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University Management: Practice and Analysis

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Interventions for Fostering Self-Regulated Learning as Tools for University Management in the Digital Environment

https://doi.org/10.15826/umpa.2024.04.037

Abstract

In recent years, universities have widely adopted courses and programs in online formats. Despite their advantages, online education necessitates a high degree of autonomy and self-regulation from students. Research indicates that the level of self-regulated learning predicts students’ success in online learning; however, students often possess insufficient skills in this area, presenting a barrier to the broad and effective implementation of online education in universities. Self-regulated learning skills can be developed through targeted interventions. This review presents an analysis of interventions employed in universities worldwide. There exists a vast diversity of interventions, yet only a relatively small number are supported by empirical data regarding their effectiveness. A necessary systematization and understanding of which characteristics of interventions most effectively foster the development of self-regulated learning skills is lacking. No prior studies have been conducted to construct typologies of interventions. This paper addresses this existing gap and proposes the development of a typology of interventions based on several criteria. Through the analysis of 68 interventions described in 62 articles, the following criteria for typology were identified: levels of student activity in the process of learning skills; the phase of the self-regulated learning cycle targeted by the intervention; the degree of task structuring; the presence and type of feedback on task performance; the stage of the course at which the intervention is implemented; and the duration of the intervention. The typology enables a shift from analyzing the effectiveness of specific interventions to examining their characteristics that contribute to the development of self-regulated learning skills. Further investigation into the impact of intervention characteristics on effectiveness will reduce resource expenditures on development and simplify the implementation process within university educational practices. The typology presented in this article, along with practical recommendations for implementing interventions into university programs, can serve as an effective management mechanism for maintaining high educational quality amidst the extensive growth of online learning. This work is of interest to researchers, educators, and university administration. The data provided can be utilized for designing effective interventions and for transforming university management systems to enhance students’ levels of autonomy and self-regulation.

About the Author

M. S. Khamidulina
National Research University Higher School of Economics
Russian Federation

Marianna S. Khamidulina – Specialist on Educational and Methodological Work at the First Moscow State Medical University of the Ministry of Health of the Russian Federation named after I. M. Sechenov (Sechenov University), Chief Executive Officer of LLC “Esculapia”, Graduate Student and Research Assistant at the Institute of Education of the National Research University Higher School of Economics.

16/10 Potapovsky lane, Moscow, 101000



References

1. Chirikov I. et al. Online education platforms scale college STEM instruction with equivalent learning outcomes at lower cost. Science Advances, 2020, vol. 6, nr 15. doi: 10.1126/ sciadv.aay5324 (In Eng.).

2. Castro M. D. B., Tumibay G. M. A literature review: eff icacy of online lear ning courses for higher education institution using meta-analysis. Education and Information Technologies, 2021, vol. 26, pp. 1367–1385. doi: 10.1007/s10639–019–10027-z (In Eng.).

3. Pasport priopitetnogo proekta “Sovremennaya cifrovaya obrazovatelnaya sreda v Rossiyskoy Federacii” (protokol ot 25.10.2016, No 9). [Passpor t of the Pr ior it y Project “Moder n digital educational environment in the Russian Federation” (protocol from 25.10.2016, nr 9)], available at: http://static.government.ru/media/files/8SiLmMBgjAN89vZbUUtmuF5lZYfTvOAG.pdf (accessed 24.09.2024). (In Russ.).

4. Hong J. C., Lee Y. F., Ye J. H. Procrastination predicts online self-regulated learning and online learning ineffectiveness during the coronavirus lockdown. Personality and Individual Differences, 2021, vol. 174. doi: 10.1016/j.paid.2021.110673 (In Eng.).

5. Inan F. et al. The impact of self-regulation strategies on student success and satisfaction in an online course. EdMedia+ Innovate Learning Online 2022. Association for the Advancement of Computing in Education (AACE), 2017, vol. 16, nr 1, pp. 23–32. (In Eng.).

6. Artino A., Ioannou A. Promoting academic motivation and self-regulation: Practical guidelines for online instructors. Society for Information Technology & Teacher Education International Conference. Association for the Advancement of Computing in Education (AACE), 2008, pp. 208–212. (In Eng.).

7. Mikroyannidis A. et al. Self-regulated lear ning in formal education: perceptions, challenges and opportunities. International Journal of Technology Enhanced Learning, 2014, vol. 6, nr 2, pp. 145–163. doi: 10.1504/ijtel.2014.066860 (In Eng.).

8. Christie H., Barron P., D’Annunzio-Green N. Direct entrants in transition: becoming independent learners. Studies in Higher Education, 2013, vol. 38, nr 4, pp. 623–637. doi: 10.1080/03075079.2011.588326. (In Eng.).

9. Noyens D. et al. The directional links between students’ academic motivation and social integration during the first year of higher education. European Journal of Psychology of Education, 2019, vol. 34, pp. 67–86. doi: 10.1007/s10212-017-0365-6. (In Eng.).

10. Zimmerman B. J. Becoming a self-regulated learner: An overview. Theory into Practice, 2002, vol. 41, nr 2, pp. 64–70. doi: 10.1207/s15430421tip4102_2 (In Eng.).

11. Greene J. A., Azevedo R. A macro-level analysis of SRL processes and their relations to the acquisition of a sophisticated mental model of a complex system. Contemporary Educational Psychology, 2009, vol. 34, nr 1, pp. 18–29. doi: 10.1016/j.cedpsych.2008.05.006. (In Eng.).

12. Cho M. H., Shen D. Self-regulation in online learning. Distance Education, 2013, vol. 34, nr 3, pp. 290–301. doi: 10.1080/01587919.2013.835770. (In Eng.).

13. Sun J. C. Y., Rueda R. Situational interest, computer self-efficacy and self-regulation: Their impact on student engagement in dist ance education. British Journal of Educational Technology, 2012, vol. 43, nr 2, pp. 191–204. doi: 10.1111/j.1467–8535.2010.01157.x (In Eng.).

14. Araka E. et al. 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, 2020, vol. 15, pp. 1–21. doi: 10.1186/s41039-020-00129-5 (In Eng.).

15. Zheng L. The effectiveness of self-regulated learning scaffolds on academic perfor mance in computer-based lear ning environ ments: A met a-analysis. Asia Pacif ic Education Review, 2016, vol. 17, pp. 187–202. doi: 10.1007/s12564-016-9426-9 (In Eng.).

16. Wang Y., Sperling R. A. Characteristics of effective selfregulated learning interventions in mathematics classrooms: A systematic review. Frontiers in Education, 2020, vol. 5, p. 58. doi: 10.3389/feduc.2020.00058 (In Eng.).

17. Edisherashvili N. et al. Suppor ting self-regulated learning in distance learning contexts at higher education level: systematic literature review. Frontiers in Psychology, 2022, vol. 12, p. 6132. doi: 10.3389/fpsyg.2021.792422 (In Eng.).

18. Xu Z. et al. Sy nthesizi ng research evidence on self-reg ulated lear n i ng and academ ic achievement i n online and blended lear ning environ ments: A scoping review. Educational Research Review, 2023. doi: 10.1016/j.edurev.2023.100510 (In Eng.).

19. Xu Z. et al. A meta-analysis of the efficacy of selfregulated learning interventions on academic achievement in online and blended environments in K-12 and higher education. Behaviour & Information Technology, 2023, vol. 42, nr 16, pp. 2911–2931. doi: 10.1080/0144929x.2022.2151935 (In Eng.).

20. Jansen R. S. et al. Self-regulated learning partially mediates the effect of self-regulated learning interventions on achievement in higher education: A meta-analysis. Educational Research Review, 2019, vol. 28. doi: 10.1016/j.edurev.2019.100292 (In Eng.).

21. Theobald M. Self-regulated learning training programs enhance university students’ academic performance, selfregulated learning strategies, and motivation: A meta-analysis. Contemporary Educational Psychology, 2021, vol. 66, 19 p. doi: 10.1016/j.cedpsych.2021.101976 (In Eng.).

22. Chen J. The effectiveness of self-regulated learning (SRL) interventions on L2 learning achievement, strategy employment and self-efficacy: A meta-analytic study. Frontiers in Psychology, 2022, vol. 13, 17 p. doi 10.3389/fpsyg.2022.1021101 (In Eng.).

23. Flavell J. H. Metacognition and cognitive monitoring: A new area of cognitive–developmental inquiry. American Psychologist, 1979, vol. 34, nr 10, pp. 906–911. doi 10.1037//0003–066x.34.10.906 (In Eng.).

24. Zimmerman B. J. Self-regulated learning and academic achievement: An overview. Educational Psychologist, 1990, vol. 25, nr 1, pp. 3–17. doi: 10.1007/978-14612-36184_1 (In Eng.).

25. Vosniadou S. et al. The promotion of self-regulated learning in the classroom: a theoretical framework and an observation study. Metacognition and Learning, 2024, pp. 1–39. doi: 10.1007/s11409-024-09374-1 (In Eng.).

26. Vosniadou S. et al. Beliefs about the self-regulation of learning predict cognitive and metacognitive strategies and academic performance in pre-service teachers. Metacognition and Learning, 2021, 32 p. doi 10.1007/s11409‑020‑09258‑0 (In Eng.)

27. Grunschel C. et al. “I’ll stop procrastinating now!” Fostering specific processes of self-regulated learning to reduce academic procrastination. Journal of Prevention & Intervention in the Community, 2018, vol. 46, nr 2, pp. 143–157. doi: 10.1080/10852352.2016.1198166 (In Eng.).

28. Wong J. et al. Examining the use of prompts to facilitate self-regulated learning in Massive Open Online Courses. Computers in Human Behavior, 2021, vol. 115. doi: 10.1016/j.chb.2020.106596 (In Eng.).

29. Yeomans M., Reich J. Planning prompts increase and forecast course completion in massive open online courses. Proceedings of the Seventh International Learning Analytics & Knowledge Conference, 2017, pp. 464– 473. doi: 10.1145/3027385.3027416 (In Eng.).

30. Lehmann T., Hähnlein I., Ifenthaler D. Cognitive, metacognitive and motivational perspectives on pref lection in self-regulated online learning. Computers in Human Behavior, 2014, vol. 32, pp. 313–323. doi: 10.1016/j.chb.2013.07.051 (In Eng.).

31. Marquès J. M. et al. Using a notification, recommendation and monitoring system to improve interaction in an automated assessment tool: An analysis of students’ perceptions. International Journal of Human-ComputerInteraction, 2022, vol. 38, 4, pp. 351–370. doi 10.1080/10447318.2021.1938400 (In Eng.).

32. Sitzmann T., Ely K. Sometimes you need a reminder: The effects of prompting self-regulation on regulatory processes, learning, and attrition. Journal of Applied Psychology, 2010, vol. 95, nr 1, pp. 132–134. doi: 10.1037/a0018080 (In Eng.).

33. Tabuenca B. et al. Stop and think: Exploring mobile notifications to foster ref lective practice on meta-learning. IEEE Transactions on Learning Technologies, 2014, vol. 8, nr 1, pp. 124–135. doi: 10.1109/tlt.2014.2383611 (In Eng.).

34. Wong J. et al. Facilitating goal setting and planning to enhance online self-regulation of learning. Computers in Human Behavior, 2021, vol. 124, 15 p. doi: 10.1016/j.chb.2021.106913 (In Eng.).

35. Saddawi-Konef ka D. et al. Changing resident physician studying behaviors: A randomized, comparative effectiveness trial of goal setting versus use of WOOP. Journal of Graduate Medical Education, 2017, vol. 9, nr 4, pp. 451–457. doi: 10.4300/jgme-d-16–00703.1 (In Eng.).

36. Raković M. et al. Examining the critical role of evalu at ion a nd a d apt at ion i n self-reg ulated lea r n i ng. Contemporary Educational Psychology, 2022, vol. 68, 14 p. doi: 10.3102/1690112 (In Eng.).

37. Broadbent J., Panadero E., Fuller-Tyszkiewicz M. Effects of mobile-app learning diaries vs online training on specific self-regulated learning components. Educational Technology Research and Development, 2020, vol. 68, pp. 2351–2372. doi: 10.1007/s11423-020-09781-6 (In Eng.).

38. Dignath-van Ewijk C., Fabriz S., Büttner G. Fostering self-regulated lear ning among students by means of an electronic learning diary: A training experiment. Journal of Cognitive Education and Psychology, 2015, vol. 14, nr 1, pp. 77–97. doi: 10.1891/1945–8959.14.1.77 (In Eng.).

39. Panadero E. A review of self-regulated learning: Six models and four directions for research. Frontiers in Psychology, 2017, vol. 8, p. 422. doi: 10.3389/fpsyg.2017.00422 (In Eng.).

40. Pintrich P. R. The role of goal orientation in selfregulated learning. Handbook of self-regulation, Academic, 2000, 52 p. doi: 10.1016/b978–012109890–2/50043–3 (In Eng.).

41. Milligan C., Littlejohn A. How health professionals regulate their learning in massive open online courses. The Internet and Higher Education, 2016, vol. 31, pp. 113–121. doi: 10.1016/j.iheduc.2016.07.005 (In Eng.).

42. Chang C. C. et al. Does using e-portfolios for ref lective writing enhance high school students’ self-regulated learning? Technology, Pedagogy and Education, 2016, vol. 25, nr 3, pp. 317–336. doi: 10.1080/1475939x.2015.1042907 (In Eng.).

43. Masui C., De Corte E. Learning to reflect and to attribute constructively as basic components of self-regulated learning. British Journal of Educational Psychology, 2005, vol. 75, nr 3, pp. 351–372. doi: 10.1348/000709905x25030 (In Eng.).

44. Ganda D. R., Boruchovitch E. Promoting self-regulated learning of Brazilian Preservice student Teachers: results of an intervention Program. Frontiers in Education, 2018, vol. 3, p. 5. doi: 10.3389/feduc.2018.00005 (In Eng.).

45. Nguyen L. T., Ikeda M. The effects of ePortfolio-based learning model on student self-regulated learning. Active Learning in Higher Education, 2015, vol. 16, nr 3, pp. 197–209. doi: 10.1177/1469787415589532 (In Eng.).

46. Dever D. A. et al. Pedagogical Agent Support and Its Relationship to Learners’ Self-regulated Learning Strategy Use with an Intelligent Tutoring System. In: International Conference on Artificial Intelligence in Education, Springer International Publishing, 2022, pp. 332–343. doi: 10.1007/978-3-031-11644-5_27 (In Eng.).

47. Cazan A. M. Enhancing self-regulated lear ning by learning journals. Procedia-Social and Behavioral Sciences, 2012, vol. 33, pp. 413–417. doi: 10.1016/j.sbspro.2012.01.154 (In Eng.).

48. Nückles M. et al. The self-regulation-view in writingto-learn: Using journal writing to optimize cognitive load in self-regulated learning. Educational Psychology Review, 2020, vol. 32, pp. 1089–1126. doi: 10.1007/s10648-020-09541-1 (In Eng.).

49. Weber F. et al. The GoalTrees Hierarchical Goal-Setting Intervention for Higher Education: Three Formative Studies. In: Open and Inclusive Educational Practice in the Digital World, Springer International Publishing, 2022, pp. 47– 63. doi: 10.1007/978-3-031-18512-0_4 (In Eng.).

50. Ibarra-Sáiz M. S., Rod r íg uezGómez G., Boud D. Developing student competence through peer assessment: the role of feedback, self-regulation and evaluative judgement. Higher Education, 2020, vol. 80, nr 1, pp. 137–156. doi: 10.1007/s10734-019-00469-2 (In Eng.).

51. Kulkarni C. et al. Designing scalable and sustainable peer interactions online. In: Design Thinking Research: Taking Breakthrough Innovation Home, Springer International Publishing, 2016, pp. 237–273. doi: 10.1007/978-3-319-40382-3_14 (In Eng.).

52. Chou C. Y., Zou N. B. An analysis of internal and external feedback in self-regulated learning activities mediated by self-regulated learning tools and open learner models. International Journal of Educational Technolog y in Higher Education, 2020, vol. 17, nr 1, pp. 1–27. doi: 10.1186/s41239–020–00233-y (In Eng.).

53. Järvelä S., Nguyen A., Hadwin A. Human and artificial intelligence collaboration for socially shared regulation in learning. British Journal of Educational Technology, 2023, vol. 54, nr 5, pp. 1057–1076. (In Eng.).

54. Bransen D. et al. Putting self-regulated learning in context: Integrating self-, co-, and socially shared regulation of learning. Medical Education, 2022, vol. 56, nr 1, pp. 29–36. doi: 10.1111/medu.14566 (In Eng.).

55. Zheng X., Luo L., Liu C. Facilitating undergraduates’ online self-regulated learning: The role of teacher feedback. The Asia-Pacific Education Researcher, 2023, vol. 32, nr 6, pp. 805–816. doi: 10.1007/s40299-022-00697-8 (In Eng.).

56. Lee Y. F., Hwang G. J., Chen P. Y. Impacts of an AIbased chat bot on college students’ after-class review, academic performance, self-efficacy, learning attitude, and motivation. Educational Technology Research and Development, 2022, vol. 70, nr 5, pp. 1843–1865. doi 10.1007/s11423‑022‑10142‑8 (In Eng.). 57. Lin M. P. C., Chang D. CHAT-ACTS: A pedagogical framework for personalized chatbot to enhance active learning and self-regulated learning. Computers and Education: Artificial Intelligence, 2023, vol. 5, pp. 100–167. doi: 10.1016/j.caeai.2023.100167 (In Eng.).

57. Schrader C., Grassinger R. Tell me that I can do it better. The effect of attributional feedback from a learning technology on achievement emotions and performance and the moderating role of individual adaptive reactions to errors. Computers & Education, 2021, vol. 161, 30 p. doi: 10.1016/j.compedu.2020.104028 (In Eng.).

58. Wu Y., Schunn C. D. Passive, active, and constr uctive engagement with peer feedback: A revised model of learning from peer feedback. Contemporary Educational Psycholog y, 2023, vol. 73, pp. 102 –160. doi: 10.1016/j.cedpsych.2023.102160 (In Eng.).

59. Davis D. et al. Follow the successful crowd: raising MOOC completion rates through social comparison at scale. In: Proceedings of the Seventh International Learning Analytics & Knowledge Conference, 2017, pp. 454–463. doi: 10.1145/3027385.3027411 (In Eng.).

60. Cobos R. Self-Regulated Learning and Active Feedback of MOOC Learners Supported by the Intervention Strategy of a Learning Analytics System. Electronics, 2023, vol. 12, nr 15, pp. 33–68. doi: 10.3390/electronics12153368 (In Eng.).

61. Matcha W. et al. A systematic review of empirical studies on learning analytics dashboards: A self-regulated learning perspective. IEEE Transactions on Learning Technologies, 2019, vol. 13, nr 2, pp. 226–245. doi: 10.1109/tlt.2019.2916802 (In Eng.).

62. Karaoglan Yilmaz F. G., Yilmaz R. Learning analytics intervention improves students’ engagement in online learning. Technology, Knowledge and Learning, 2022, vol. 27, nr 2, pp. 449–460. doi: 10.1007/s10758–021–09547-w (In Eng.).

63. Baadte C. Effects of short-term video-based interventions and instructions on teachers’ feedback skills to support students’ self-regulated learning. European Journal of Psycholog y of Education, 2019, vol. 34, pp. 559 –578. doi: 10.1007/s10212-018-00409-1 (In Eng.).

64. Schippers M. C., Scheepers A. W. A., Peterson J. B. A scalable goal-setting intervention closes both the gender and ethnic minority achievement gap. Palgrave Communications, 2015, vol. 1, nr 1, pp. 1–12. doi: 10.1057/palcomms.2015.14 (In Eng.).

65. Vilkova K. The promises and pitfalls of self-regulated learning interventions in MOOCs. Technology, Knowledge and Learning, 2022, vol. 27, nr 3, pp. 689–705. doi: 10.1007/s10758-021-09580-9 (In Eng.).

66. Wolters C. A., Hoops L. D. Self-regulated learning interventions for motivationally disengaged college students. In: In T. Cleary. Self-regulated learning interventions with at-risk youth: Enhancing adaptability, performance, and wellbeing, American Psychological Association, 2015, pp. 66–78. doi: 10.1037/14641–004 (In Eng.).

67. Pérez-Álvarez R. A. et al. Characterizing learners’ engagement in MOOCs: An observational case study using the NoteMyProgress tool for supporting self-regulation. IEEE transactions on Learning Technologies, 2020, vol. 13, nr 4, pp. 676–688. doi: 10.1109/tlt.2020.3003220 (In Eng.).

68. Bar nard L. et al. Measuring self-regulation in online and blended learning environments. The Internet and Higher Education, 2009, vol. 12, nr 1, pp. 1–6. doi: 10.1007/s10639–020–10244-x (In Eng.).

69. Vilkova K., Shcheglova I. Deconstructing self-regulated learning in MOOCs: In search of help-seeking mechanisms. Education and Information Technologies, 2021, vol. 26, nr 1, pp. 17–33. doi: 10.1007/s10639–020–10244-x (In Eng.).

70. Han F., Ellis R. A. Self-repor ted and digital-trace measures of computer science students’ self-regulated learning in blended course designs. Education and Information Technologies, 2023, vol. 28, nr 10, pp. 13253–13268. doi: 10.1007s10639-023-11698-5 (In Eng.).

71. Gupta P., Bamel U. A Study on the Relationship between Domain Specific Self-Efficacy and Self-Regulation in E-learning Contexts. Online Learning, 2023, vol. 27, nr 4. doi: 10.24059/olj.v27i4.3658 (In Eng.).


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Khamidulina M.S. Interventions for Fostering Self-Regulated Learning as Tools for University Management in the Digital Environment. University Management: Practice and Analysis. 2024;28(4):95-104. (In Russ.) https://doi.org/10.15826/umpa.2024.04.037

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