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

An Agent-based Modeling Framework for Learning Progression Research in Middle School Mathematics Curriculum | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article An Agent-based Modeling Framework for Learning Progression Research in Middle School Mathematics Curriculum Zhenfeng Jiang, Aidah Abdul Karim, Fariza Khalid, Hongchun Qu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5286639/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 05 Dec, 2025 Read the published version in International Journal of Artificial Intelligence in Education → Version 1 posted 11 You are reading this latest preprint version Abstract Large-scale experimental studies on Learning Progression (LP) in middle school mathematics face challenges, such as resource limitations and ethical considerations. This study introduces a simulation-based framework for LP exploration, centered on the Multi-Agent-Based Student Cognitive Development (MAB-SCD) model. The MAB-SCD model, built using Agent-Based Modeling (ABM), integrates student learning processes and cognitive development into coherent learning trajectories. It was conceptualized around the LP construction process and key instructional activities in middle school mathematics, using the BDI cognitive framework for design and implementation. A systematic verification process was conducted to ensure its suitability for LP research. Global sensitivity analysis revealed complex parameter interactions, providing insights into model dynamics and enabling simulation optimization to more accurately represent student learning experiences. Historical data were used for parameter tuning and validation, ensuring the alignment between model outputs and real-world observations. Calibration and validation results confirmed the model’s effectiveness in reflecting students' progress and cognitive development. Additionally, the model's validity was demonstrated in a typical LP research task, showing effective integration of cognitive processes with learning trajectories. Positioned at the intersection of cognitive architecture and educational theory, these findings offer actionable insights for educators and researchers. By promoting the use of computational simulations, this study enhances the understanding of mathematics learning progressions across large student populations over extended periods. Educational simulation Learning Progression Agent-based Modelling Cognitive Development BDI cognitive framework Full Text Additional Declarations No competing interests reported. Supplementary Files AppendixA.docx AppendixB.docx AppendixC.docx Cite Share Download PDF Status: Published Journal Publication published 05 Dec, 2025 Read the published version in International Journal of Artificial Intelligence in Education → Version 1 posted Editorial decision: Revision requested 31 Mar, 2025 Reviews received at journal 20 Mar, 2025 Reviews received at journal 26 Feb, 2025 Reviews received at journal 19 Feb, 2025 Reviewers agreed at journal 11 Feb, 2025 Reviewers agreed at journal 10 Feb, 2025 Reviewers agreed at journal 26 Jan, 2025 Reviewers invited by journal 28 Dec, 2024 Editor assigned by journal 21 Oct, 2024 Submission checks completed at journal 21 Oct, 2024 First submitted to journal 18 Oct, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5286639","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":368401394,"identity":"d371ad45-b626-49a7-970d-d592313b23b6","order_by":0,"name":"Zhenfeng 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