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

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Abstract

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.

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Data Availability

Data associated with this study have been deposited into Github repository, which can be accessed via the following link: [https://github.com/shouchenjiang/MAB-SCD-Model.git](https:/github.com/shouchenjiang/MAB-SCD-Model.git).

References

  • Alharbi, K. (2025). Agent-Based Simulation, Machine Learning, and Gamification: An Integrated Framework for Addressing Disruptive Behaviour and Enhancing Student and Teacher Performance in Educational Settings (Doctoral dissertation, Durham University).

  • Alharbi, K., Cristea, A. I., Shi, L., Tymms, P., & Brown, C. (2021). Agent-based classroom environment simulation: the effect of disruptive schoolchildren’s behaviour versus teacher control over neighbours. In I. Roll, D. McNamara, S. Sosnovsky, R. Luckin, & V. Dimitrova (Eds.), Proceedings of the 22nd International Conference on Artificial Intelligence in Education .48–53. Springer. https://doi.org/10.1007/978-3-030-78270-2_8

  • Alonzo, A. C., Wooten, M. M., & Christensen, J. (2022). Learning progressions as a simplified model: Examining teachers’ reported uses to inform classroom assessment practices. Science Education, 106(4), 852–889.

    Article  Google Scholar 

  • Ansya, Y. A. U., Alfianita, A., & Syahkira, H. P. (2024). Optimizing mathematics learning in fifth grades: The critical role of evaluation in improving student achievement and character. Progres Pendidikan, 5(3), 302–311.

    Article  Google Scholar 

  • Attali, Y., & Arieli-Attali, M. (2019). Validating classifications from learning progressions: Framework and implementation. ETS Research Report Series, 2019(1), 1–20.

    Article  Google Scholar 

  • Borgonovo, E., Pangallo, M., Rivkin, J., Rizzo, L., & Siggelkow, N. (2022). Sensitivity analysis of agent-based models: A new protocol. Computational and Mathematical Organization Theory, 28(1), 52–94.

    Article  Google Scholar 

  • Bourgais, M., Taillandier, P., & Vercouter, L. (2020). BEN: An architecture for the behavior of social agents. Journal of Artificial Societies and Social Simulation. https://doi.org/10.18564/jasss.4437

    Article  Google Scholar 

  • Brahier, D. J. (2020). Teaching secondary and middle school mathematics. Routledge.

  • Cai, Y., Tu, D., & Ding, S. (2013). A simulation study to compare five cognitive diagnostic models. Acta Psychologica Sinica, 45(11), 1295.

    Article  Google Scholar 

  • Caporale, N., & Dan, Y. (2008). Spike timing–dependent plasticity: A hebbian learning rule. Annual Review of Neuroscience, 31, 25–46.

    Article  Google Scholar 

  • Chen, F., Yan, Y., & Xin, T. (2017). Developing a learning progression for number sense based on the rule space model in China. Educational Psychology, 37(2), 128–144.

    Article  Google Scholar 

  • Chiriacescu, V., Soh, L. K., & Shell, D. F. (2013). Understanding human learning using a multi-agent simulation of the Unified Learning Model. In 2013 IEEE 12th International Conference on Cognitive Informatics and Cognitive Computing , 143–152. IEEE.

  • Clements, D. H., & Sarama, J. (2020). Learning and teaching early math: The learning trajectories approach. Routledge.

  • Confrey, J., Maloney, A., Shah, M., & Belcher, M. (2019). A synthesis of research on learning trajectories/progressions in mathematics. Future of education and skills 2030: Curriculum analysis.

  • Confrey, J., Toutkoushian, E., & Shah, M. (2020). Working at scale to initiate ongoing validation of learning trajectory-based classroom assessments for middle grade mathematics. The Journal of Mathematical Behavior, 60, 100818.

    Article  Google Scholar 

  • Cui, Y., & Leighton, J. P. (2009). The hierarchy consistency index: Evaluating person fit for cognitive diagnostic assessment. Journal of Educational Measurement, 46(4), 429–449.

    Article  Google Scholar 

  • Cutting, C., & Lowrie, T. (2023). Bounded learning progressions: A framework to capture young children’s development of mathematical activity in play-based contexts. Mathematics Education Research Journal, 35(2), 317–337.

    Article  Google Scholar 

  • De La Torre, J. (2009). DINA model and parameter estimation: A didactic. Journal of Educational and Behavioral Statistics, 34(1), 115–130.

    Article  Google Scholar 

  • Gallagher, M. A., Parsons, S. A., & Vaughn, M. (2022). Adaptive teaching in mathematics: A review of the literature. Educational Review, 74(2), 298–320.

    Article  Google Scholar 

  • Gao, Y., Zhai, X., Cui, Y., Xin, T., & Bulut, O. (2021). Re-validating a learning progression of buoyancy for middle school students: A longitudinal study. Research in Science Education. https://doi.org/10.1007/s11165-021-10021-x

    Article  Google Scholar 

  • Harris, L. R., Adie, L., & Wyatt-Smith, C. (2022). Learning progression–based assessments: A systematic review of student and teacher uses. Review of Educational Research, 92(6), 996–1040.

    Article  Google Scholar 

  • Howden, N., Rönnquist, R., Hodgson, A., & Lucas, A. (2001). JACK intelligent agents-summary of an agent infrastructure. In 5th International conference on autonomous agents (Vol. 6).

  • Iwanaga, T., Usher, W., & Herman, J. (2022). Toward SALib 2.0: Advancing the accessibility and interpretability of global sensitivity analyses. Socio-Environmental Systems Modelling, 4, 18155–18155.

    Article  Google Scholar 

  • Jin, H., Yan, D., & Krajcik, J. S. (Eds.). (2024). Handbook of research on science learning progressions. Routledge.

  • Kennedy, W. G. (2011). Modelling human behaviour in agent-based models. Agent-based models of geographical systems , 167–179. Springer Netherlands.

  • Koster, A., Koch, F., Assumpção, N., & Primo, T. (2016). The role of agent-based simulation in education. In Advances in Social Computing and Digital Education: 7th International Workshop on Collaborative Agents Research and Development, CARE 2016, Singapore, May 9, 2016 and Second International Workshop on Social Computing in Digital Education, SocialEdu 2016, Zagreb, Croatia, June 6, 2016, Revised Selected Papers 7 ,156–167. Springer International Publishing.

  • Krajcik, J., & Shin, N. (2023). Student Conceptions, conceptual Change, and learning progressions. Handbook of research on science education ,121–157. Routledge.

  • Lee, Y-W., & Sawaki, Y. (2009). Application of three cognitive diagnosis models to ESL reading and listening assessments. Language Assessment Quarterly, 6(3), 239–263.

    Article  Google Scholar 

  • Leighton, J. P., Gierl, M. J., & Hunka, S. M. (2004). The attribute hierarchy method for cognitive assessment: A variation on Tatsuoka’s rule-space approach. Journal of Educational Measurement, 41(3), 205–237.

    Article  Google Scholar 

  • Liu, X. X., Gong, S. Y., Zhang, H. P., Yu, Q. L., & Zhou, Z. J. (2021). Perceived teacher support and creative self-efficacy: The mediating roles of autonomous motivation and achievement emotions in Chinese junior high school students. Thinking Skills and Creativity, 39, 100752.

    Article  Google Scholar 

  • Ma, C., Ouyang, J., & Xu, G. (2023). Learning latent and hierarchical structures in cognitive diagnosis models. Psychometrika, 88(1), 175–207.

    Article  MathSciNet  MATH  Google Scholar 

  • Ministry of Education of the People’s Republic of China. (2022). Curriculum Standards for Compulsory Education Mathematics. Ministry of Education.

    Google Scholar 

  • Montes, G. (2012). Using artificial societies to understand the impact of teacher student match on academic performance: The case of same race effects. Journal of Artificial Societies and Social Simulation, 15(4), 8.

    Article  Google Scholar 

  • Morris, M. D. (1991). Factorial sampling plans for preliminary computational experiments. Technometrics, 33(2), 161–174.

    Article  Google Scholar 

  • National Research Council. (2007). Taking science to school: Learning and teaching science in grades K-8. National Academies.

  • Newell, A. (1990). Unified theories of cognition. Harvard University Press.

  • Ormazábal, I., Borotto, F. A., & Astudillo, H. F. (2021). An agent-based model for teaching–learning processes. Physica A: Statistical Mechanics and its Applications, 565, Article 125563.

    Article  Google Scholar 

  • Pellegrino, J. W., DiBello, L. V., & Goldman, S. R. (2016). A framework for conceptualizing and evaluating the validity of instructionally relevant assessments. Educational Psychologist, 51(1), 59–81.

    Article  Google Scholar 

  • Peng, P., & Kievit, R. A. (2020). The development of academic achievement and cognitive abilities: A bidirectional perspective. Child Development Perspectives, 14(1), 15–20.

    Article  Google Scholar 

  • Pokahr, A., Braubach, L., & Lamersdorf, W. (2005). Jadex: A BDI reasoning engine. Multi-agent Programming: Languages Platforms and Applications, 149–174.

  • Rakić, K., Rosić, M., & Boljat, I. (2020). A survey of agent-based modelling and simulation tools for educational purpose. Tehnički Vjesnik, 27(3), 1014–1020.

    Google Scholar 

  • Salinas, I. (2009). Learning progressions in science education: Two approaches for development. In Learning Progressions in Science (LeaPS) Conference, Iowa City, IA.

  • Saltelli, A., Annoni, P., Azzini, I., Campolongo, F., Ratto, M., & Tarantola, S. (2010). Variance based sensitivity analysis of model output. Design and estimator for the total sensitivity index. Computer Physics Communications, 181(2), 259–270.

    Article  MathSciNet  MATH  Google Scholar 

  • Seah, R., & Horne, M. (2020). The construction and validation of a geometric reasoning test item to support the development of learning progression. Mathematics Education Research Journal, 32(4), 607–628.

    Article  Google Scholar 

  • Shin, N., Stevens, S. Y., Short, H., & Krajcik, J. (2009). Learning progressions to support coherence curricula in instructional material, instruction, and assessment design. In Learning Progressions in Science (LeaPS) Conference, Iowa City, IA.

  • Simon, M. (2020). Hypothetical learning trajectories in mathematics education. Encyclopedia of mathematics education , 354–357. Springer International Publishing.

  • Simpson-Singleton, S. R., & Che, X. (2019). Agent-based modeling and simulation approaches in stem education research. Journal of International Technology and Information Management, 28(3), 2–42.

    Article  Google Scholar 

  • Sobol, I. M. (2001). Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates. Mathematics and Computers in Simulation, 55(1–3), 271–280.

    Article  MathSciNet  MATH  Google Scholar 

  • Sohail, A. (2023). Genetic algorithms in the fields of artificial intelligence and data sciences. Annals of Data Science, 10(4), 1007–1018.

    Article  MathSciNet  Google Scholar 

  • Stepanić, J., Pejić Bach, M., & Kasać, J. (2013). Agent based model of young researchers in higher education institutions. Interdisciplinary Description of Complex Systems: INDECS, 11(2), 209–216.

    Article  Google Scholar 

  • Taguma, M., Makowiecki, K., & Gabriel, F. (2023). OECD learning compass 2030: Implications for mathematics curricula. Mathematics curriculum reforms around the world: The 24th ICMI study , 479–509. Springer International Publishing.

  • Taillandier, P., Grignard, A., Marilleau, N., Philippon, D., Huynh, Q. N., Gaudou, B., & Drogoul, A. (2019). Participatory modeling and simulation with the gama platform. Journal of Artificial Societies and Social Simulation. https://doi.org/10.18564/jasss.3964

    Article  Google Scholar 

  • Templin, J., & Bradshaw, L. (2013). Measuring the reliability of diagnostic classification model examinee estimates. Journal of Classification, 30(2), 251–275.

    Article  MathSciNet  MATH  Google Scholar 

  • de Van Walle, J. A., Karp, K. S., & Bay-Williams, J. M. (2022). Elementary and middle school mathematics: Teaching developmentally. Pearson.

    Google Scholar 

  • Verma, V., & Aggarwal, R. K. (2020). A comparative analysis of similarity measures akin to the Jaccard index in collaborative recommendations: Empirical and theoretical perspective. Social Network Analysis and Mining, 10(1), 43.

    Article  Google Scholar 

  • Wang, M., & Zheng, X. (2021). Using game-based learning to support learning science: A study with middle school students. The Asia-Pacific Education Researcher, 30(2), 167–176.

    Article  Google Scholar 

  • Wędrychowicz, B., & Maleszka, M. (2023). Agent based model of elementary school group learning–A case study. In Nguyen N. T. et al. (Eds.), Computational collective intelligence: ICCCI (pp. 56–67). Springer. https://doi.org/10.1007/978-3-031-41456-5_5

  • Yuan, L., Liu, Y., Chen, P., & Xin, T. (2022). Development of a new learning progression verification method based on the hierarchical diagnostic classification model: Taking grade 5 students’ fractional operations as an example. Educational Measurement: Issues and Practice, 41(3), 69–82. https://doi.org/10.1111/emip.12504

    Article  Google Scholar 

  • Zhou, J., Bao, J., & He, R. (2023). Characteristics of good mathematics teaching in China: Findings from classroom observations. International Journal of Science and Mathematics Education, 21(4), 1177–1196.

    Article  Google Scholar 

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Acknowledgements

This research benefited from the generous support of Universiti Kebangsaan Malaysia and Zaozhuang University. Their expertise and resources created an enriching academic environment that fostered research and learning.

Funding

This work was supported by the Key Research Program of the Science Foundation of Shandong Province (Grant number ZR2020KE001).

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Zhenfeng Jiang wrote the main manuscript text and Hongchun Qu prepared research data. All authors reviewed the manuscript.

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Correspondence to Zhenfeng Jiang or Hongchun Qu.

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Jiang, Z., Karim, A.A., Khalid, F. et al. An Agent-based Modeling Framework for Learning Progression Research in Middle School Mathematics Curriculum. Int J Artif Intell Educ (2025). https://doi.org/10.1007/s40593-025-00530-5

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