Phản hồi thông tin và Đề xuất dựa trên AI giải thích để Hỗ trợ Tự điều chỉnh của Sinh viên

Adadi, A., & Berrada, M. (2018). Peeking inside the black-box: A survey on explainable artificial intelligence (Xai). IEEE Access, 6, 52138–52160. Afzaal, M., Nouri, J., Zia, A., Papapetrou, P., Fors, U., Wu, Y., Li, X., & Weegar, R. (2021). Explainable ai for data-driven feedback and intelligent action recommendations to support students self-regulation. Frontiers in Artificial Intelligence, 4. Akçapınar, G., Altun, A., & Aşkar, P. (2019). Using learning analytics to develop early-warning system for at-risk students. International Journal of Educational Technology in Higher Education, 16(1), 1–20. Algayres, M. G., & Triantafyllou, E. (2020). Learning analytics in flipped classrooms: A scoping review. Electronic Journal of e-Learning, 18(5), 397–409. Alonso-Mencía, M. E., Alario-Hoyos, C., Estévez-Ayres, I., & Kloos, C. D. (2021). Analysing self-regulated learning strategies of Mooc learners through self-reported data. Australasian Journal of Educational Technology, 37(3), 56–70. Baneres, D., Rodríguez-Gonzalez, M. E., & Serra, M. (2019). An early feedback prediction system for learners at-risk within a first-year higher education course. IEEE Transactions on Learning Technologies, 12(2), 249–263. Cano, A., & Leonard, J. D. (2019). Interpretable multiview early warning system adapted to underrepresented student populations. IEEE Transactions on Learning Technologies, 12(2), 198–211. Cavanagh, T., Chen, B., Lahcen, R. A. M., & Paradiso, J. R. (2020). Constructing a design framework and pedagogical approach for adaptive learning in higher education: A practitioner’s perspective. International Review of Research in Open and Distributed Learning, 21(1), 173–197. Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002). Smote: Synthetic minority over-sampling technique. Journal of artificial intelligence research, 16, 321–357. Cicchinelli, A., Veas, E., Pardo, A., Pammer-Schindler, V., Fessl, A., Barreiros, C., & Lindstädt, S. (2018). Finding traces of self-regulated learning in activity streams. In: In Proceedings of the 8th international conference on learning analytics and knowledge. pp. 191-200. Corrin, L., & De Barba, P. (2014). Exploring students’ interpretation of feedback delivered through learning analytics dashboards [pp. 629-633]. B. Hegarty, J. McDonald y S.-K. Loke, Rhetoric and Reality: Critical perspectives on educational technology. Dunedin: University of Melbourne. Recuperado de https://melbourne-cshe.unimelb.edu.au/research/research-projects/edutech/learninganalytics-dashboards. Davis, D., Chen, G., Jivet, I., Hauff, C., & Houben, G.-J. (2016). Encouraging metacognition & self-regulation in Moocs through increased learner feedback. LAL@ LAK, pp. 17-22. DeSimone, J. A., Harms, P. D., & DeSimone, A. J. (2015). Best practice recommendations for data screening. Journal of Organizational Behavior, 36(2), 171–181. Di Mitri, D., Scheffel, M., Drachsler, H., Börner, D., Ternier, S., & Specht, M. (2017). Learning pulse: A machine learning approach for predicting performance in self-regulated learning using multimodal data. In: Proceedings of the seventh international learning analytics & knowledge conference, pp. 188- 197. Fariani, R. I., Junus, K., & Santoso, H. B. (2022). A systematic literature review on personalised learning in the higher education context (pp. 1–28). Knowledge and learning: Technology. Garcia, R., Falkner, K., & Vivian, R. (2018). Systematic literature review: Self-regulated learning strategies using e-learning tools for computer science. Computers & Education, 123, 150–163. Gašević, D., Mirriahi, N., & Dawson, S. (2014). Analytics of the effects of video use and instruction to support reflective learning. In: Proceedings of the fourth international conference on learning analytics and Knowledge, pp. 123-132. Kizilcec, R. F., Pérez-Sanagustín, M., & Maldonado, J. J. (2016). Recommending self-regulated learning strategies does not improve performance in a Mooc. In: Proceedings of the third (2016) ACM conference on learning@ scale, pp. 101-104. Kizilcec, R. F., Pérez-Sanagustín, M., & Maldonado, J. J. (2017). Self-regulated learning strategies predict learner behavior and goal attainment in massive open online courses. Computers & Education, 104, 18–33. Lau, C., Sinclair, J., Taub, M., Azevedo, R., & Jang, E. E. (2017). Transitioning self-regulated learning profiles in hypermedia-learning environments. In: Proceedings of the seventh international learning analytics & knowledge conference, pp. 198-202. Manso-Vázquez, M., Caeiro-Rodríguez, M., & Llamas-Nistal, M. (2018). An Xapi application profile to monitor self-regulated learning strategies. IEEE Access, 6, 42467–42481. Matcha, W., Gašević, D., & Pardo, A. (2019). A systematic review of empirical studies on learning analytics dashboards: A self-regulated learning perspective. IEEE Transactions on Learning Technologies, 13(2), 226–245. Matcha, W., Gašević, D., Uzir, N. A., Jovanović, J., & Pardo, A. (2019). Analytics of learning strategies: Associations with academic performance and feedback. In: Proceedings of the 9th international conference on learning analytics & knowledge, pp. 461-470. Molenaar, I., Horvers, A., & Baker, R. S. (2021). What can moment-by-moment learning curves tell about students’ self-regulated learning? Learning and Instruction, 72, 101206. Montgomery, A. P., Mousavi, A., Carbonaro, M., Hayward, D. V., & Dunn, W. (2019). Using learning analytics to explore self-regulated learning in flipped blended learning music teacher education. British Journal of Educational Technology, 50(1), 114–127. Moreno-Marcos, P. M., Muñoz-Merino, P. J., Maldonado-Mahauad, J., Perez-Sanagustin, M., Alario- Hoyos, C., & Kloos, C. D. (2020). Temporal analysis for dropout prediction using self-regulated learning strategies in self-paced Moocs. Computers & Education, 145, 103728. 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. Mubarak, A. A., Cao, H., & Zhang, W. (2022). Prediction of students’ early dropout based on their interaction logs in online learning environment. Interactive Learning Environments, 30(8), 1414–1433. Nouri, J., Saqr, M., & Fors, U. (2019). Predicting performance of students in a flipped classroom using machine learning: Towards automated data-driven formative feedback. In: 10th international conference on education, training and informatics (ICETI 2019), vol 17, pp. 17-21. O’Lynn, R. G. (2021). Designing effective feedback processes in higher education: A learning-focused approach (research into higher education). The Wabash Center Journal on Teaching, 2 (2). Ott, C., Robins, A., Haden, P., & Shephard, K. (2015). Illustrating performance indicators and course characteristics to support students’ self-regulated learning in cs1. Computer Science Education, 25(2), 174–198. Papamitsiou, Z., Economides, A. A., Pappas, I. O., & Giannakos, M. N. (2018). Explaining learning performance using response-time, self-regulation and satisfaction from content: An FSQCA approach. In: Proceedings of the 8th international conference on learning analytics and knowledge, pp. 181-190. Ramaswami, G., Susnjak, T., Mathrani, A., & Umer, R. (2022). Use of predictive analytics within learning analytics dashboards: A review of case studies (pp. 1–22). Knowledge and learning: Technology. Rohloff, T., & Meinel, C. (2018). Towards personalized learning objectives in Moocs. In: European conference on technology enhanced learning, pp. 202-215. Sáiz Manzanares, M. C., Marticorena Sánchez, R., García Osorio, C. I., & Díez-Pastor, J. F. (2017). How do b-learning and learning patterns influence learning outcomes? Frontiers in Psychology, 8, 745. Scheffel, M., Kirschenmann, U., Taske, A., Adloff, K., Kiesel, M., Klemke, R., & Wolpers, M. (2013). Exploring logiassist-the mobile learning and assistance platform for truck drivers. In: European conference on technology enhanced learning, pp. 343-356. Siadaty, M., Gašević, D., & Hatala, M. (2016). Measuring the impact of technological scaffolding interventions on micro-level processes of self-regulated workplace learning. Computers in Human Behavior, 59, 469–482. Silva, J. C. S., Zambom, E., Rodrigues, R. L., Ramos, J. L. C., de Souza, F., & d. F. (2018). Effects of learning analytics on students’ self-regulated learning in flipped classroom. International Journal of Information and Communication Technology Education (IJICTE), 14(3), 91–107. Tabuenca, B., Kalz, M., Drachsler, H., & Specht, M. (2015). Time will tell: The role of mobile learning analytics in self-regulated learning. Computers & Education, 89, 53–74. Verma, S., Dickerson, J., & Hines, K. (2020). Counterfactual explanations for machine learning: A review. arXiv preprint arXiv:2010.10596. Vilkova, K. (2022). The promises and pitfalls of self-regulated learning interventions in Moocs. Technology, Knowledge and Learning, 27(3), 689–705. Wainer, J., & Cawley, G. (2021). Nested cross-validation when selecting classifiers is overzealous for most practical applications. Expert Systems with Applications, 182, 115222. Wong, J., Baars, M., Davis, D., Van Der Zee, T., Houben, G.-J., & Paas, F. (2019). Supporting self regulated learning in online learning environments and Moocs: A systematic review. International Journal of Human-Computer Interaction, 35(4–5), 356–373. You, J. W. (2016). Identifying significant indicators using lMS data to predict course achievement in online learning. The Internet and Higher Education, 29, 23–30. Yousef, A. M. F., & Khatiry, A. R. (2021). Cognitive versus behavioral learning analytics dashboards for supporting learner’s awareness, reflection, and learning process. Interactive Learning Environments, 1-17. Zimmerman, B. J. (1990). Self-regulated learning and academic achievement: An overview. Educational Psychologist, 25(1), 3–17. Zimmerman, B. J. (2002). Becoming a self-regulated learner: An overview. Theory into practice, 41(2), 64–70. Zimmerman, B. J., & Campillo, M. (2003). Motivating self-regulated problem solvers. The psychology of problem solving, p. 233262. Zimmerman, B. J., & Schunk, D. H. (2011). Handbook of self-regulation of learning and performance. Routledge/Taylor & Francis Group.