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Using Permutation Tests to Identify Statistically Sound and Nonredundant Sequential Patterns in Educational Event Sequences

Author

Listed:
  • Yingbin Zhang

    (South China Normal University)

  • Luc Paquette

    (University of Illinois at Urbana-Champaign)

  • Nigel Bosch

    (University of Illinois at Urbana-Champaign)

Abstract

Frequent sequential pattern mining is a valuable technique for capturing the relative arrangement of learning events, but current algorithms often return excessive learning event patterns, many of which may be noise or redundant. These issues exacerbate researchers’ burden when interpreting the patterns to derive actionable insights into learning processes. This study proposed permutation tests for identifying sequential patterns whose occurrences are statistically significantly greater than the chance value and different from their superpatterns. Simulations demonstrated that the test for detecting sound patterns had a low false discovery rate and high power, while the test for detecting nonredundant patterns also showed a high accuracy. Empirical data analyses found that the patterns detected in training data were generalizable to test data.

Suggested Citation

  • Yingbin Zhang & Luc Paquette & Nigel Bosch, 2025. "Using Permutation Tests to Identify Statistically Sound and Nonredundant Sequential Patterns in Educational Event Sequences," Journal of Educational and Behavioral Statistics, , vol. 50(3), pages 387-419, June.
    • Handle: RePEc:sae:jedbes:v:50:y:2025:i:3:p:387-419
    DOI: 10.3102/10769986241248772
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