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Using Sequence Mining Techniques for Understanding Incorrect Behavioral Patterns on Interactive Tasks

Author

Listed:
  • Esther Ulitzsch

    (IPN—Leibniz Institute for Science and Mathematics Education)

  • Qiwei He

    (6729Educational Testing Service)

  • Steffi Pohl

    (9166Freie Universität Berlin)

Abstract

Interactive tasks designed to elicit real-life problem-solving behavior are rapidly becoming more widely used in educational assessment. Incorrect responses to such tasks can occur for a variety of different reasons such as low proficiency levels, low metacognitive strategies, or motivational issues. We demonstrate how behavioral patterns associated with incorrect responses can, in part, be understood, supporting insights into the different sources of failure on a task. To this end, we make use of sequence mining techniques that leverage the information contained in time-stamped action sequences commonly logged in assessments with interactive tasks for (a) investigating what distinguishes incorrect behavioral patterns from correct ones and (b) identifying subgroups of examinees with similar incorrect behavioral patterns. Analyzing a task from the Programme for the International Assessment of Adult Competencies 2012 assessment, we find incorrect behavioral patterns to be more heterogeneous than correct ones. We identify multiple subgroups of incorrect behavioral patterns, which point toward different levels of effort and lack of different subskills needed for solving the task. Albeit focusing on a single task, meaningful patterns of major differences in how examinees approach a given task that generalize across multiple tasks are uncovered. Implications for the construction and analysis of interactive tasks as well as the design of interventions for complex problem-solving skills are derived.

Suggested Citation

  • Esther Ulitzsch & Qiwei He & Steffi Pohl, 2022. "Using Sequence Mining Techniques for Understanding Incorrect Behavioral Patterns on Interactive Tasks," Journal of Educational and Behavioral Statistics, , vol. 47(1), pages 3-35, February.
    • Handle: RePEc:sae:jedbes:v:47:y:2022:i:1:p:3-35
    DOI: 10.3102/10769986211010467
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    References listed on IDEAS

    as
    1. Chen, Yunxiao & Li, Xiaoou & Liu, Jingchen & Ying, Zhiliang, 2019. "Statistical analysis of complex problem-solving process data: an event history analysis approach," LSE Research Online Documents on Economics 100871, London School of Economics and Political Science, LSE Library.
    2. Xueying Tang & Zhi Wang & Qiwei He & Jingchen Liu & Zhiliang Ying, 2020. "Latent Feature Extraction for Process Data via Multidimensional Scaling," Psychometrika, Springer;The Psychometric Society, vol. 85(2), pages 378-397, June.
    3. Esther Ulitzsch & Qiwei He & Vincent Ulitzsch & Hendrik Molter & André Nichterlein & Rolf Niedermeier & Steffi Pohl, 2021. "Combining Clickstream Analyses and Graph-Modeled Data Clustering for Identifying Common Response Processes," Psychometrika, Springer;The Psychometric Society, vol. 86(1), pages 190-214, March.
    Full references (including those not matched with items on IDEAS)

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