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Analyzing Students’ Problem-Solving Sequences: A Human-in-the-Loop Approach
Journal article   Open access   Peer reviewed

Analyzing Students’ Problem-Solving Sequences: A Human-in-the-Loop Approach

Erica Kleinman, Murtuza Shergadwala, Magy Seif El-Nasr, Zhaoqing Teng, Jennifer Villareale, Andy Bryant and Jichen Zhu
Journal of Learning Analytics, pp 1-23
04 Jun 2022
DOI: https://doi.org/10.18608/jla.2022.7465
Featured in Collection :   UN Sustainable Development Goals @ Drexel
url
https://doi.org/10.18608/jla.2022.7465View
Published, Version of Record (VoR)CC BY-NC-ND V4.0,  Open

Abstract

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.

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13 citations in Web of Science
14 citations in Scopus

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UN Sustainable Development Goals (SDGs)

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#4 Quality Education

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Web of Science research areas
Education & Educational Research
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