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  • Yun H, Song H and Kim Y. (2024). Identifying university students’ online self-regulated learning profiles: predictors, outcomes, and differentiated instructional strategies. European Journal of Psychology of Education. 10.1007/s10212-024-00907-5. 40:1. Online publication date: 1-Mar-2025.

    https://link.springer.com/10.1007/s10212-024-00907-5

  • Lee H and Bosch N. (2024). Subtopic-specific heterogeneity in computer-based learning behaviors. International Journal of STEM Education. 10.1186/s40594-024-00519-x. 11:1.

    https://stemeducationjournal.springeropen.com/articles/10.1186/s40594-024-00519-x

  • Winter M, Mordel J, Mendzheritskaya J, Biedermann D, Ciordas-Hertel G, Hahnel C, Bengs D, Wolter I, Goldhammer F, Drachsler H, Artelt C and Horz H. (2024). Behavioral trace data in an online learning environment as indicators of learning engagement in university students. Frontiers in Psychology. 10.3389/fpsyg.2024.1396881. 15.

    https://www.frontiersin.org/articles/10.3389/fpsyg.2024.1396881/full

  • Alhazbi S, Al‐ali A, Tabassum A, Al‐Ali A, Al‐Emadi A, Khattab T and Hasan M. (2024). Using learning analytics to measure self‐regulated learning: A systematic review of empirical studies in higher education . Journal of Computer Assisted Learning. 10.1111/jcal.12982. 40:4. (1658-1674). Online publication date: 1-Aug-2024.

    https://onlinelibrary.wiley.com/doi/10.1111/jcal.12982

  • Tang H. (2024). Understanding self-regulated learning and learner performance in MOOCs. Distance Education. 10.1080/01587919.2024.2338712. (1-16).

    https://www.tandfonline.com/doi/full/10.1080/01587919.2024.2338712

  • Zhang Y, Ye Y, Paquette L, Wang Y and Hu X. (2024). Investigating the reliability of aggregate measurements of learning process data: From theory to practice. Journal of Computer Assisted Learning. 10.1111/jcal.12951. 40:3. (1295-1308). Online publication date: 1-Jun-2024.

    https://onlinelibrary.wiley.com/doi/10.1111/jcal.12951

  • van Sluijs M and Matzat U. (2023). Predicting time‐management skills from learning analytics. Journal of Computer Assisted Learning. 10.1111/jcal.12893. 40:2. (525-537). Online publication date: 1-Apr-2024.

    https://onlinelibrary.wiley.com/doi/10.1111/jcal.12893

  • Bourguet M. Demonstrating the impact of study regularity on academic success using learning analytics. Proceedings of the 14th Learning Analytics and Knowledge Conference. (736-741).

    https://doi.org/10.1145/3636555.3636845

  • Afzaal M, Zia A, Nouri J and Fors U. (2023). Informative Feedback and Explainable AI-Based Recommendations to Support Students’ Self-regulation. Technology, Knowledge and Learning. 10.1007/s10758-023-09650-0. 29:1. (331-354). Online publication date: 1-Mar-2024.

    https://link.springer.com/10.1007/s10758-023-09650-0

  • Yu Z, Xu W and Sukjairungwattana P. (2022). A meta-analysis of eight factors influencing MOOC-based learning outcomes across the world. Interactive Learning Environments. 10.1080/10494820.2022.2096641. 32:2. (707-726). Online publication date: 7-Feb-2024.

    https://www.tandfonline.com/doi/full/10.1080/10494820.2022.2096641

  • Xu X, Su Y, Hong W, Zhang Y and Zhuang T. (2024). The impact of a Personal Learning Environment on Chinese postgraduates’ online self-regulated learning skills / Impacto de un Entorno Personal de Aprendizaje en las aptitudes de aprendizaje autorregulado en línea de estudiantes de posgrado en China . Journal for the Study of Education and Development: Infancia y Aprendizaje. 10.1177/02103702231225382. 47:1. (173-205). Online publication date: 1-Feb-2024.

    https://journals.sagepub.com/doi/10.1177/02103702231225382

  • Ober T, Cheng Y, Carter M and Liu C. (2023). Leveraging performance and feedback‐seeking indicators from a digital learning platform for early prediction of students' learning outcomes. Journal of Computer Assisted Learning. 10.1111/jcal.12870. 40:1. (219-240). Online publication date: 1-Feb-2024.

    https://onlinelibrary.wiley.com/doi/10.1111/jcal.12870

  • Torres Jiménez S, Ramírez-Echeverry J and Restrepo-Calle F. (2023). The Development and Validation of the Questionnaire to Characterize Learning Strategies in Computer Programming (CEAPC). Journal of Educational Computing Research. 10.1177/07356331231183450. 61:8. (103-138). Online publication date: 1-Jan-2024.

    http://journals.sagepub.com/doi/10.1177/07356331231183450

  • Boulahmel A, Djelil F, Gilliot J, Leray P and Smits G. (2024). Mining Discriminative Sequential Patterns of Self-regulated Learners. Generative Intelligence and Intelligent Tutoring Systems. 10.1007/978-3-031-63031-6_12. (137-149).

    https://link.springer.com/10.1007/978-3-031-63031-6_12

  • Fajri N, Syahputra Y, Putri Karisma S and Ifdil I. (2023). Navigating Academic Challenges: Self-Regulated Learning Analysis of Academic Procrastination Students. KONSELOR. 10.24036/0202312244-0-86. 12:2. (65-73).

    https://counselor.ppj.unp.ac.id/index.php/konselor/article/view/44

  • Atabay M and Çakiroğlu Ü. (2023). Learners' Online Interactions and Their Self-Regulation Learning Skills From a Learning Analytics Perspective. Perspectives on Learning Analytics for Maximizing Student Outcomes. 10.4018/978-1-6684-9527-8.ch015. (305-317).

    https://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/978-1-6684-9527-8.ch015

  • Butakor P. (2023). EXPLORING PRE-SERVICE TEACHERS’ BELIEFS ABOUT THE ROLE OF ARTIFICIAL INTELLIGENCE IN HIGHER EDUCATION IN GHANA. International Journal of Innovative Technologies in Social Science. 10.31435/rsglobal_ijitss/30092023/8057:3(39).

    https://rsglobal.pl/index.php/ijitss/article/view/2613

  • Hilpert J, Greene J and Bernacki M. (2023). Leveraging complexity frameworks to refine theories of engagement: Advancing self‐regulated learning in the age of artificial intelligence. British Journal of Educational Technology. 10.1111/bjet.13340. 54:5. (1204-1221). Online publication date: 1-Sep-2023.

    https://bera-journals.onlinelibrary.wiley.com/doi/10.1111/bjet.13340

  • Damayanti A, Kusumawardani S and Wibirama S. (2023). A Review of Learners' Self-Regulated Learning Behavior Analysis Using Log-Data Traces 2023 IEEE 12th International Conference on Engineering Education (ICEED). 10.1109/ICEED59801.2023.10264050. 979-8-3503-0742-9. (90-95).

    https://ieeexplore.ieee.org/document/10264050/

  • Men Q, Gimbert B and Cristol D. (2023). The Effect of Self-Regulated Learning in Online Professional Training. International Journal of Mobile and Blended Learning. 15:2. (1-17). Online publication date: 16-Feb-2023.

    https://doi.org/10.4018/IJMBL.318225

  • Fessl A, Divitini M and Maitz K. (2023). Transferring Digital Competences for Teaching from Theory into Practice Through Reflection. Responsive and Sustainable Educational Futures. 10.1007/978-3-031-42682-7_41. (554-559).

    https://link.springer.com/10.1007/978-3-031-42682-7_41

  • Mirzababaei B, Maitz K, Fessl A and Pammer-Schindler V. (2023). Interactive Web-Based Learning Materials Vs. Tutorial Chatbot: Differences in User Experience. Responsive and Sustainable Educational Futures. 10.1007/978-3-031-42682-7_15. (213-228).

    https://link.springer.com/10.1007/978-3-031-42682-7_15

  • Wise E, Adams R, Lyketsos C and Leoutsakos J. (2022). Graphical methods for understanding changes in states: Understanding medication use pathways. International Journal of Methods in Psychiatric Research. 10.1002/mpr.1932. 31:4. Online publication date: 1-Dec-2022.

    https://onlinelibrary.wiley.com/doi/10.1002/mpr.1932

  • Alhazbi S and Hasan M. (2022). Using Learning Analytics to Explore the Role of Self-regulation in students’ Achievements in Synchronous Online Learning 2022 20th International Conference on Information Technology Based Higher Education and Training (ITHET). 10.1109/ITHET56107.2022.10031804. 978-1-6654-8908-9. (1-5).

    https://ieeexplore.ieee.org/document/10031804/

  • Tang H and Bao Y. (2022). Self-regulated learner profiles in MOOCs: A cluster analysis based on the item response theory. Interactive Learning Environments. 10.1080/10494820.2022.2129394. (1-17).

    https://www.tandfonline.com/doi/full/10.1080/10494820.2022.2129394

  • Pardos Z, Borchers C and Yu R. (2022). Credit hours is not enough: Explaining undergraduate perceptions of course workload using LMS records. The Internet and Higher Education. 10.1016/j.iheduc.2022.100882. (100882). Online publication date: 1-Aug-2022.

    https://linkinghub.elsevier.com/retrieve/pii/S1096751622000380

  • Etxebarria B, Sánchez F, Rojo N and Barona A. (2022). Multiple Intelligence Informed Resources for Addressing Sustainable Development Goals in Management Engineering. Sustainability. 10.3390/su14148439. 14:14. (8439).

    https://www.mdpi.com/2071-1050/14/14/8439

  • Yu H, Hu R and Chen M. (2022). Global Pandemic Prevention Continual Learning—Taking Online Learning as an Example: The Relevance of Self-Regulation, Mind-Unwandered, and Online Learning Ineffectiveness. Sustainability. 10.3390/su14116571. 14:11. (6571).

    https://www.mdpi.com/2071-1050/14/11/6571

  • Lorås M, Sindre G, Trætteberg H and Aalberg T. (2021). Study Behavior in Computing Education—A Systematic Literature Review. ACM Transactions on Computing Education. 22:1. (1-40). Online publication date: 31-Mar-2022.

    https://doi.org/10.1145/3469129

  • Rizki P, Handoko I, Purnama P and Rustam D. (2022). Promoting Self-Regulated Learning for Students in Underdeveloped Areas: The Case of Indonesia Nationwide Online-Learning Program. Sustainability. 10.3390/su14074075. 14:7. (4075).

    https://www.mdpi.com/2071-1050/14/7/4075

  • Kent C, Akanji A, du Boulay B, Bashir I, Fikes T, Rodríguez De Jesús S, Ramirez Hall A, Alvarado P, Jones J, Cukurova M, Sher V, Blake C, Fisher A, Greenwood J and Luckin R. (2022). Mind the Gap. Applying Data Science and Learning Analytics Throughout a Learner’s Lifespan. 10.4018/978-1-7998-9644-9.ch001. (1-26).

    https://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/978-1-7998-9644-9.ch001

  • Seidel N, Karolyi H, Burchart M and de Witt C. (2022). Approaching Adaptive Support for Self-regulated Learning. Innovations in Learning and Technology for the Workplace and Higher Education. 10.1007/978-3-030-90677-1_39. (409-424).

    https://link.springer.com/10.1007/978-3-030-90677-1_39

  • Dass S, Gary K and Cunningham J. (2021). Predicting Student Dropout in Self-Paced MOOC Course Using Random Forest Model. Information. 10.3390/info12110476. 12:11. (476).

    https://www.mdpi.com/2078-2489/12/11/476

  • Liu L, Caliph S, Simpson C, Khoo R, Neviles G, Muthumuni S and Lyons K. (2021). Pharmacy Student Challenges and Strategies towards Initial COVID-19 Curriculum Changes. Healthcare. 10.3390/healthcare9101322. 9:10. (1322).

    https://www.mdpi.com/2227-9032/9/10/1322

  • Gardner J, O'Leary M and Yuan L. (2021). Artificial intelligence in educational assessment: ‘Breakthrough? Or buncombe and ballyhoo?’. Journal of Computer Assisted Learning. 10.1111/jcal.12577. 37:5. (1207-1216). Online publication date: 1-Oct-2021.

    https://onlinelibrary.wiley.com/doi/10.1111/jcal.12577

  • Dominguez C, Garcia-Izquierdo F, Jaime A, Perez B, Rubio A and Zapata M. Using Process Mining to Analyze Time Distribution of Self-Assessment and Formative Assessment Exercises on an Online Learning Tool. IEEE Transactions on Learning Technologies. 10.1109/TLT.2021.3119224. 14:5. (709-722).

    https://ieeexplore.ieee.org/document/9566834/

  • Chung C and Hsiao I. Examining the Effect of Self-explanations in Distributed Self-assessment. Technology-Enhanced Learning for a Free, Safe, and Sustainable World. (149-162).

    https://doi.org/10.1007/978-3-030-86436-1_12

  • Gonzalez-Nucamendi A, Noguez J, Neri L, Robledo-Rella V, García-Castelán R and Escobar-Castillejos D. (2021). The prediction of academic performance using engineering student’s profiles. Computers and Electrical Engineering. 93:C. Online publication date: 1-Jul-2021.

    https://doi.org/10.1016/j.compeleceng.2021.107288

  • Gledson A, Apaolaza A, Barthold S, Günther F, Yu H and Vigo M. Characterising Student Engagement Modes through Low-Level Activity Patterns. Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization. (88-97).

    https://doi.org/10.1145/3450613.3456818

  • Rodriguez F, Lee H, Rutherford T, Fischer C, Potma E and Warschauer M. Using Clickstream Data Mining Techniques to Understand and Support First-Generation College Students in an Online Chemistry Course. LAK21: 11th International Learning Analytics and Knowledge Conference. (313-322).

    https://doi.org/10.1145/3448139.3448169

  • Salehian Kia F, Hatala M, Baker R and Teasley S. Measuring Students’ Self-Regulatory Phases in LMS with Behavior and Real-Time Self Report. LAK21: 11th International Learning Analytics and Knowledge Conference. (259-268).

    https://doi.org/10.1145/3448139.3448164

  • Zhang T, Taub M and Chen Z. Measuring the Impact of COVID-19 Induced Campus Closure on Student Self-Regulated Learning in Physics Online Learning Modules. LAK21: 11th International Learning Analytics and Knowledge Conference. (110-120).

    https://doi.org/10.1145/3448139.3448150

  • Tang H. (2021). Person-centered analysis of self-regulated learner profiles in MOOCs: a cultural perspective. Educational Technology Research and Development. 10.1007/s11423-021-09939-w.

    http://link.springer.com/10.1007/s11423-021-09939-w

  • Araka E, Oboko R, Maina E and Gitonga R. (2021). A Conceptual Educational Data Mining Model for Supporting Self-Regulated Learning in Online Learning Environments. Handbook of Research on Equity in Computer Science in P-16 Education. 10.4018/978-1-7998-4739-7.ch016. (278-292).

    http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/978-1-7998-4739-7.ch016

  • ElSayed A, Caeiro-Rodriguez M, Mikic-Fonte F and Llamas-Nistal M. A Novel Method to Measure Self-Regulated Learning Based on Social Media. IEEE Access. 10.1109/ACCESS.2021.3092943. 9. (93516-93528).

    https://ieeexplore.ieee.org/document/9465800/

  • Adnan M, Habib A, Ashraf J, Mussadiq S, Raza A, Abid M, Bashir M and Khan S. Predicting at-Risk Students at Different Percentages of Course Length for Early Intervention Using Machine Learning Models. IEEE Access. 10.1109/ACCESS.2021.3049446. 9. (7519-7539).

    https://ieeexplore.ieee.org/document/9314000/

  • Tahir F, Mitrović A and Sotardi V. (2021). Investigating Effects of Selecting Challenging Goals. Artificial Intelligence in Education. 10.1007/978-3-030-78270-2_62. (349-354).

    https://link.springer.com/10.1007/978-3-030-78270-2_62

  • Stewart A, Solyst J, Buddemeyer A, Hatley L, Henderson-Singer S, Scott K, Walker E and Ogan A. (2021). Explaining Engagement: Learner Behaviors in a Virtual Coding Camp. Artificial Intelligence in Education. 10.1007/978-3-030-78270-2_60. (338-343).

    https://link.springer.com/10.1007/978-3-030-78270-2_60

  • Baker R, Xu D, Park J, Yu R, Li Q, Cung B, Fischer C, Rodriguez F, Warschauer M and Smyth P. (2020). The benefits and caveats of using clickstream data to understand student self-regulatory behaviors: opening the black box of learning processes. International Journal of Educational Technology in Higher Education. 10.1186/s41239-020-00187-1. 17:1. Online publication date: 1-Dec-2020.

    https://educationaltechnologyjournal.springeropen.com/articles/10.1186/s41239-020-00187-1

  • Araka E, Maina E, Gitonga R and Oboko R. (2020). Research trends in measurement and intervention tools for self-regulated learning for e-learning environments—systematic review (2008–2018). Research and Practice in Technology Enhanced Learning. 10.1186/s41039-020-00129-5. 15:1. Online publication date: 1-Dec-2020.

    https://telrp.springeropen.com/articles/10.1186/s41039-020-00129-5

  • Wang Y, Dong C and Zhang X. (2020). Improving MOOC learning performance in China: An analysis of factors from the TAM and TPB. Computer Applications in Engineering Education. 10.1002/cae.22310. 28:6. (1421-1433). Online publication date: 1-Nov-2020.

    https://onlinelibrary.wiley.com/doi/10.1002/cae.22310

  • van Alten D, Phielix C, Janssen J and Kester L. (2020). Effects of self-regulated learning prompts in a flipped history classroom. Computers in Human Behavior. 10.1016/j.chb.2020.106318. 108. (106318). Online publication date: 1-Jul-2020.

    https://linkinghub.elsevier.com/retrieve/pii/S0747563220300728

  • Gadella L, Estevez-Ayres I, Fisteus J and Delgado-Kloos C. (2020). Application of learning analytics to study the accuracy of self-reported working patterns in self-regulated learning questionnaires 2020 IEEE Global Engineering Education Conference (EDUCON). 10.1109/EDUCON45650.2020.9125405. 978-1-7281-0930-5. (1201-1205).

    https://ieeexplore.ieee.org/document/9125405/

  • Zhang Y, Paquette L, Baker R, Ocumpaugh J, Bosch N, Munshi A and Biswas G. The relationship between confusion and metacognitive strategies in Betty's Brain. Proceedings of the Tenth International Conference on Learning Analytics & Knowledge. (276-284).

    https://doi.org/10.1145/3375462.3375518

  • Fadljević L, Maitz K, Kowald D, Pammer-Schindler V and Gasteiger-Klicpera B. Slow is good. Proceedings of the Tenth International Conference on Learning Analytics & Knowledge. (112-117).

    https://doi.org/10.1145/3375462.3375502

  • Viberg O, Khalil M and Baars M. Self-regulated learning and learning analytics in online learning environments. Proceedings of the Tenth International Conference on Learning Analytics & Knowledge. (524-533).

    https://doi.org/10.1145/3375462.3375483

  • Kia F, Teasley S, Hatala M, Karabenick S and Kay M. How patterns of students dashboard use are related to their achievement and self-regulatory engagement. Proceedings of the Tenth International Conference on Learning Analytics & Knowledge. (340-349).

    https://doi.org/10.1145/3375462.3375472

  • Jansen R, van Leeuwen A, Janssen J, Conijn R and Kester L. (2020). Supporting learners' self-regulated learning in Massive Open Online Courses. Computers & Education. 146:C. Online publication date: 1-Mar-2020.

    https://doi.org/10.1016/j.compedu.2019.103771

  • Ahmad Uzir N, Gašević D, Matcha W, Jovanović J and Pardo A. (2019). Analytics of time management strategies in a flipped classroom. Journal of Computer Assisted Learning. 10.1111/jcal.12392. 36:1. (70-88). Online publication date: 1-Feb-2020.

    https://onlinelibrary.wiley.com/doi/10.1111/jcal.12392

  • Yarygina O. Learning analytics of CS0 students programming errors. Proceedings of the 23rd International Conference on Academic Mindtrek. (149-152).

    https://doi.org/10.1145/3377290.3377319

  • Van Laer S and Elen J. (2019). The effect of cues for calibration on learners' self-regulated learning through changes in learners’ learning behaviour and outcomes. Computers & Education. 135:C. (30-48). Online publication date: 1-Jul-2019.

    https://doi.org/10.1016/j.compedu.2019.02.016

  • ARAKA E, MAINA E, GITONGA R and OBOKO R. (2019). A Conceptual Model for Measuring and Supporting Self-Regulated Learning using Educational Data Mining on Learning Management Systems 2019 IST-Africa Week Conference (IST-Africa). 10.23919/ISTAFRICA.2019.8764852. 978-1-905824-63-2. (1-11).

    https://ieeexplore.ieee.org/document/8764852/

  • Motz B, Quick J, Schroeder N, Zook J and Gunkel M. The validity and utility of activity logs as a measure of student engagement. Proceedings of the 9th International Conference on Learning Analytics & Knowledge. (300-309).

    https://doi.org/10.1145/3303772.3303789

  • Matcha W, Gašević D, Uzir N, Jovanović J and Pardo A. Analytics of Learning Strategies. Proceedings of the 9th International Conference on Learning Analytics & Knowledge. (461-470).

    https://doi.org/10.1145/3303772.3303787

  • Wang F. (2019). On Prediction of Online Behaviors and Achievement Using Self-regulated Learning Awareness in Flipped Classrooms. International Journal of Information and Education Technology. 10.18178/ijiet.2019.9.12.1320. 9:12. (874-879).

    http://www.ijiet.org/show-131-1538-1.html

  • Ahmad Uzir N, Gašević D, Matcha W, Jovanović J, Pardo A, Lim L and Gentili S. (2019). Discovering Time Management Strategies in Learning Processes Using Process Mining Techniques. Transforming Learning with Meaningful Technologies. 10.1007/978-3-030-29736-7_41. (555-569).

    http://link.springer.com/10.1007/978-3-030-29736-7_41