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

Yingbin Zhang, Luc Paquette and Nigel Bosch
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Yingbin Zhang: South China Normal University
Luc Paquette: University of Illinois at Urbana-Champaign
Nigel Bosch: University of Illinois at Urbana-Champaign

Journal of Educational and Behavioral Statistics, 2025, vol. 50, issue 3, 387-419

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.

Keywords: sequential pattern mining; permutation test; event sequence; behavioral pattern (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:sae:jedbes:v:50:y:2025:i:3:p:387-419

DOI: 10.3102/10769986241248772

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