Analyzing Students’ Problem-Solving Sequences

Educational technology is shifting toward facilitating personalized learning. Such personalization, however, requires a detailed understanding of students’ problem-solving processes. Sequence analysis (SA) is a promising approach to gaining granular insights into student problem solving; however, existing techniques are difficult to interpret because they offer little room for human input in the analysis process. Ultimately, in a learning context, a human stakeholder makes the decisions, so they should be able to drive the analysis process. In this paper, we present a human-in-the-loop approach to SA that uses visualization to allow a stakeholder to better understand both the data and the algorithm. We illustrate the method with a case study in the context of a learning game called Parallel. Results reveal six groups of students organized based on their problem-solving patterns and highlight individual differences within each group. We compare the results to a state-of-the-art method run with the same data and discuss the benefits of our method and the implications of this work.