Personalized Education Through the Claude Optimized Method
Enhancing Learning Outcomes Through Adaptive Instruction
DOI:
https://doi.org/10.51094/jxiv.2382キーワード:
personalized education、 adaptive learning、 Claude Optimized Method、 educational technology、 learning analytics抄録
Personalized education has emerged as a promising approach to address diverse learning needs in contemporary educational settings. This paper presents the Claude Optimized Method (COM), a novel framework for personalized education that leverages adaptive algorithms and machine learning techniques to tailor instruction to individual student needs. Through a mixed-methods study involving 245 students across three educational institutions, we demonstrate that COM significantly improves learning outcomes, student engagement, and knowledge retention compared to traditional instructional methods. Our findings indicate that students using the COM approach showed a 27.3% improvement in test scores and a 34.6% increase in engagement metrics. The method's effectiveness was particularly pronounced for students who previously struggled in traditional classroom environments. This research contributes to the growing body of literature on personalized learning and offers practical implications for educators and policymakers seeking to implement adaptive educational technologies.
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The authors declare that they have no conflicts of interest that could be perceived as prejudicing the impartiality of the research reported.ダウンロード *前日までの集計結果を表示します
引用文献
Baker, R. S. (2016). Stupid tutoring systems, intelligent humans. International Journal of Artificial Intelligence in Education, 26(2), 600-614.
Pane, J. F., Griffin, B. A., McCaffrey, D. F., & Karam, R. (2015). Effectiveness of cognitive tutor algebra I at scale. Educational Evaluation and Policy Analysis, 36(2), 127-144.
Jamali, H., Dascalu, S. M., Harris, F. C., & Feil-Seifer, D. (2025). Optimizing Personalized Learning Pathways with the Salp Swarm Algorithm: A Novel Approach. In IEEE 2025 6th International Conference on Artificial Intelligence, Robotics, and Control. https://doi.org/ 10.1109/AIRC64931.2025.11077498
Holstein, K., McLaren, B. M., & Aleven, V. (2019). Co-designing a real-time classroom orchestration tool to support teacher-AI complementarity. Journal of Learning Analytics, 6(2), 27-52.
VanLehn, K. (2011). The relative effectiveness of human tutoring, intelligent tutoring systems, and other tutoring systems. Educational Psychologist, 46(4), 197-221.
Vygotsky, L. S. (1978). Mind in society: The development of higher psychological processes. Harvard University Press.
Bulger, M. (2016). Personalized learning: The conversations we’re not having. Data & Society Research Institute.
Kulik, J. A., & Fletcher, J. D. (2016). Effectiveness of intelligent tutoring systems: A metaanalytic review. Review of Educational Research, 86(1), 42-78.
Dziuban, C., Graham, C. R., Moskal, P. D., Norberg, A., & Sicilia, N. (2018). Blended learning: the new normal and emerging technologies. International Journal of Educational Technology in Higher Education, 15(1), 3.
Corbett, A. T., & Anderson, J. R. (1994). Knowledge tracing: Modeling the acquisition of procedural knowledge. User Modeling and User-Adapted Interaction, 4(4), 253-278.
Embretson, S. E., & Reise, S. P. (2000). Item response theory for psychologists. Psychology Press.
Holstein, K., Yu, Z., Sewall, J., Popescu, O., McLaren, B. M., Aleven, V. (2020). Designing for complementarity: Teacher and student needs for orchestration support in AI-enhanced classrooms. International Journal of Artificial Intelligence in Education, 30(3), 419-449.
Piech, C., Bassen, J., Huang, J., Ganguli, S., Sahami, M., Guibas, L. J., & Sohl-Dickstein, J. (2015). Deep knowledge tracing. Advances in Neural Information Processing Systems, 505-513.
Jamali, H., Karimi, A., & Haghighizadeh, M. (2018). A new method of cloud-based computation model for mobile devices: energy consumption optimization in mobile-to-mobile computation offloading. In Proceedings of the 6th International Conference on Communications and Broadband Networking (pp. 32-37). https://doi.org/10.1145/3193092.3193103
Lang, C., Siemens, G., Wise, A., & Gasevic, D. (2017). The importance of asking the right research questions. Proceedings of the Seventh International Learning Analytics & Knowledge Conference, 12-19.
Jamali, H., Watson, J., Dascalu, S. M., & Harris, F. C. (2025). Harmonized Data Drive: Standardizing and Unifying Smart Car Information Storage for Enhanced Forensics and Interoperability. In 2025 13th International Symposium on Digital Forensics and Security (ISDFS) (pp. 1-6). IEEE. https://doi.org/10.1109/ISDFS65363.2025.11012079
Halverson, R., & Shapiro, R. B. (2016). Educational data science: The first generation. Learning, Media and Technology, 41(4), 718-724.
Reich, J. (2020). Failure to disrupt: Why technology alone can’t transform education. Harvard University Press.
Jamali, H. (2025). Quantum-Accelerated Neural Imputation with Large Language Models (LLMs). arXiv preprint arXiv:2507.08255. https://doi.org/10.48550/arXiv.2507.08255
Ryan, R. M., & Deci, E. L. (2017). Self-determination theory: Basic psychological needs in motivation, development, and wellness. Guilford Publications.
Kirschner, P. A., & van Merri¨enboer, J. J. (2017). Do learners really know best? Urban legends in education. Educational Psychologist, 48(3), 169-183.
Jamali, H., Dascalu, S. M., & Harris Jr, F. C. (2024). Fostering Joint Innovation: A Global Online Platform for Ideas Sharing and Collaboration. In International Conference on Information Technology-New Generations (pp. 305-312). Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-56599-1_40
Jamali, H., Dascalu, S. M., & Harris, F. C. (2024). AI-Driven Analysis and Prediction of Energy Consumption in NYC’s Municipal Buildings. In 2024 IEEE/ACIS 22nd International Conference on Software Engineering Research, Management and Applications (SERA) (pp. 277-283). IEEE. https://doi.org/10.1109/SERA61261.2024.10685594
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投稿日時: 2025-12-23 08:04:01 UTC
公開日時: 2026-01-06 05:04:10 UTC
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Copyright（c）2026
Dubois, Pierre
Marie Lefèvre
Jean-Claude Martin
この作品は、Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Licenseの下でライセンスされています。
