成效評量是各類場域驅動持續改善的核心環節。於學習場域，數位學習平台之普及使學習歷程資料得以大量蒐集，為學習歷程中之成效評量提供了基礎；然現行之學習成效評量仍多屬總結性、事後性之單一維度判定，難以於學習歷程進行中辨識學生之學習弱點、釐清其成因並預警後續之學習風險，評量結果亦不易轉化為可執行之教學對策，致使評量與教學改善之間未能有效銜接。
本研究首先提出 OPDCAA 階段性成效評量模式，並開發一套基於代理人之階段性學習成效評量技術，用於描述性、診斷性、預測性與指導性四類分析，於每一學習階段依序進行成效評量、成因診斷、風險預測與對策生成。於成效評量，代理人評量學生對學習概念之掌握程度，並分別自作答歷程、學習平台操作足跡與臉部表情，分析認知、態度與情意三指標之成效；針對成因診斷，代理人從學習者之先備知識、作答軌跡與題型困難等面向釐清學習弱點之成因；於風險預測，代理人以深度知識追蹤技術推估後續學習概念之學習風險，並以反事實消融方法分析所預測之風險的原因，使預測結果可被解釋；於對策生成，代理人運用知識圖譜檢索增強生成技術並結合大型語言模型，將可溯源之診斷結果轉換為引用課文、具學理依據之個別化學習對策。
本研究以某國小六年級學生於跨領域數位閱讀學習平台之四篇文本學習歷程進行驗證。在風險預測方面，學習概念之深度學習風險預測模型的 AUC 為 0.775，高於僅憑題目難易度預測的基準模型（0.630）；本研究進一步以知識蒸餾將預測能力內化至語言模型後，其 AUC 於有使用與無使用概念圖之提示策略下介於 0.839 至 0.858，均高於其教師模型；反事實消融顯示之先備知識先後相關性與概念知識地圖之先備結構一致，顯示模型之風險預測依據符合既定的概念先備關係，並為後續弱點診斷與補救對策生成提供可理解之依據。對策生成方面，25 名學生共產出 82 條個別化對策，其中 76 條（92.7%）直接引用教材原句，且均可回溯至上游分析結果，顯示所生成對策具備可追溯性。應用驗證以準實驗設計比較使用整合本評量技術之平台的實驗組（n = 25）與未使用之對照組（n = 24），並以學科起點能力為共變項進行共變數分析，實驗組於自然科學期末考顯著優於對照組（F(1, 46) = 5.505, p = .023, Hedges' g = 0.66）；平台內部之階段性評量亦顯示學生於認知與態度維度呈顯著進步。
本研究以單一代理人整合四類分析，將學習成效評量由事後之結果判定推進至涵蓋成因診斷、風險預測與對策生成之階段性機制；並以確定性量化模組與生成式語言模型分工，使所生成之個別化學習對策兼具可溯源性與可讀性。所提出之方法與技術可於學習歷程中支持學生學習弱點診斷與個別化對策之生成，並為教師之教學調整提供依據。

Outcome assessment is central to continuous improvement across domains—from quality management in manufacturing and performance appraisal in enterprises to learning assessment in education. In the learning domain, the proliferation of digital learning platforms has made it possible to collect rich process data and to assess learning as it unfolds; yet prevailing assessments of learning outcomes remain summative, retrospective, and unidimensional. They rarely identify, during the learning process, where a learner is weak, why the weakness arises, and where difficulties are likely to occur next; nor are their results readily translated into executable instructional actions. A gap therefore persists between assessment and instructional improvement.
This study proposes and develops an agent-based stage-wise learning outcome assessment technique. An OPDCAA stage-wise assessment model is first proposed, and a single intelligent agent integrates descriptive, diagnostic, predictive, and prescriptive analytics, autonomously performing outcome assessment, cause diagnosis, risk forewarning, and intervention generation at each learning stage. For descriptive analysis, the agent represents each learner through a student model, estimates concept mastery and its uncertainty, and derives cognitive, attitudinal, and affective indicators from response logs, action footprints, and facial affect. For cause diagnosis, it attributes the causes of weakness across prerequisite gaps, response trajectories, and item-type difficulties. For risk prediction, deep knowledge tracing estimates the risk of upcoming concepts, and counterfactual ablation reveals the prerequisites on which the prediction relies, making the prediction interpretable. For intervention generation, knowledge-graph retrieval-augmented generation coupled with a large language model converts traceable diagnostic results into individualized recommendations that quote the reading material and rest on evidence-based methods.
The technique was validated with sixth-grade students’ learning processes across four texts on a cross-disciplinary digital reading platform. The concept-risk prediction model attained an AUC of 0.775, clearly exceeding a non-personalized difficulty baseline (0.630). After knowledge distillation internalized this predictive capability into a language model, the distilled model attained AUCs ranging from 0.839 to 0.858 across three concept-graph prompting strategies, all exceeding the teacher model (0.773). The prerequisite dependencies revealed by counterfactual ablation were consistent with the structure of the concept knowledge map. For intervention generation, 82 individualized recommendations were produced for 25 students, of which 76 (92.7%) directly quoted the instructional text, with no fabricated content. In application, a quasi-experimental design compared an experimental group using the platform that integrates the proposed assessment (n = 25) with a control group (n = 24); after controlling for prior subject ability, the experimental group significantly outperformed the control group on the science final examination (Hedges’ g = 0.66). Within-platform stage-wise assessment further showed significant gains in the cognitive and attitudinal dimensions.
By integrating the four analytic types within a single agent, this study advances learning outcome assessment from retrospective judgment to a stage-wise mechanism that encompasses cause diagnosis, risk forewarning, and prescriptive guidance. Separating deterministic quantitative modules from generative language modeling renders the resulting recommendations both traceable and readable. The proposed method and technique support the diagnosis of weaknesses and the generation of individualized interventions during the learning process, and provide a basis for instructional adjustment by teachers.

陳品蓉（2026）。結合診斷分析之即時學習異常預測方法與技術開發［碩士論文，國立成功大學］。臺灣博碩士論文知識加值系統。https://ndltd.ncl.edu.tw/cgi-bin/gs32/gsweb.cgi/login?o=dnclcdr&s=id%3D%22113NCKU5621039%22.&searchmode=basic
Andersson, C., & Palm, T. (2017). The impact of formative assessment on student achievement: A study of the effects of changes to classroom practice after a comprehensive professional development programme. Learning and Instruction, 49, 92–102. https://doi.org/10.1016/j.learninstruc.2016.12.006
Ausubel, D. P. (1960). The use of advance organizers in the learning and retention of meaningful verbal material. Journal of Educational Psychology, 51(5), 267–272. https://doi.org/10.1037/h0046669
Baker, R. S. J. d., D’Mello, S. K., Rodrigo, M. M. T., & Graesser, A. C. (2010). Better to be frustrated than bored: The incidence, persistence, and impact of learners’ cognitive-affective states during interactions with three different computer-based learning environments. International Journal of Human-Computer Studies, 68(4), 223–241. https://doi.org/10.1016/j.ijhcs.2009.12.003
Baker, R. S. J. d., & Yacef, K. (2009). The state of educational data mining in 2009: A review and future visions. Journal of Educational Data Mining, 1(1), 3–17. https://doi.org/10.5281/zenodo.3554657
Banerjee, A., Bandyopadhyay, T., & Acharya, P. (2013). Data analytics: Hyped up aspirations or true potential. Vikalpa: The Journal for Decision Makers, 38(4), 1–12. https://doi.org/10.1177/0256090920130401
Beck, J. E., & Gong, Y. (2013). Wheel-spinning: Students who fail to master a skill. In H. C. Lane, K. Yacef, J. Mostow, & P. Pavlik (Eds.), Artificial intelligence in education (Lecture Notes in Computer Science, Vol. 7926, pp. 431–440). Springer. https://doi.org/10.1007/978-3-642-39112-5_44
Black, P., & Wiliam, D. (1998). Assessment and classroom learning. Assessment in Education: Principles, Policy & Practice, 5(1), 7–74. https://doi.org/10.1080/0969595980050102
Bloom, B. S. (1968). Learning for mastery. Evaluation Comment, 1(2), 1–12.
Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D. M., Wu, J., Winter, C., … Amodei, D. (2020). Language models are few-shot learners. Advances in Neural Information Processing Systems, 33, 1877–1901. https://proceedings.neurips.cc/paper/2020/hash/1457c0d6bfcb4967418bfb8ac142f64a-Abstract.html
Campione, J. C., & Brown, A. L. (1987). Linking dynamic assessment with school achievement. In C. S. Lidz (Ed.), Dynamic assessment: An interactional approach to evaluating learning potential (pp. 82–115). Guilford Press.
Carless, D. (2017). Scaling up assessment for learning: Progress and prospects. In D. Carless, S. Bridges, C. Chan, & R. Glofcheski (Eds.), Scaling up assessment for learning in higher education (pp. 3–17). Springer. https://doi.org/10.1007/978-981-10-3045-1_1
Cepeda, N. J., Pashler, H., Vul, E., Wixted, J. T., & Rohrer, D. (2006). Distributed practice in verbal recall tasks: A review and quantitative synthesis. Psychological Bulletin, 132(3), 354–380. https://doi.org/10.1037/0033-2909.132.3.354
Chandola, V., Banerjee, A., & Kumar, V. (2009). Anomaly detection: A survey. ACM Computing Surveys, 41(3), Article 15. https://doi.org/10.1145/1541880.1541882
Chatti, M. A., Dyckhoff, A. L., Schroeder, U., & Thüs, H. (2012). A reference model for learning analytics. International Journal of Technology Enhanced Learning, 4(5/6), 318–331. https://doi.org/10.1504/IJTEL.2012.051815
Chen, C.-M., Chen, L.-C., & Horng, W.-J. (2021). A collaborative reading annotation system with formative assessment and feedback mechanisms to promote digital reading performance. Interactive Learning Environments, 29(5), 848–865. https://doi.org/10.1080/10494820.2019.1636091
Chu, Z., Wang, S., Xie, J., Zhu, T., Yan, Y., Ye, J., Zhong, A., Hu, X., Liang, J., Yu, P. S., & Wen, Q. (2025). LLM agents for education: Advances and applications. In Findings of the Association for Computational Linguistics: EMNLP 2025 (pp. 13782–13810). Association for Computational Linguistics. https://doi.org/10.18653/v1/2025.findings-emnlp.743
Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Lawrence Erlbaum Associates.
Conati, C., Porayska-Pomsta, K., & Mavrikis, M. (2018). AI in education needs interpretable machine learning: Lessons from open learner modelling [Preprint]. arXiv. https://doi.org/10.48550/arXiv.1807.00154
Corbett, A. T., & Anderson, J. R. (1995). Knowledge tracing: Modeling the acquisition of procedural knowledge. User Modeling and User-Adapted Interaction, 4(4), 253–278. https://doi.org/10.1007/BF01099821
Deming, W. E. (1950). Elementary principles of the statistical control of quality. Nippon Kagaku Gijutsu Renmei.
D’Mello, S. K., & Kory, J. (2015). A review and meta-analysis of multimodal affect detection systems. ACM Computing Surveys, 47(3), Article 43. https://doi.org/10.1145/2682899
D’Mello, S., Lehman, B., Pekrun, R., & Graesser, A. (2014). Confusion can be beneficial for learning. Learning and Instruction, 29, 153–170. https://doi.org/10.1016/j.learninstruc.2012.05.003
Doshi-Velez, F., & Kim, B. (2017). Towards a rigorous science of interpretable machine learning [Preprint]. arXiv. https://doi.org/10.48550/arXiv.1702.08608
Fredricks, J. A., Blumenfeld, P. C., & Paris, A. H. (2004). School engagement: Potential of the concept, state of the evidence. Review of Educational Research, 74(1), 59–109. https://doi.org/10.3102/00346543074001059
Fritz, C. O., Morris, P. E., & Richler, J. J. (2012). Effect size estimates: Current use, calculations, and interpretation. Journal of Experimental Psychology: General, 141(1), 2–18. https://doi.org/10.1037/a0024338
Furlanello, T., Lipton, Z. C., Tschannen, M., Itti, L., & Anandkumar, A. (2018). Born-again neural networks. In Proceedings of the 35th International Conference on Machine Learning (Proceedings of Machine Learning Research, Vol. 80, pp. 1607–1616). PMLR. https://proceedings.mlr.press/v80/furlanello18a.html
Gagné, R. M. (1968). Presidential address of Division 15 learning hierarchies. Educational Psychologist, 6(1), 1–9. https://doi.org/10.1080/00461526809528968
Ge, X. (2021). Emotion matters for academic success. Educational Technology Research and Development, 69(1), 67–70. https://doi.org/10.1007/s11423-020-09925-8
Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., & Rubin, D. B. (2013). Bayesian data analysis (3rd ed.). CRC Press. https://doi.org/10.1201/b16018
Gervet, T., Koedinger, K., Schneider, J., & Mitchell, T. (2020). When is deep learning the best approach to knowledge tracing? Journal of Educational Data Mining, 12(3), 31–54. https://doi.org/10.5281/zenodo.4143614
Hedges, L. V. (1981). Distribution theory for Glass’s estimator of effect size and related estimators. Journal of Educational Statistics, 6(2), 107–128. https://doi.org/10.2307/1164588
Hinton, G., Vinyals, O., & Dean, J. (2015). Distilling the knowledge in a neural network [Preprint]. arXiv. https://doi.org/10.48550/arXiv.1503.02531
Hirsh, J. B., Kang, S. K., & Bodenhausen, G. V. (2012). Personalized persuasion: Tailoring persuasive appeals to recipients’ personality traits. Psychological Science, 23(6), 578–581. https://doi.org/10.1177/0956797611436349
Hollander, M., Wolfe, D. A., & Chicken, E. (2013). Nonparametric statistical methods (3rd ed.). Wiley. https://doi.org/10.1002/9781119196037
Hu, E. J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., & Chen, W. (2022). LoRA: Low-rank adaptation of large language models. In International Conference on Learning Representations. https://openreview.net/forum?id=nZeVKeeFYf9
Imai, M. (1986). Kaizen: The key to Japan’s competitive success. McGraw-Hill.
Jensen, L. X., Bearman, M., & Boud, D. (2021). Understanding feedback in online learning—A critical review and metaphor analysis. Computers & Education, 173, Article 104271. https://doi.org/10.1016/j.compedu.2021.104271
Kasneci, E., Sessler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., Günnemann, S., Hüllermeier, E., Krusche, S., Kutyniok, G., Michaeli, T., Nerdel, C., Pfeffer, J., Poquet, O., Sailer, M., Schmidt, A., Seidel, T., … Kasneci, G. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences, 103, Article 102274. https://doi.org/10.1016/j.lindif.2023.102274
Khalil, M., & Ebner, M. (2015). Learning analytics: Principles and constraints. In Proceedings of EdMedia 2015—World Conference on Educational Media and Technology (pp. 1789–1799). Association for the Advancement of Computing in Education. https://tugraz.elsevierpure.com/en/publications/learning-analytics-principles-and-constraints
Khan, I., Ahmad, A. R., Jabeur, N., & Mahdi, M. N. (2021). A conceptual framework to aid attribute selection in machine learning student performance prediction models. International Journal of Interactive Mobile Technologies, 15(15), 4–19. https://doi.org/10.3991/ijim.v15i15.20019
Khosravi, H., Buckingham Shum, S., Chen, G., Conati, C., Tsai, Y.-S., Kay, J., Knight, S., Martinez-Maldonado, R., Sadiq, S., & Gašević, D. (2022). Explainable artificial intelligence in education. Computers and Education: Artificial Intelligence, 3, Article 100074. https://doi.org/10.1016/j.caeai.2022.100074
Knight, J. E., & Allen, S. (2012). Applying the PDCA cycle to the complex task of teaching and assessing public relations writing. International Journal of Higher Education, 1(2), 67–83. https://doi.org/10.5430/ijhe.v1n2p67
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444. https://doi.org/10.1038/nature14539
Leelaluk, S., Tang, C., Švábenský, V., & Shimada, A. (2025). Knowledge distillation in RNN-attention models for early prediction of student performance. In Proceedings of the 40th ACM/SIGAPP Symposium on Applied Computing (pp. 64–73). Association for Computing Machinery. https://doi.org/10.1145/3672608.3707805
Levene, H. (1960). Robust tests for equality of variances. In I. Olkin (Ed.), Contributions to probability and statistics: Essays in honor of Harold Hotelling (pp. 278–292). Stanford University Press.
Lewis, P., Perez, E., Piktus, A., Petroni, F., Karpukhin, V., Goyal, N., Küttler, H., Lewis, M., Yih, W., Rocktäschel, T., Riedel, S., & Kiela, D. (2020). Retrieval-augmented generation for knowledge-intensive NLP tasks. Advances in Neural Information Processing Systems, 33, 9459–9474. https://proceedings.neurips.cc/paper_files/paper/2020/hash/6b493230205f780e1bc26945df7481e5-Abstract.html
List, A., & Alexander, P. A. (2019). Toward an integrated framework of multiple text use. Educational Psychologist, 54(1), 20–39. https://doi.org/10.1080/00461520.2018.1505514
Mayer, R. E. (2009). Multimedia learning (2nd ed.). Cambridge University Press. https://doi.org/10.1017/CBO9780511811678
Mohamed Nafuri, A. F., Sani, N. S., Zainudin, N. F. A., Rahman, A. H. A., & Aliff, M. (2022). Clustering analysis for classifying student academic performance in higher education. Applied Sciences, 12(19), Article 9467. https://doi.org/10.3390/app12199467
Mothilal, R. K., Sharma, A., & Tan, C. (2020). Explaining machine learning classifiers through diverse counterfactual explanations. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (pp. 607–617). Association for Computing Machinery. https://doi.org/10.1145/3351095.3372850
Nagatani, K., Zhang, Q., Sato, M., Chen, Y.-Y., Chen, F., & Ohkuma, T. (2019). Augmenting knowledge tracing by considering forgetting behavior. In The World Wide Web Conference (pp. 3101–3107). Association for Computing Machinery. https://doi.org/10.1145/3308558.3313565
Nakagawa, H., Iwasawa, Y., & Matsuo, Y. (2019). Graph-based knowledge tracing: Modeling student proficiency using graph neural network. In 2019 IEEE/WIC/ACM International Conference on Web Intelligence (pp. 156–163). IEEE. https://doi.org/10.1145/3350546.3352513
Pandey, S., & Karypis, G. (2019). A self-attentive model for knowledge tracing. In Proceedings of the 12th International Conference on Educational Data Mining (pp. 384–389). International Educational Data Mining Society.
Pearl, J. (2009). Causality: Models, reasoning, and inference (2nd ed.). Cambridge University Press. https://doi.org/10.1017/CBO9780511803161
Pekrun, R. (2006). The control-value theory of achievement emotions: Assumptions, corollaries, and implications for educational research and practice. Educational Psychology Review, 18(4), 315–341. https://doi.org/10.1007/s10648-006-9029-9
Pekrun, R., Goetz, T., Titz, W., & Perry, R. P. (2002). Academic emotions in students’ self-regulated learning and achievement: A program of qualitative and quantitative research. Educational Psychologist, 37(2), 91–105. https://doi.org/10.1207/S15326985EP3702_4
Peng, B., Zhu, Y., Liu, Y., Bo, X., Shi, H., Hong, C., Zhang, Y., & Tang, S. (2024). Graph retrieval-augmented generation: A survey [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2408.08921
Perie, M., Marion, S., & Gong, B. (2009). Moving toward a comprehensive assessment system: A framework for considering interim assessments. Educational Measurement: Issues and Practice, 28(3), 5–13. https://doi.org/10.1111/j.1745-3992.2009.00149.x
Picard, R. W., Papert, S., Bender, W., Blumberg, B., Breazeal, C., Cavallo, D., Machover, T., Resnick, M., Roy, D., & Strohecker, C. (2004). Affective learning—A manifesto. BT Technology Journal, 22(4), 253–269. https://doi.org/10.1023/B:BTTJ.0000047603.37042.33
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, 28, 505–513. https://proceedings.neurips.cc/paper_files/paper/2015/hash/bac9162b47c56fc8a4d2a519803d51b3-Abstract.html
Popham, W. J. (2008). Transformative assessment. ASCD.
Poropat, A. E. (2009). A meta-analysis of the five-factor model of personality and academic performance. Psychological Bulletin, 135(2), 322–338. https://doi.org/10.1037/a0014996
Posner, G. J., Strike, K. A., Hewson, P. W., & Gertzog, W. A. (1982). Accommodation of a scientific conception: Toward a theory of conceptual change. Science Education, 66(2), 211–227. https://doi.org/10.1002/sce.3730660207
Prakasa, Y. F. (2023). Implementation of PDCA metacognitive learning strategies to metacognitive abilities, science process skills, and student learning outcomes. Jurnal Pendidikan Profesi Guru, 1(1), 12–26. https://doi.org/10.22373/jppg.v1i1.2439
Qwen, Yang, A., Yang, B., Zhang, B., Hui, B., Zheng, B., Yu, B., Li, C., Liu, D., Huang, F., Wei, H., Lin, H., Yang, J., Tu, J., Zhang, J., Yang, J., Yang, J., Zhou, J., Lin, J., … Qiu, Z. (2024). Qwen2.5 technical report [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2412.15115
Roediger, H. L., III, & Karpicke, J. D. (2006). Test-enhanced learning: Taking memory tests improves long-term retention. Psychological Science, 17(3), 249–255. https://doi.org/10.1111/j.1467-9280.2006.01693.x
Romero, C., & Ventura, S. (2007). Educational data mining: A survey from 1995 to 2005. Expert Systems with Applications, 33(1), 135–146. https://doi.org/10.1016/j.eswa.2006.04.005
Rosenshine, B. (2012). Principles of instruction: Research-based strategies that all teachers should know. American Educator, 36(1), 12–19, 39. https://www.aft.org/ae/spring2012/rosenshine
Russell, J. A. (1980). A circumplex model of affect. Journal of Personality and Social Psychology, 39(6), 1161–1178. https://doi.org/10.1037/h0077714
Sadler, D. R. (1989). Formative assessment and the design of instructional systems. Instructional Science, 18(2), 119–144. https://doi.org/10.1007/BF00117714
Schick, T., Dwivedi-Yu, J., Dessì, R., Raileanu, R., Lomeli, M., Hambro, E., Zettlemoyer, L., Cancedda, N., & Scialom, T. (2023). Toolformer: Language models can teach themselves to use tools. Advances in Neural Information Processing Systems, 36, 68539–68551. https://doi.org/10.52202/075280-2997
Schildkamp, K., van der Kleij, F. M., Heitink, M. C., Kippers, W. B., & Veldkamp, B. P. (2020). Formative assessment: A systematic review of critical teacher prerequisites for classroom practice. International Journal of Educational Research, 103, Article 101602. https://doi.org/10.1016/j.ijer.2020.101602
Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002). Experimental and quasi-experimental designs for generalized causal inference. Houghton Mifflin.
Siemens, G. (2013). Learning analytics: The emergence of a discipline. American Behavioral Scientist, 57(10), 1380–1400. https://doi.org/10.1177/0002764213498851
Stamper, J., Xiao, R., & Hou, X. (2024). Enhancing LLM-based feedback: Insights from intelligent tutoring systems and the learning sciences. In A. M. Olney, I.-A. Chounta, Z. Liu, O. C. Santos, & I. I. Bittencourt (Eds.), Artificial intelligence in education: Posters and late breaking results, workshops and tutorials, industry and innovation tracks, practitioners, doctoral consortium and blue sky (Communications in Computer and Information Science, Vol. 2150, pp. 32–43). Springer. https://doi.org/10.1007/978-3-031-64315-6_3
Tempelaar, D. T., Rienties, B., & Giesbers, B. (2015). In search for the most informative data for feedback generation: Learning analytics in a data-rich context. Computers in Human Behavior, 47, 157–167. https://doi.org/10.1016/j.chb.2014.05.038
VanLehn, K. (2011). The relative effectiveness of human tutoring, intelligent tutoring systems, and other tutoring systems. Educational Psychologist, 46(4), 197–221. https://doi.org/10.1080/00461520.2011.611369
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30, 5998–6008. https://proceedings.neurips.cc/paper_files/paper/2017/hash/3f5ee243547dee91fbd053c1c4a845aa-Abstract.html
Verma, S., Boonsanong, V., Hoang, M., Hines, K. E., Dickerson, J. P., & Shah, C. (2020). Counterfactual explanations and algorithmic recourses for machine learning: A review [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2010.10596
Vygotsky, L. S. (1978). Mind in society: The development of higher psychological processes (M. Cole, V. John-Steiner, S. Scribner, & E. Souberman, Eds.). Harvard University Press.
Wachter, S., Mittelstadt, B., & Russell, C. (2018). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. Harvard Journal of Law & Technology, 31(2), 841–887.
Wang, L., Ma, C., Feng, X., Zhang, Z., Yang, H., Zhang, J., Chen, Z., Tang, J., Chen, X., Lin, Y., Zhao, W. X., Wei, Z., & Wen, J.-R. (2024). A survey on large language model based autonomous agents. Frontiers of Computer Science, 18(6), Article 186345. https://doi.org/10.1007/s11704-024-40231-1
Wilcoxon, F. (1945). Individual comparisons by ranking methods. Biometrics Bulletin, 1(6), 80–83. https://doi.org/10.2307/3001968
Winstone, N. E., & Boud, D. (2022). The need to disentangle assessment and feedback in higher education. Studies in Higher Education, 47(3), 656–667. https://doi.org/10.1080/03075079.2020.1779687
Wood, D., Bruner, J. S., & Ross, G. (1976). The role of tutoring in problem solving. Journal of Child Psychology and Psychiatry, 17(2), 89–100. https://doi.org/10.1111/j.1469-7610.1976.tb00381.x
Wooldridge, M., & Jennings, N. R. (1995). Intelligent agents: Theory and practice. Knowledge Engineering Review, 10(2), 115–152. https://doi.org/10.1017/S0269888900008122
Xi, Z., Chen, W., Guo, X., He, W., Ding, Y., Hong, B., Zhang, M., Wang, J., Jin, S., Zhou, E., Zheng, R., Fan, X., Wang, X., Xiong, L., Zhou, Y., Wang, W., Jiang, C., Zou, Y., Liu, X., … Gui, T. (2025). The rise and potential of large language model based agents: A survey. Science China Information Sciences, 68(2), Article 121101. https://doi.org/10.1007/s11432-024-4222-0
Yang, A. C. M., Flanagan, B., & Ogata, H. (2022). Adaptive formative assessment system based on computerized adaptive testing and the learning memory cycle for personalized learning. Computers and Education: Artificial Intelligence, 3, Article 100104. https://doi.org/10.1016/j.caeai.2022.100104
Yao, S., Zhao, J., Yu, D., Du, N., Shafran, I., Narasimhan, K., & Cao, Y. (2023). ReAct: Synergizing reasoning and acting in language models. In International Conference on Learning Representations.
Yeung, C.-K., & Yeung, D.-Y. (2018). Addressing two problems in deep knowledge tracing via prediction-consistent regularization. In Proceedings of the Fifth Annual ACM Conference on Learning at Scale (Article 5, pp. 1–10). Association for Computing Machinery. https://doi.org/10.1145/3231644.3231647
Zhang, J., Shi, X., King, I., & Yeung, D.-Y. (2017). Dynamic key-value memory networks for knowledge tracing. In Proceedings of the 26th International Conference on World Wide Web (pp. 765–774). Association for Computing Machinery. https://doi.org/10.1145/3038912.3052580
Zimmerman, B. J. (2002). Becoming a self-regulated learner: An overview. Theory Into Practice, 41(2), 64–70. https://doi.org/10.1207/s15430421tip4102_2