PathViewer: Visualizing Pathways Through Student Data
Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems, page 960--964. New York, NY, USA, ACM, (2017)DOI: 10.1145/3025453.3025819
Abstract
Analysis of student data is critical for improving education. In particular, educators need to understand what approaches their students are taking to solve a problem. However, identifying student strategies and discovering areas of confusion is difficult because an educator may not know what queries to ask or what patterns to look for in the data. In this paper, we present a visualization tool, PathViewer, to model the paths that students follow when solving a problem. PathViewer leverages ideas from flow diagrams and natural language processing to visualize the sequences of intermediate steps that students take. Using PathViewer, we analyzed how several students solved a Python assignment, discovering interesting and unexpected patterns. Our results suggest that PathViewer can allow educators to quickly identify areas of interest, drill down into specific areas, and identify student approaches to the problem as well as misconceptions they may have.
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