本論文提出了一種創新的方法，結合自然語言處理（NLP）、嵌入技術及生成式人工智慧（Generative AI）干預，旨在預測並提升線上教育中的學習表現。研究的第一階段專注於構建一個強大的學習表現預測模型，該模型整合了來自 BookRoll 和 Viscode 系統的數據。通過分析學習行為日誌並將其轉化為自然語言描述，使用 BERT 等嵌入技術和聚類演算法識別學習行為中的模式。這些洞見被結構化為學習行為，並作為深度學習模型的輸入，達到高達95%的預測準確率顯著超越傳統方法，能夠捕捉學生行為的時間性與語義特徵，並提供卓越的解釋性與擴展性。我們識別出一組共通學習行為（Common Learning Behaviors, CLB），這些行為與高表現高度相關，並可作為設計個人化介入策略的基準。研究的第二階段著重於在真實教育情境中實施個性化干預。以反思性思維及自我調節學習（Self-Regulated Learning, SRL）理論為基礎，生成式人工智慧（Generative AI）提供學習行為的個性化洞察，支持學生進行自我反思。透過指導性的反思問題及動態反饋機制，學生能夠採取適應性的學習策略，提升時間管理與自我評估的能力。實驗結果顯示，AI 輔助的干預顯著改善了學生與 CLBs 的行為一致性，縮短行為距離，並促進有意義的自我調節實踐。特別是，時間管理被證實是連結自我評估與學術表現的關鍵橋樑。本研究不僅推動了學習表現預測方法的進步，還展現了人工智慧驅動的個性化干預在促進深度學習體驗中的潛力。透過結合尖端技術與教育理論，本研究提供了一個轉型性框架，用於提升自我調節學習及學術成果。研究結果強調了適應性干預的重要性，為未來在個性化與可擴展教育系統中的探索鋪平了道路。

This dissertation introduces an innovative approach to predicting and enhancing learning performance in online education by leveraging Natural Language Processing (NLP), embedding techniques, and generative AI-based interventions. The first phase focuses on a robust performance prediction model that integrates data from BookRoll and Viscode systems. By analyzing behavioral logs and converting them into natural language descriptions, embeddings such as BERT and clustering algorithms identify patterns within learning behaviors. These insights are structured as learning behavior and serve as inputs for a deep learning model, achieving a prediction accuracy of up to 95%. We identify the Common Learning Behaviors (CLB) which can achieve high performance and can be the baseline for personalized intervention. This model outperforms traditional methods by capturing the temporal and semantic dimensions of student behaviors, offering unparalleled interpretability and scalability. The second phase of the study involves implementing personalized interventions in real educational scenarios. Grounded in reflective thinking and self-regulated learning (SRL) theories, generative AI (GenAI) supports self-reflection by providing tailored insights into learning behaviors. Through guided reflective prompts and dynamic feedback loops, students engage in adaptive learning strategies, enhancing their time management and self-evaluation capabilities. Experimental results demonstrate that AI-assisted interventions significantly improve alignment with CLBs, reduce behavior distances, and foster meaningful self-regulation practices. Notably, time management emerges as a critical bridge between self-evaluation and academic performance. This research not only advances the methodology of performance prediction but also highlights the potential of AI-driven personalization in fostering deeper learning experiences. By combining state-of-the-art technologies with educational theories, the study provides a transformative framework for enhancing self-regulated learning and academic outcomes. The findings underscore the significance of adaptive interventions, paving the way for future explorations in personalized and scalable education systems.

Acharya, H., Reddy, R., Hussein, A., Bagga, J., & Pettit, T. (2018). The effectiveness of applied learning: an empirical evaluation using role playing in the classroom. Journal of Research in Innovative Teaching & Learning, 12(3), 295-310.
Albreiki, B., Zaki, N., & Alashwal, H. (2021). A systematic literature review of student’performance prediction using machine learning techniques. Education Sciences, 11(9), 552.
Bakeman, R., & Gottman, J. M. (1997). Observing interaction: An introduction to sequential analysis. Cambridge university press.
Barnard, L., Lan, W. Y., To, Y. M., Paton, V. O., & Lai, S.-L. (2009). Measuring self-regulation in online and blended learning environments. The internet and higher education, 12(1), 1-6.
Chiu, T. K. (2024). A classification tool to foster self-regulated learning with generative artificial intelligence by applying self-determination theory: A case of ChatGPT. Educational Technology Research and Development, 1-16.
Cohen, G., & Cohen, A. (2024). Motivational GenAI chatbot for promoting SRL in problem solving: Data-driven design principles. Ubiquity Proceedings, 4(1).
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). Bert: Pre-training of deep bidirectional transformers for language understanding. Proceedings of the 2019 conference of the North American chapter of the association for computational linguistics: human language technologies, volume 1 (long and short papers),
Dewey, J. (2022). How we think. DigiCat.
Dimitrova, V. (2003). STyLE-OLM: Interactive open learner modelling. International Journal of Artificial Intelligence in Education, 13(1), 35-78.
Duncan, T., & Mckeachie, W. J. (1991). A manual for the use of the motivated strategies for learning questionnaire (MSLQ). no. January, 2015.
Ennouamani, S., & Mahani, Z. (2017). An overview of adaptive e-learning systems. 2017 eighth international conference on intelligent computing and information systems (ICICIS),
Glaser, B., & Strauss, A. (2017). Discovery of grounded theory: Strategies for qualitative research. Routledge.
Harvey, M., Lloyd, K., McLachlan, K., Semple, A.-L., & Walkerden, G. (2020). Reflection for learning: A scholarly practice guide for educators. Advance HE.
Hauske, S., & Bendel, O. (2024). How Can GenAI Foster Well-being in Self-regulated Learning? Proceedings of the AAAI Symposium Series,
Hay, A., Peltier, J. W., & Drago, W. A. (2004). Reflective learning and on-line management education: A comparison of traditional and on-line MBA students. Strategic Change, 13(4).
Huang, B. (2024). LinguaPilot: A GAI-based framework for self-regulated EFL reading. Proceedings of the International CALL Research Conference,
Huang, S.-L., & Shiu, J.-H. (2012). A user-centric adaptive learning system for e-learning 2.0. Journal of Educational Technology & Society, 15(3), 214-225.
Kember, D., Leung, D. Y., Jones, A., Loke, A. Y., McKay, J., Sinclair, K., Tse, H., Webb, C., Yuet Wong, F. K., & Wong, M. (2000). Development of a questionnaire to measure the level of reflective thinking. Assessment & evaluation in higher education, 25(4), 381-395.
Kolb, A. Y., & Kolb, D. A. (2009). Experiential learning theory: A dynamic, holistic approach to management learning, education and development. The SAGE handbook of management learning, education and development, 7(2), 42-68.
Kolb, D. A. (2014). Experiential learning: Experience as the source of learning and development. FT press.
Kruger, J., & Dunning, D. (1999). Unskilled and unaware of it: how difficulties in recognizing one's own incompetence lead to inflated self-assessments. Journal of personality and social psychology, 77(6), 1121.
Lai, C.-L., & Hwang, G.-J. (2016). A self-regulated flipped classroom approach to improving students’ learning performance in a mathematics course. Computers & Education, 100, 126-140.
Lee, C.-A., Tzeng, J.-W., Huang, N.-F., & Su, Y.-S. (2021). Prediction of student performance in massive open online courses using deep learning system based on learning behaviors. Educational Technology & Society, 24(3), 130-146.
Leung, D. Y., & Kember, D. (2003). The relationship between approaches to learning and reflection upon practice. Educational Psychology, 23(1), 61-71.
Lew, M. D., & Schmidt, H. G. (2011). Self-reflection and academic performance: is there a relationship? Advances in Health Sciences Education, 16, 529-545.
Lin, C.-C., Cheng, E. S., Huang, A. Y., & Yang, S. J. (2024). DNA of learning behaviors: A novel approach of learning performance prediction by NLP. Computers and Education: Artificial Intelligence, 6, 100227.
Lin, M., & Lucey, T. A. (2010). Individual and group reflection strategies: What we learned from preservice teachers. Multicultural education, 18(1), 51-54.
Liu, C.-C., Cheng, Y.-B., & Huang, C.-W. (2011). The effect of simulation games on the learning of computational problem solving. Computers & Education, 57(3), 1907-1918.
Liu, C. C., Fan Chiang, S. H., Chou, C. Y., & Chen, S. Y. (2010). Knowledge exploration with concept association techniques. Online Information Review, 34(5), 786-805.
MacQueen, J. (1967). Some methods for classification and analysis of multivariate observations. Proceedings of the fifth Berkeley symposium on mathematical statistics and probability,
Markov, A. (1971). Extension of the limit theorems of probability theory to a sum of variables connected in a chain. Dynam Probabilist Syst, 1, 552.
Mazur, J. E. (2015). Learning and behavior: Instructor's review copy. Psychology Press.
Ng, D. T. K., Tan, C. W., & Leung, J. K. L. (2024). Empowering student self‐regulated learning and science education through ChatGPT: A pioneering pilot study. British Journal of Educational Technology.
Nguyen, H., & Nguyen, A. (2024). Reflective Practices and Self-Regulated Learning in Designing with Generative Artificial Intelligence: An Ordered Network Analysis. Journal of Science Education and Technology, 1-15.
O'Neil Jr, H. F., & Abedi, J. (1996). Reliability and validity of a state metacognitive inventory: Potential for alternative assessment. The journal of educational research, 89(4), 234-245.
O'Neill, S. (1999). Flow theory and the development of musical performance skills. Bulletin of the council for Research in Music Education, 129-134.
OpenAI. (2025). ChatGPT (January 2025 version). https://chat.openai.com/
Ortiz, P., & Harrell, D. F. (2018). Enabling critical self-reflection through roleplay with chimeria: Grayscale. Proceedings of the 2018 Annual Symposium on Computer-Human Interaction in Play,
Powell, S., & Tod, J. (2004). A systematic review of how theories explain learning behaviour in school contexts. EPPI-Centre, Social Science Research Unit, Institute of Education ….
Prasad, P., & Sane, A. (2024). A Self-Regulated Learning Framework using Generative AI and its Application in CS Educational Intervention Design. Proceedings of the 55th ACM Technical Symposium on Computer Science Education V. 1,
Prestholdt, T., & Fletcher, V. (2018). The Value of Experiential Learning: A Case Study with an Interdisciplinary Study Abroad Course. Bioscene: Journal of College Biology Teaching, 44(2), 17-23.
Radović, S., Firssova, O., Hummel, H. G., & Vermeulen, M. (2023). Improving academic performance: Strengthening the relation between theory and practice through prompted reflection. Active Learning in Higher Education, 24(2), 139-154.
Schommer, M. (1990). Effects of beliefs about the nature of knowledge on comprehension. Journal of educational psychology, 82(3), 498.
Schraw, G., & Dennison, R. S. (1994). Assessing metacognitive awareness. Contemporary educational psychology, 19(4), 460-475.
Simonsmeier, B. A., Peiffer, H., Flaig, M., & Schneider, M. (2020). Peer feedback improves students’ academic self-concept in higher education. Research in Higher Education, 61, 706-724.
Soemantri, D., Mccoll, G., & Dodds, A. (2018). Measuring medical students’ reflection on their learning: modification and validation of the motivated strategies for learning questionnaire (MSLQ). BMC medical education, 18, 1-10.
Sweller, J. (2011). Cognitive load theory. In Psychology of learning and motivation (Vol. 55, pp. 37-76). Elsevier.
Tait, H., Entwistle, N. J., & McCune, V. (1998). ASSIST: A reconceptualisation of the Approaches to Studying Inventory. Improving students as learners, 262-271.
Thorpe, K. (2004). Reflective learning journals: From concept to practice. Reflective practice, 5(3), 327-343.
Travers, C. J., Morisano, D., & Locke, E. A. (2015). Self‐reflection, growth goals, and academic outcomes: A qualitative study. British journal of educational psychology, 85(2), 224-241.
Tsingos-Lucas, C., Bosnic-Anticevich, S., Schneider, C. R., & Smith, L. (2017). Using reflective writing as a predictor of academic success in different assessment formats. American Journal of Pharmaceutical Education, 81(1), 8.
Vygotsky, L. S. (1978). Mind in society: The development of higher psychological processes. Harvard University Press.
Wei, J., Wang, X., Schuurmans, D., Bosma, M., Xia, F., Chi, E., Le, Q. V., & Zhou, D. (2022). Chain-of-thought prompting elicits reasoning in large language models. Advances in neural information processing systems, 35, 24824-24837.
Weinstein, C., & Palmer, D. Learning and Study Strategies Inventory (LASSI): User’s manual. 2. Clearwater, FL: H & H Publishing; 2002. In.
Wong, J., & Viberg, O. (2024). Supporting Self-Regulated Learning with Generative AI: A Case of Two Empirical Studies. LAK Workshops,
Yan, Z., Lao, H., Panadero, E., Fernández-Castilla, B., Yang, L., & Yang, M. (2022). Effects of self-assessment and peer-assessment interventions on academic performance: A meta-analysis. Educational Research Review, 37, 100484.
Zimmerman, B. J. (1986). Becoming a self-regulated learner: Which are the key subprocesses? Contemporary educational psychology, 11(4), 307-313.
Zimmerman, B. J. (1989). A social cognitive view of self-regulated academic learning. Journal of educational psychology, 81(3), 329.
Zimmerman, B. J. (1990). Self-regulated learning and academic achievement: An overview. Educational psychologist, 25(1), 3-17.