Features and Strategies of Adaptive Learning Analytics Dashboards for Supporting Self-Regulated Learning
A Systematic Literature Review
DOI:
https://doi.org/10.18608/jla.2026.9303Keywords:
adaptive learning analytics dashboard, learning analytics dashboard, self-regulated learning, systematic review, research paperAbstract
This systematic review investigates the features and strategies of adaptive learning analytics dashboards (LADs) that support self-regulated learning (SRL), mapping them to its metacognitive, motivational, and behavioural components. This mapping is intended to strengthen students’ SRL skills. Using the Kitchenham method (2004), the study systematically analyzes 69 articles published from January 2019 to October 2024 through the planning, conducting, and reporting stages. Of these articles, 25 concern conventional LADs and 44 concern adaptive LADs implemented to support SRL. The findings indicate that the adaptive characteristics of LADs help students decide what action to take after analyzing their learning data. Recommended adaptive LAD features include goal-setting, progress tracking, self-assessment, feedback mechanisms, and performance prediction. These features promote monitoring and reflection, which may improve student motivation and active engagement. Future research should analyze adaptive LAD features that support SRL components in greater depth and use longitudinal designs to examine the effects of adaptive LADs on students’ SRL abilities.
References
Ackermans, K., Bakker, M., van Loon, A.-M., Kral, M., & Camp, G. (2025). Young learners’ motivation, self-regulation and performance in personalized learning. Computers & Education, 226, Article 105208. https://doi.org/10.1016/j.compedu.2024.105208
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, Article 723447. https://doi.org/10.3389/frai.2021.723447
Aguilar, S. J., Karabenick, S. A., Teasley, S. D., & Baek, C. (2021). Associations between learning analytics dashboard exposure and motivation and self-regulated learning. Computers & Education, 162, Article 104085. https://doi.org/10.1016/j.compedu.2020.104085
Akçapınar, G., & Hasnine, M. N. (2022). Discovering the effects of learning analytics dashboard on students’ behavioral patterns using differential sequence mining. Procedia Computer Science, 207, 3818–3825. https://doi.org/10.1016/j.procs.2022.09.443
Alam, M. I., Malone, L., Nadolny, L., Brown, M., & Cervato, C. (2023). Investigating the impact of a gamified learning analytics dashboard: Student experiences and academic achievement. Journal of Computer Assisted Learning, 39(5), 1436–1449. https://doi.org/10.1111/jcal.12853
Alhazbi, S., & Hasan, M. A. (2022). Using learning analytics to explore the role of self-regulation in students’ achievements in synchronous online learning. In 2022 20th International Conference on Information Technology Based Higher Education and Training (ITHET) (pp. 1–5). IEEE. https://doi.org/10.1109/ithet56107.2022.10031804
Arnold, K. E., & Sclater, N. (2017). Student perceptions of their privacy in leaning analytics applications. In Proceedings of the Seventh International Learning Analytics & Knowledge Conference (pp. 66–69). ACM Press. https://doi.org/10.1145/3027385.3027392
Ataş, A. H., & Yıldırım, Z. (2025). A shared metacognition-focused instructional design model for online collaborative learning environments. Educational Technology Research and Development, 73(1), 567–613. https://doi.org/10.1007/s11423-024-10423-4
Bayazit, A., Ilgaz, H., Gönüllü, İ., & Erden, Ş. (2023). Profiling students via clustering in a flipped clinical skills course using learning analytics. Medical Teacher, 45(7), 724–731. https://doi.org/10.1080/0142159x.2022.2152663
Berková, K., Chalupová, M., Smrčka, F., Musil, M., & Frendlovská, D. (2024). A design of the panel for the progress and formative self-assessment detection in the learning analytics. Education and Information Technologies, 29(13), 16441–16467. https://doi.org/10.1007/s10639-024-12496-3
Carrera-Rivera, A., Ochoa, W., Larrinaga, F., & Lasa, G. (2022). How-to conduct a systematic literature review: A quick guide for computer science research. MethodsX, 9, Article 101895. https://doi.org/10.1016/j.mex.2022.101895
Cha, H.-J., & Park, T. (2019). Applying and evaluating visualization design guidelines for a MOOC dashboard to facilitate self-regulated learning based on learning analytics. KSII Transactions on Internet and Information Systems, 13(6), 2799–2823. https://doi.org/10.3837/tiis.2019.06.002
Chen, J. (2024). Effects of learning analytics-based feedback on students’ self-regulated learning and academic achievement in a blended EFL course. System, 124, Article 103388. https://doi.org/10.1016/j.system.2024.103388
Cheng, N., Zhao, W., Xu, X., Liu, H., & Tao, J. (2024). The influence of learning analytics dashboard information design on cognitive load and performance. Education and Information Technologies, 29(15), 19729–19752. https://doi.org/10.1007/s10639-024-12606-1
Chernikova, O., Heitzmann, N., Stadler, M., Holzberger, D., Seidel, T., & Fischer, F. (2020). Simulation-based learning in higher education: A meta-analysis. Review of Educational Research, 90(4), 499–541. https://doi.org/10.3102/0034654320933544
De Bruijn-Smolders, M., & Prinsen, F. R. (2024). Effective student engagement with blended learning: A systematic review. Heliyon, 10(23), Article e39439. https://doi.org/10.1016/j.heliyon.2024.e39439
Deng, H., Wang, X., Guo, Z., Decker, A., Duan, X., Wang, C., Ambrose, G. A., & Abbott, K. (2019). PerformanceVis: Visual analytics of student performance data from an introductory chemistry course. Visual Informatics, 3(4), 166–176. https://doi.org/10.1016/j.visinf.2019.10.004
Eickholt, J., Weible, J. L., & Teasley, S. D. (2022). Student-facing learning analytics dashboard: Profiles of student use. In 2022 IEEE Frontiers in Education Conference (FIE) (pp. 1–9). IEEE. https://doi.org/10.1109/fie56618.2022.9962531
Er, E., Dimitriadis, Y., & Gašević, D. (2021a). A collaborative learning approach to dialogic peer feedback: A theoretical framework. Assessment & Evaluation in Higher Education, 46(4), 586–600. https://doi.org/10.1080/02602938.2020.1786497
Er, E., Dimitriadis, Y., & Gašević, D. (2021b). Collaborative peer feedback and learning analytics: Theory-oriented design for supporting class-wide interventions. Assessment & Evaluation in Higher Education, 46(2), 169–190. https://doi.org/10.1080/02602938.2020.1764490
Espasa, A., Guasch, T., Mayordomo, R. M., & Martinez-Melo, M. (2022). Prior experience with online feedback: Its influence on students’ engagement. Distance Education, 43(3), 444–465. https://doi.org/10.1080/01587919.2022.2088480
Ez-zaouia, M., Tabard, A., & Lavoué, E. (2020). Emodash: A dashboard supporting retrospective awareness of emotions in online learning. International Journal of Human-Computer Studies, 139, Article 102411. https://doi.org/10.1016/j.ijhcs.2020.102411
Fan, Y., Matcha, W., Uzir, N. A. A., Wang, Q., & Gašević, D. (2021). Learning analytics to reveal links between learning design and self-regulated learning. International Journal of Artificial Intelligence in Education, 31(4), 980–1021. https://doi.org/10.1007/s40593-021-00249-z
Farrell, T., Alani, H., & Mikroyannidis, A. (2024). Mediating learning with learning analytics technology: Guidelines for practice. Teaching in Higher Education, 29(6), 1500–1520. https://doi.org/10.1080/13562517.2022.2067745
Fleur, D. S., Marshall, M., Pieters, M., Brouwer, N., Oomens, G., Konstantinidis, A., Winnips, K., Moes, S., Van den Bos, W., Bredeweg, B., & Van Vliet, E. A. (2023). IguideME: Supporting self-regulated learning and academic achievement with personalized peer-comparison feedback in higher education. Journal of Learning Analytics, 10(2), 100–114. https://doi.org/10.18608/jla.2023.7853
Fleur, D. S., van den Bos, W., & Bredeweg, B. (2023). Social comparison in learning analytics dashboard supporting motivation and academic achievement. Computers and Education Open, 4, Article 100130. https://doi.org/10.1016/j.caeo.2023.100130
Fu, Q., Bai, X., Zheng, Y., Du, R., Wang, D., & Zhang, T. (2023). VisOJ: Real-time visual learning analytics dashboard for online programming judge. The Visual Computer, 39(6), 2393–2405. https://doi.org/10.1007/s00371-022-02586-z
Gallagher, T., Slof, B., van der Schaaf, M., Arztmann, M., Fracaro, S. G., & Kester, L. (2024). Learning analytics dashboard design: Workplace learner preferences for reference frames in immersive training in practice. Journal of Computer Assisted Learning, 40(6), 2840–2855. https://doi.org/10.1111/jcal.13042
Gallagher, T., Slof, B., van der Schaaf, M., Toyoda, R., Tehreem, Y., Garcia Fracaro, S., & Kester, L. (2024). Reference frames for learning analytics dashboards: The progress and social reference frame and occupational self‐efficacy. Journal of Computer Assisted Learning, 40(2), 742–760. https://doi.org/10.1111/jcal.12912
Gelan, A., Fastré, G., Verjans, M., Martin, N., Janssenswillen, G., Creemers, M., Lieben, J., Depaire, B., & Thomas, M. (2018). Affordances and limitations of learning analytics for computer-assisted language learning: A case study of the VITAL project. Computer Assisted Language Learning, 31(3), 294–319. https://doi.org/10.1080/09588221.2017.1418382
Han, J., Kim, K. H., Rhee, W., & Cho, Y. H. (2021). Learning analytics dashboards for adaptive support in face-to-face collaborative argumentation. Computers & Education, 163, Article 104041. https://doi.org/10.1016/j.compedu.2020.104041
Heikkinen, S., Saqr, M., Malmberg, J., & Tedre, M. (2023). Supporting self-regulated learning with learning analytics interventions – a systematic literature review. Education and Information Technologies, 28(3), 3059–3088. https://doi.org/10.1007/s10639-022-11281-4
Hellings, J., & Haelermans, C. (2022). The effect of providing learning analytics on student behaviour and performance in programming: A randomised controlled experiment. Higher Education, 83(1), 1–18. https://doi.org/10.1007/s10734-020-00560-z
Hooshyar, D., Pedaste, M., Saks, K., Leijen, Ä., Bardone, E., & Wang, M. (2020). Open learner models in supporting self-regulated learning in higher education: A systematic literature review. Computers & Education, 154, Article 103878. https://doi.org/10.1016/j.compedu.2020.103878
Hsu, C.-Y., Horikoshi, I., Li, H., Majumdar, R., & Ogata, H. (2022). Extracting students’ self-regulation strategies in an online extensive reading environment using the experience API (xAPI). In Proceedings of the 30th International Conference on Computers in Education (pp. 326–331). Asia-Pacific Society for Computers in Education. https://library.apsce.net/index.php/ICCE/article/view/4501
Hsu, C.-Y., Horikoshi, I., Li, H., Majumdar, R., & Ogata, H. (2023). Supporting “time awareness” in self-regulated learning: How do students allocate time during exam preparation? Smart Learning Environments, 10(1), Article 21. https://doi.org/10.1186/s40561-023-00243-z
Hu, Y.-H., Liao, B.-K., & Hsieh, C.-L. (2024). Effectiveness of a gamified learning analytics dashboard with coregulation mechanism for self-regulated learning in college ethics courses. Interactive Learning Environments, 32(10), 6628–6644. https://doi.org/10.1080/10494820.2023.2277741
Huang, L., Zheng, J., Lajoie, S. P., Chen, Y., Hmelo-Silver, C. E., & Wang, M. (2024). Examining university teachers’ self-regulation in using a learning analytics dashboard for online collaboration. Education and Information Technologies, 29(7), 8523–8547. https://doi.org/10.1007/s10639-023-12131-7
Ifenthaler, D., Schumacher, C., & Kuzilek, J. (2023). Investigating students' use of self‐assessments in higher education using learning analytics. Journal of Computer Assisted Learning, 39(1), 255–268. https://doi.org/10.1111/jcal.12744
Ipinnaiye, O., & Risquez, A. (2024). Exploring adaptive learning, learner-content interaction and student performance in undergraduate economics classes. Computers & Education, 215, Article 105047. https://doi.org/10.1016/j.compedu.2024.105047
Jääskelä, P., Heilala, V., Kärkkäinen, T., & Häkkinen, P. (2021). Student agency analytics: Learning analytics as a tool for analysing student agency in higher education. Behaviour & Information Technology, 40(8), 790–808. https://doi.org/10.1080/0144929x.2020.1725130
Järvelä, S., & Hadwin, A. (2024). Triggers for self-regulated learning: A conceptual framework for advancing multimodal research about SRL. Learning and Individual Differences, 115, Article 102526. https://doi.org/10.1016/j.lindif.2024.102526
Jivet, I. (2021). The dashboard that loved me: Designing adaptive learning analytics for self-regulated learning [Doctoral dissertation, Open Universiteit]. https://research.ou.nl/en/publications/the-dashboard-that-loved-me-designing-adaptive-learning-analytics/
Jivet, I., Scheffel, M., Drachsler, H., & Specht, M. (2017). Awareness is not enough: Pitfalls of learning analytics dashboards in the educational practice. In É. Lavoué, H. Drachsler, K. Verbert, J. Broisin, & M. Pérez-Sanagustín (Eds.), Data driven approaches in digital education (Vol. 10474, pp. 82–96). Springer. https://doi.org/10.1007/978-3-319-66610-5_7
Joseph-Richard, P., Uhomoibhi, J., & Jaffrey, A. (2021). Predictive learning analytics and the creation of emotionally adaptive learning environments in higher education institutions: A study of students' affect responses. The International Journal of Information and Learning Technology, 38(2), 243–257. https://doi.org/10.1108/ijilt-05-2020-0077
Karaoglan Yilmaz, F. G. (2022). Utilizing learning analytics to support students' academic self-efficacy and problem-solving skills. The Asia-Pacific Education Researcher, 31(2), 175–191. https://doi.org/10.1007/s40299-020-00548-4
Karaoglan Yilmaz, F. G., & Yilmaz, R. (2021). Learning analytics as a metacognitive tool to influence learner transactional distance and motivation in online learning environments. Innovations in Education and Teaching International, 58(5), 575–585. https://doi.org/10.1080/14703297.2020.1794928
Karlen, Y., Hertel, S., & Hirt, C. N. (2020). Teachers’ professional competences in self-regulated learning: An approach to integrate teachers’ competences as self-regulated learners and as agents of self-regulated learning in a holistic manner. Frontiers in Education, 5, Article 159. https://doi.org/10.3389/feduc.2020.00159
Kaur, A., & Chahal, K. K. (2024). A learning analytics dashboard for data-driven recommendations on influences of non-cognitive factors in introductory programming. Education and Information Technologies, 29(8), 9221–9256. https://doi.org/10.1007/s10639-023-12125-5
Keuning, T., & van Geel, M. (2021). Differentiated teaching with adaptive learning systems and teacher dashboards: The teacher still matters most. IEEE Transactions on Learning Technologies, 14(2), 201–210. https://doi.org/10.1109/tlt.2021.3072143
Kitchenham, B. (2004). Procedures for performing systematic reviews (Keele University Technical Report TR/SE-0401; NICTA Technical Report 0400011T.1). Keele University & National ICT Australia.
Kitchenham, B., & Charters, S. (2007). Guidelines for performing systematic literature reviews in software engineering (Version 2.3; EBSE Technical Report EBSE-2007-01). Keele University & Durham University.
Kılıç, A. H., & İzmirli, S. (2024). A systematic literature review of articles on learning analytics. Asian Journal of Distance Education, 19(2), 187–202. https://www.asianjde.com/ojs/index.php/AsianJDE/article/view/799
Kleimola, R., Hirsto, L., & Ruokamo, H. (2025). Promoting higher education students’ self-regulated learning through learning analytics: A qualitative study. Education and Information Technologies, 30(4), 4959–4986. https://doi.org/10.1007/s10639-024-12978-4
Knoop-van Campen, C. A. N., van der Graaf, J., Horvers, A., Kooi, R., Dijkstra, R., & Molenaar, I. (2024). Enacting control with student dashboards: The role of motivation. Journal of Computer Assisted Learning, 40(3), 1137–1153. https://doi.org/10.1111/jcal.12936
Krishnan, K. V., Venugopalan, M., & Sahib, P. R. (2024). Tailoring success: How individualised learning and increased awareness can combat dropout in skill development programs. Journal of Open Innovation: Technology, Market, and Complexity, 10(4), Article 100430. https://doi.org/10.1016/j.joitmc.2024.100430
Liao, C.-H., & Wu, J.-Y. (2022). Deploying multimodal learning analytics models to explore the impact of digital distraction and peer learning on student performance. Computers & Education, 190, Article 104599. https://doi.org/10.1016/j.compedu.2022.104599
Liebendörfer, M., Kempen, L., & Schukajlow, S. (2023). First-year university students' self-regulated learning during the COVID-19 pandemic: A qualitative longitudinal study. ZDM – Mathematics Education, 55(1), 119–131. https://doi.org/10.1007/s11858-022-01444-5
Lim, L., Bannert, M., van der Graaf, J., Fan, Y., Rakovic, M., Singh, S., Molenaar, I., & Gašević, D. (2024). How do students learn with real‐time personalized scaffolds? British Journal of Educational Technology, 55(4), 1309–1327. https://doi.org/10.1111/bjet.13414
Lim, L., Bannert, M., van der Graaf, J., Singh, S., Fan, Y., Surendrannair, S., Rakovic, M., Molenaar, I., Moore, J., & Gašević, D. (2023). Effects of real-time analytics-based personalized scaffolds on students’ self-regulated learning. Computers in Human Behavior, 139, Article 107547. https://doi.org/10.1016/j.chb.2022.107547
Lim, L.-A., Dawson, S., Gašević, D., Joksimović, S., Pardo, A., Fudge, A., & Gentili, S. (2021). Students’ perceptions of, and emotional responses to, personalised learning analytics-based feedback: An exploratory study of four courses. Assessment & Evaluation in Higher Education, 46(3), 339–359. https://doi.org/10.1080/02602938.2020.1782831
Lim, L.-A., Gentili, S., Pardo, A., Kovanović, V., Whitelock-Wainwright, A., Gašević, D., & Dawson, S. (2021). What changes, and for whom? A study of the impact of learning analytics-based process feedback in a large course. Learning and Instruction, 72, Article 101202. https://doi.org/10.1016/j.learninstruc.2019.04.003
Lu, H., & Zhang, X. (2024). Multi-level students’ sense of community development in hybrid and online learning environments at higher educational institutions: A systematic literature review. Interactive Learning Environments, 32(9), 5397–5420. https://doi.org/10.1080/10494820.2023.2214803
MacFarlane, A., Russell-Rose, T., & Shokraneh, F. (2022). Search strategy formulation for systematic reviews: Issues, challenges and opportunities. Intelligent Systems with Applications, 15, Article 200091. https://doi.org/10.1016/j.iswa.2022.200091
Maheshi, B., Dai, W., Martinez-Maldonado, R., & Tsai, Y. S. (2024). Dialogic feedback at scale: Recommendations for learning analytics design. Journal of Computer Assisted Learning, 40(6), 2790–2808. https://doi.org/10.1111/jcal.13034
Matcha, W., Uzir, N. A., Gašević, D., & Pardo, A. (2020). A systematic review of empirical studies on learning analytics dashboards: A self-regulated learning perspective. IEEE Transactions on Learning Technologies, 13(2), 226–245. https://doi.org/10.1109/tlt.2019.2916802
Molenaar, I., Horvers, A., Dijkstra, R., & Baker, R. S. (2020). Personalized visualizations to promote young learners' SRL. In Proceedings of the Tenth International Conference on Learning Analytics & Knowledge (pp. 330–339). ACM. https://doi.org/10.1145/3375462.3375465
Mougiakou, S., Vinatsella, D., Sampson, D., Papamitsiou, Z., Giannakos, M., & Ifenthaler, D. (2023). Educational data analytics for teachers and school leaders. Springer. https://doi.org/10.1007/978-3-031-15266-5
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. https://doi.org/10.1080/10494820.2020.1727529
Nguyen, A., Tuunanen, T., Gardner, L., & Sheridan, D. (2021). Design principles for learning analytics information systems in higher education. European Journal of Information Systems, 30(5), 541–568. https://doi.org/10.1080/0960085x.2020.1816144
Osakwe, I., Chen, G., Fan, Y., Rakovic, M., Li, X., Singh, S., Molenaar, I., Bannert, M., & Gašević, D. (2023). Reinforcement learning for automatic detection of effective strategies for self-regulated learning. Computers and Education: Artificial Intelligence, 5, Article 100181. https://doi.org/10.1016/j.caeai.2023.100181
Ouyang, F., Zhang, L., Wu, M., & Jiao, P. (2024). Empowering collaborative knowledge construction through the implementation of a collaborative argument map tool. The Internet and Higher Education, 62, Article 100946. https://doi.org/10.1016/j.iheduc.2024.100946
Paredes, Y. V., Huang, P.-K., & Hsiao, I.-H. (2019). Utilising behavioural analytics in a blended programming learning environment. New Review of Hypermedia and Multimedia, 25(3), 89–111. https://doi.org/10.1080/13614568.2019.1695961
Park, Y., & Jo, I.-H. (2019). Factors that affect the success of learning analytics dashboards. Educational Technology Research and Development, 67(6), 1547–1571. https://doi.org/10.1007/s11423-019-09693-0
Pintrich, P. R. (2004). A conceptual framework for assessing motivation and self-regulated learning in college students. Educational Psychology Review, 16(4), 385–407. https://doi.org/10.1007/s10648-004-0006-x
Pintrich, P. R., & De Groot, E. V. (1990). Motivational and self-regulated learning components of classroom academic performance. Journal of Educational Psychology, 82(1), 33–40. https://doi.org/10.1037/0022-0663.82.1.33
Ramaswami, G., Susnjak, T., & Mathrani, A. (2023). Effectiveness of a learning analytics dashboard for increasing student engagement levels. Journal of Learning Analytics, 10(3), 115–134. https://doi.org/10.18608/jla.2023.7935
Schwendimann, B. A., Rodríguez-Triana, M. J., Vozniuk, A., Prieto, L. P., Shirvani Boroujeni, M., Holzer, A., Gillet, D., & Dillenbourg, P. (2017). Perceiving learning at a glance: A systematic literature review of learning dashboard research. IEEE Transactions on Learning Technologies, 10(1), 30–41. https://doi.org/10.1109/TLT.2016.2599522
Sedrakyan, G., Malmberg, J., Verbert, K., Järvelä, S., & Kirschner, P. A. (2020). Linking learning behavior analytics and learning science concepts: Designing a learning analytics dashboard for feedback to support learning regulation. Computers in Human Behavior, 107, Article 105512. https://doi.org/10.1016/j.chb.2018.05.004
Sedrakyan, G., Mannens, E., & Verbert, K. (2019). Guiding the choice of learning dashboard visualizations: Linking dashboard design and data visualization concepts. Journal of Computer Languages, 50, 19–38. https://doi.org/10.1016/j.jvlc.2018.11.002
Shadiev, R., Yi, S., & Altinay, F. (2024). Cultivating self-directed learning abilities in K-12 students through immersive online virtual tours. Interactive Learning Environments, 32(10), 7338–7363. https://doi.org/10.1080/10494820.2024.2312923
Silva, R. F. D., Gimenes, I. M. S., & Maldonado, J. C. (2021). Analyzing learners' behavior and discourse within large online communities: A social learning analytics dashboard. In Anais do XXXII Simpósio Brasileiro de Informática na Educação (SBIE 2021) (pp. 576–585). Sociedade Brasileira de Computação. https://doi.org/10.5753/sbie.2021.217468
Society for Learning Analytics Research. (2025). What is learning analytics? SoLAR. https://www.solaresearch.org/about/what-is-learning-analytics/
Tepgec, M., Heil, J., & Ifenthaler, D. (2025). Feedback literacy matters: Unlocking the potential of learning analytics-based feedback. Assessment & Evaluation in Higher Education, 50(1), 50–66. https://doi.org/10.1080/02602938.2024.2367587
Topali, P., Ortega-Arranz, A., Asensio-Pérez, J. I., Villagrá-Sobrino, S. L., Martínez-Monés, A., & Dimitriadis, Y. (2025). e-FeeD4Mi: Human-centred design of personalised and contextualised feedback in MOOCs. Behaviour & Information Technology, 44(5), 1053–1070. https://doi.org/10.1080/0144929x.2024.2376201
Toyokawa, Y., Majumdar, R., Kondo, T., Horikoshi, I., & Ogata, H. (2024). Active reading dashboard in a learning analytics enhanced language-learning environment: Effects on learning behavior and performance. Journal of Computers in Education, 11(2), 495–522. https://doi.org/10.1007/s40692-023-00267-x
Tretow-Fish, T. A. B., Khalid, M. S., & Leweke, V. A. C. (2023). Prototyping the learning analytics dashboards of an adaptive learning platform: Faculty perceptions versus designers’ intentions. In P. Zaphiris & A. Ioannou (Eds.), Learning and collaboration technologies (Vol. 14040, pp. 531–545). Springer. https://doi.org/10.1007/978-3-031-34411-4_36
Ustun, A. B., Zhang, K., Karaoğlan-Yilmaz, F. G., & Yilmaz, R. (2023). Learning analytics based feedback and recommendations in flipped classrooms: An experimental study in higher education. Journal of Research on Technology in Education, 55(5), 841–857. https://doi.org/10.1080/15391523.2022.2040401
Valle, N., Antonenko, P., Valle, D., Sommer, M., Huggins-Manley, A. C., Dawson, K., Kim, D., & Baiser, B. (2021). Predict or describe? How learning analytics dashboard design influences motivation and statistics anxiety in an online statistics course. Educational Technology Research and Development, 69(3), 1405–1431. https://doi.org/10.1007/s11423-021-09998-z
van de Oudeweetering, K., Knox, J., & Decuypere, M. (2024). Problematizing feedback loops: ‘On’, ‘with’, and ‘beyond’ analytics dashboards in MOOCs. Learning, Media and Technology, 49(4), 685–700. https://doi.org/10.1080/17439884.2023.2264188
van Leeuwen, A. (2023). Teachers’ experiences of monitoring their students in online higher education: Recommendations for course design and opportunities for learning analytics. Technology, Pedagogy and Education, 32(5), 589–604. https://doi.org/10.1080/1475939x.2023.2254297
van Sluijs, M., & Matzat, U. (2024). Predicting time‐management skills from learning analytics. Journal of Computer Assisted Learning, 40(2), 525–537. https://doi.org/10.1111/jcal.12893
Verbert, K., Govaerts, S., Duval, E., Santos, J. L., Van Assche, F., Parra, G., & Klerkx, J. (2014). Learning dashboards: An overview and future research opportunities. Personal and Ubiquitous Computing, 18, 1499–1514. https://doi.org/10.1007/s00779-013-0751-2
Viberg, O., Khalil, M., & Baars, M. (2020). Self-regulated learning and learning analytics in online learning environments. In Proceedings of the Tenth International Conference on Learning Analytics & Knowledge (pp. 524–533). ACM. https://doi.org/10.1145/3375462.3375483
Villagrán, I., Hernández, R., Schuit, G., Neyem, A., Fuentes, J., Larrondo, L., Margozzini, E., Hurtado, M. T., Iriarte, Z., Miranda, C., Varas, J., & Hilliger, I. (2024). Enhancing feedback uptake and self-regulated learning in procedural skills training: Design and evaluation of a learning analytics dashboard. Journal of Learning Analytics, 11(2), 138–156. https://doi.org/10.18608/jla.2024.8195
Wan, H., Zhong, Z., Tang, L., & Gao, X. (2023). Pedagogical interventions in SPOCs: Learning behavior dashboards and knowledge tracing support exercise recommendation. IEEE Transactions on Learning Technologies, 16(3), 431–442. https://doi.org/10.1109/tlt.2023.3242712
Wang, D., & Han, H. (2021). Applying learning analytics dashboards based on process‐oriented feedback to improve students' learning effectiveness. Journal of Computer Assisted Learning, 37(2), 487–499. https://doi.org/10.1111/jcal.12502
Xia, X., & Qi, W. (2023). Dropout prediction and decision feedback supported by multi temporal sequences of learning behavior in MOOCs. International Journal of Educational Technology in Higher Education, 20(1), Article 32. https://doi.org/10.1186/s41239-023-00400-x
Xu, B., Huang, Z., Liu, J., Shen, S., Liu, Q., Chen, E., Wu, J., & Wang, S. (2023). Learning behavior-oriented knowledge tracing. In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (pp. 2789–2800). ACM. https://doi.org/10.1145/3580305.3599407
Xu, X., Zhao, W., Li, Y., Qiao, L., Tao, J., & Liu, F. (2025). The impact of visualizations with learning paths on college students’ online self-regulated learning. Education and Information Technologies, 30(3), 2917–2940. https://doi.org/10.1007/s10639-024-12933-3
Yan, H., Lin, F., & Kinshuk. (2021). Including learning analytics in the loop of self-paced online course learning design. International Journal of Artificial Intelligence in Education, 31(4), 878–895. https://doi.org/10.1007/s40593-020-00225-z
Yang, C. C. Y., Wu, J.-Y., & Ogata, H. (2025). Learning analytics dashboard-based self-regulated learning approach for enhancing students’ e-book-based blended learning. Education and Information Technologies, 30(1), 35–56. https://doi.org/10.1007/s10639-024-12913-7
Yang, Y., Majumdar, R., Li, H., Flanagan, B., & Ogata, H. (2024). Design of a learning dashboard to enhance reading outcomes and self-directed learning behaviors in out-of-class extensive reading. Interactive Learning Environments, 32(3), 892–909. https://doi.org/10.1080/10494820.2022.2101126
Yılmaz, R. (2020). Enhancing community of inquiry and reflective thinking skills of undergraduates through using learning analytics‐based process feedback. Journal of Computer Assisted Learning, 36(6), 909–921. https://doi.org/10.1111/jcal.12449
Yousef, A. M. F., & Khatiry, A. R. (2023). Cognitive versus behavioral learning analytics dashboards for supporting learner’s awareness, reflection, and learning process. Interactive Learning Environments, 31(9), 5460–5476. https://doi.org/10.1080/10494820.2021.2009881
Zheng, J., Huang, L., Li, S., Lajoie, S. P., Chen, Y., & Hmelo-Silver, C. E. (2021). Self-regulation and emotion matter: A case study of instructor interactions with a learning analytics dashboard. Computers & Education, 161, Article 104061. https://doi.org/10.1016/j.compedu.2020.104061
Zheng, L. (2021). Learning analytics for computer-supported collaborative learning design. In Data-driven design for computer-supported collaborative learning: Design matters (pp. 31–43). Springer. https://doi.org/10.1007/978-981-16-1718-8_3
Zheng, L., Zhong, L., & Niu, J. (2022). Effects of personalised feedback approach on knowledge building, emotions, co-regulated behavioural patterns and cognitive load in online collaborative learning. Assessment & Evaluation in Higher Education, 47(1), 109–125. https://doi.org/10.1080/02602938.2021.1883549
Zimmerman, B. J. (2000). Attaining self-regulation: A social cognitive perspective. In M. Boekaerts, P. R. Pintrich, & M. Zeidner (Eds.), Handbook of self-regulation (pp. 13–39). Elsevier.
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
Zimmerman, B. J., & Schunk, D. H. (Eds.). (2001). Self-regulated learning and academic achievement: Theoretical perspectives (2nd ed.). Routledge. https://doi.org/10.4324/9781410601032
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