Cluster Analysis of Museum Exhibit Log Files. Frontline Learning Research, 8(6), 77 - 87. https://doi.org/10.14786/flr.v8i6.597
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Articles
Author Biography

Natalie Jorion, PSI Services, LLC, USA

Natalie Jorion is a psychometrician interested in innovative item types, text mining, and learning analytics.  She has developed Shiny apps for DIF panels and Standard Settings. She has a PhD in learning sciences from the University of Illinois at Chicago with a specialization in measurement and assessment, and an MA from Northwestern University.  For her dissertation, she designed an evidence-based diagnostic assessment of conceptual understanding and common misunderstandings in statistics. In 2016, she was awarded an NSF data science fellowship to create visualizations of learner interactions in informal multimodal STEM games.

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