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April 1, 2026

Modelling strategic patterns in high school students’ mathematical problem solving: an AI-based approach integrating simulated data and clustered process analysis

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Authors

SQShengyu QiaoZCZhuoyun (Andy) Cao

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Overview

Investigates mathematical problem-solving strategies in students, revealing distinct profiles for personalized instruction.

Key Points

  • The aim is to identify and cluster high school students' strategies in solving number-pattern problems using AI techniques.
  • Generated synthetic student data to simulate different problem-solving strategies.
  • Applied clustering algorithms to real-world student log data from ASSISTments.
  • Evaluated cluster quality using Silhouette and Adjusted Rand Index scores.
  • Identified five distinct student strategy profiles from both simulated and real datasets.
  • Consistent core patterns were found, including efficient solvers, guessers, and help-seekers.
  • Some real data clusters revealed previously unobserved strategies.

Cite This Study

Qiao et al. (2026) studied this question.

synapsesocial.com/papers/69ccb7b016edfba7beb89cfehttps://doi.org/10.1108/lfet-08-2025-0096
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