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<p>Large-scale empirical studies on Learning Progressions (LPs) in middle school mathematics are often limited by resource and practical constraints. This study presents a simulation-based framework for LPs research, centered on the Multi-Agent-Based Student Cognitive Development (MAB-SCD) model. Developed using Agent-Based Modeling (ABM), the MAB-SCD model integrates student learning processes and cognitive development into structured learning trajectories. The model design follows the Belief-Desire-Intention (BDI) architecture, aligning with LPs construction principles and key instructional activities. To assess its suitability for LPs research, the model underwent systematic verification within the context of Chinese middle school mathematics. Global sensitivity analysis revealed complex parameter interactions, providing insights into model dynamics. These insights further supported simulation optimization to better represent student learning patterns. Calibration and validation with historical data indicated reasonable alignment between simulated outputs and real-world observations. Furthermore, simulation experiments demonstrated that the model effectively captures students’ learning progression and cognitive development. Although developed and tested within China’s educational context, the proposed framework and analytical methods may offer useful insights for LPs studies in other educational settings. This simulation-based framework enriches research methodologies in educational simulation and offers a potential tool for exploring learning progressions.</p>

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An Agent-based Modeling Framework for Learning Progression Research in Middle School Mathematics Curriculum

  • 期刊论文

  • 发表日期：

    2025年12月5日

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  • Zhenfeng Jiang,
  • Aidah Abdul Karim,
  • Fariza Khalid,
  • Hongchun Qu

摘要

Large-scale empirical studies on Learning Progressions (LPs) in middle school mathematics are often limited by resource and practical constraints. This study presents a simulation-based framework for LPs research, centered on the Multi-Agent-Based Student Cognitive Development (MAB-SCD) model. Developed using Agent-Based Modeling (ABM), the MAB-SCD model integrates student learning processes and cognitive development into structured learning trajectories. The model design follows the Belief-Desire-Intention (BDI) architecture, aligning with LPs construction principles and key instructional activities. To assess its suitability for LPs research, the model underwent systematic verification within the context of Chinese middle school mathematics. Global sensitivity analysis revealed complex parameter interactions, providing insights into model dynamics. These insights further supported simulation optimization to better represent student learning patterns. Calibration and validation with historical data indicated reasonable alignment between simulated outputs and real-world observations. Furthermore, simulation experiments demonstrated that the model effectively captures students’ learning progression and cognitive development. Although developed and tested within China’s educational context, the proposed framework and analytical methods may offer useful insights for LPs studies in other educational settings. This simulation-based framework enriches research methodologies in educational simulation and offers a potential tool for exploring learning progressions.