Research Article

Examining cognitive independence in the context of digital learning: A moderation analysis of student motivation, self-regulation skills, and cognitive engagement

Gulnur Ussenova 1 , Gulnara Issayeva 2 * , Ryskul Urazaliyeva 3 , Galina Karimova 1 , Marina Mukhanova 1
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1 Korkyt Ata Kyzylorda University, Kyzylorda, KAZAKHSTAN2 Abai Kazakh National Pedagogical University, Almaty, KAZAKHSTAN3 Kyzylorda Open University, Kyzylorda, KAZAKHSTAN* Corresponding Author
Contemporary Educational Technology, 17(4), October 2025, ep618, https://doi.org/10.30935/cedtech/17570
Published: 18 December 2025
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ABSTRACT

This study investigates the extent to which the use of digital educational resources (DER) predicts students’ cognitive independence (CI) in higher education and explores the moderating roles of psychological variables; motivation for digital learning, self-regulation skills (SRS), and cognitive engagement (CE). A total of 276 undergraduate students from Korkyt Ata Kyzylorda University in Kazakhstan participated in the study. Data were collected using a validated survey instrument covering five constructs: DER usage, motivation, self-regulation, CE, and CI. Moderation analyses were conducted using general linear models. The results revealed that DER usage is a significant positive predictor of CI. While motivation, self-regulation, and engagement were each strong direct predictors, only SRS and CE moderate the relationship between DER usage and CI. Specifically, students with lower levels of self-regulation or engagement benefited more from DER use. These findings show the compensatory role of digital tools in improving autonomy among less-prepared learners. The study contributes to the literature by identifying for whom DER is most effective and indicates the need for differentiated digital pedagogical strategies that promote independent learning.
Keywords: cognitive engagement, cognitive independence, digital educational resources, higher education, moderation analysis, motivation, self-regulation

CITATION (APA)

Ussenova, G., Issayeva, G., Urazaliyeva, R., Karimova, G., & Mukhanova, M. (2025). Examining cognitive independence in the context of digital learning: A moderation analysis of student motivation, self-regulation skills, and cognitive engagement. Contemporary Educational Technology, 17(4), ep618. https://doi.org/10.30935/cedtech/17570

REFERENCES

  1. Anderson, W. (2013). Independent learning: Autonomy, control, and meta-cognition. In M. G. Moore, & W. C. Diehl (Eds.), Handbook of distance education (pp. 86-103). Routledge. https://doi.org/10.4324/9780203803738.ch6
  2. Bi, J., Izadpanah, S., Mohammadi, Z., & Rezaei, Y. M. (2024). Investigating the impact of technology-based education on academic motivation, academic perseverance, and academic self-efficacy in English language learning skills. Education and Information Technologies, 29(15), 20523-20545. https://doi.org/10.1007/s10639-024-12712-0
  3. Bloshchynskyi, I., Okhrimenko, I., Dekhtiarenko, I., Rohovenko, M., Vasylchenko, R., & Prontenko, K. (2023). Cadets’ cognitive independence as a leading factor of their successful training. Revista Romaneasca Pentru Educatie Multidimensionala, 15(4), 34-49. https://doi.org/10.18662/rrem/15.4/778
  4. Camilleri, M. A., & Camilleri, A. C. (2017). Digital learning resources and ubiquitous technologies in education. Technology, Knowledge and Learning, 22, 65-82. https://doi.org/10.1007/s10758-016-9287-7
  5. Churchill, D. (2017). Digital resources for learning. Springer. https://doi.org/10.1007/978-981-10-3776-4
  6. Culbreth, D., & Martin, F. (2025). Exploring the role of synchrony in asynchronous, synchronous, and quasi-synchronous online learner engagement. Educational Technology Research and Development, 73, 2081-2111. https://doi.org/10.1007/s11423-025-10504-y
  7. Drozdikova-Zaripova, A. R., & Sabirova, E. G. (2020). Usage of digital educational resources in teaching students with application of “flipped classroom” technology. Contemporary Educational Technology, 12(2), Article ep278. https://doi.org/10.30935/cedtech/8582
  8. Eccles, J. S., & Wigfield, A. (2002). Motivational beliefs, values, and goals. Annual Review of Psychology, 53(1), 109-132. https://doi.org/10.1146/annurev.psych.53.100901.135153
  9. Elmabaredy, A., & Gencel, N. (2024). Exploring the integration of self-regulated learning into digital platforms to improve students’ achievement and performance. Discover Education, 3, Article 262. https://doi.org/10.1007/s44217-024-00233-4
  10. Fareen, J. A. M. (2022). Digital learning in higher education: A road to transformation and reform. European Journal of Interactive Multimedia and Education, 3(1), Article e02206. https://doi.org/10.30935/ejimed/11493
  11. Farrin, A., Wright-Hughes, A., Moreau, L., Schofield, H., Day, F., Murden, G., Green, J., Bowen, A., Kidd, L., Bojke, C., Forster, A., & LEAP (Life After Stroke Platform) Collaborative. (2025). Developing a national platform for delivering efficient trials for people living with stroke: The Life after Stroke Platform (LEAP). NIHR Open Research, 5, Article 25. https://doi.org/10.3310/nihropenres.13611.1
  12. Filippou, D., Buchs, C., Quiamzade, A., & Pulfrey, C. (2022). Understanding motivation for implementing cooperative learning methods: A value-based approach. Social Psychology of Education, 25(1), 169-208. https://doi.org/10.1007/s11218-021-09666-3
  13. García, D., Guerra, Y., & Leyva, J. (2021). Cognitive independence to design Biology experiments with a frame introduced by opening sequence. Revista Mendive, 19(3), 936-953.
  14. Gennari, A. S., & Valentini, M. (2024). The effects of physical activity on cognitive and learning abilities in childhood. The European Educational Researcher, 7(1), 1-30. https://doi.org/10.31757/euer.711
  15. Gm, D., Goudar, R. H., Kulkarni, A. A., Rathod, V. N., & Hukkeri, G. S. (2024). A digital recommendation system for personalized learning to enhance online education: A review. IEEE Access, 12, 34019-34041. https://doi.org/10.1109/ACCESS.2024.3369901
  16. Guay, F. (2022). Applying self-determination theory to education: Regulations types, psychological needs, and autonomy supporting behaviors. Canadian Journal of School Psychology, 37(1), 75-92. https://doi.org/10.1177/08295735211055355
  17. Guvercin, S. (2025). The urgent case for AI literacy in the 21st century. International Educational Review, 3(1), 41-44. https://doi.org/10.58693/ier.313
  18. Hayes, A. F. (2017). Introduction to mediation, moderation, and conditional process analysis: A regression-based approach. Guilford Publications.
  19. Ifenthaler, D., & Yau, J. Y.-K. (2020). Utilising learning analytics to support study success in higher education: A systematic review. Educational Technology Research and Development, 68(4), 1961-1990. https://doi.org/10.1007/s11423-020-09788-z
  20. Jeno, L. M., Vandvik, V., Eliassen, S., & Grytnes, J. A. (2019). Testing the novelty effect of an m-learning tool on internalization and achievement: A self-determination theory approach. Computers & Education, 128, 398-413. https://doi.org/10.1016/j.compedu.2018.10.008
  21. Kim, C., Park, S. W., Yoon, M., & Yun, H. (2015). An integrative literature review on the motivational design of digital learning environments. Journal of Educational Technology & Society, 18(4), 457-476.
  22. Lavrov, E., Logvinenko, V., Siryk, O., & Kyzenko, V. (2021, March). Method for assessing the information content of factors forming the cognitive independence of students. Journal of Physics: Conference Series, 1840, Article 012066. https://doi.org/10.1088/1742-6596/1840/1/012066
  23. Lee, K., Tsai, P. S., Chai, C. S., & Koh, J. H. L. (2014). Students’ perceptions of self-directed learning and collaborative learning with and without technology. Journal of Computer Assisted Learning, 30(5), 425-437. https://doi.org/10.1111/jcal.12055
  24. Li, X., Fan, Y., Li, T., Rakovic, M., Singh, S., van der Graaf, J., Lim, L., Moore, J., Molenaar, I., Bannert, M., & Gasevic, D. (2024a). The FLoRA engine: Using analytics to measure and facilitate learners’ own regulation activities. Journal of Learning Analytics, 12(1), 391-413. https://doi.org/10.18608/jla.2025.8349
  25. Li, Y., Chen, D., & Deng, X. (2024b). The impact of digital educational games on student’s motivation for learning: The mediating effect of learning engagement and the moderating effect of the digital environment. PLoS ONE, 19(1), Article e0294350. https://doi.org/10.1371/journal.pone.0294350
  26. Martin, F., & Bolliger, D. U. (2018). Engagement matters: Student perceptions on the importance of engagement strategies in the online learning environment. Online Learning, 22(1), 205-222. https://doi.org/10.24059/olj.v22i1.1092
  27. Maygeldiyeva, S., Bekzhanova, B., Zhamansarieva, L., Stamkulova, S., & Usenova, G. (2020). The development of cognitive independence of students based on information technology. E3S Web of Conferences, 159, Article 09009. https://doi.org/10.1051/e3sconf/202015909009
  28. Memon, M. A., Ting, H., Cheah, J.-H., Thurasamy, R., Chuah, F., & Cham, T. H. (2019). Moderation analysis: Issues and guidelines. Journal of Applied Structural Equation Modeling, 3(1), i-xi. https://doi.org/10.47263/JASEM.3(1)01
  29. Nagle, C. (2021). Using expectancy value theory to understand motivation, persistence, and achievement in university-level foreign language learning. Foreign Language Annals, 54(4), 1238-1256. https://doi.org/10.1111/flan.12569
  30. Nilimaa, J. (2023). New examination approach for real-world creativity and problem-solving skills in mathematics. Trends in Higher Education, 2(3), 477-495. https://doi.org/10.3390/higheredu2030028
  31. Okoń, W. (2005). Independence in school education and scientific work. The New Educational Review, 6, 15-25. https://doi.org/10.15804/tner.05.6.2.02
  32. Onah, D. F., Pang, E. L., Sinclair, J. E., & Uhomoibhi, J. (2021). An innovative MOOC platform: The implications of self-directed learning abilities to improve motivation in learning and to support self-regulation. The International Journal of Information and Learning Technology, 38(3), 283-298. http://doi.org/10.1108/IJILT-03-2020-0040
  33. Pintrich, P. R. (1995). Understanding self-regulated learning. In P. R. Pintrich (Ed.), Understanding self-regulated learning (pp. 3-12). Jossey-Bass. https://doi.org/10.1002/tl.37219956304
  34. Qiao, S., Chu, S. K. W., Shen, X., & Yeung, S. S. S. (2022). The impact of an online gamified approach embedded with self-regulated learning support on students’ reading performance and intrinsic motivation: A randomized controlled trial. Journal of Computer Assisted Learning, 38(5), 1379-1393. https://doi.org/10.1111/jcal.12684
  35. Rice, M. F., de Jesús, J. T. A., DePascal, N., McFeely, H., Heaviland, L., & Traylor, A. (2024). E-learning in times of radical uncertainties. European Educational Researcher, 7(1). 53-58, https://doi.org/10.31757/euer.713
  36. Rind, A. A., Asad, M. M., Marri, S. A., Sherwani, F., & Rehman, F. U. (2022). How integration of information and communication technologies impact academic achievement? An empirical study on Sindh Education Foundation. Journal of Applied Research in Higher Education, 14(4), 1761-1772. https://doi.org/10.1108/JARHE-05-2021-0177
  37. Ryan, R. M., Rigby, C. S., & Przybylski, A. (2006). The motivational pull of video games: A self-determination theory approach. Motivation and Emotion, 30(4), 344-360. https://doi.org/10.1007/s11031-006-9051-8
  38. Saxena, A. S. (2020). Impact of metacognitive strategies on self-regulated learning and intrinsic motivation. Journal of Psychosocial Research, 15(1), 35-46. https://doi.org/10.32381/JPR.2020.15.01.3
  39. Shcherbina, E. Y., Dorozhkin, E. M., Akimova, O. B., Yadretsov, V. A., & Shcherbin, M. D. (2017). Educational and cognitive independence of students in e-learning. Eurasian Journal of Analytical Chemistry, 12(7), 1221-1228. https://doi.org/10.12973/ejac.2017.00247a
  40. Singh, D., & Rajendran, R. (2024). Cognitive engagement as a predictor of learning gain in Python programming. Smart Learning Environments, 11(1). https://doi.org/10.1186/s40561-024-00330-9
  41. Sukkamart, A., Pimdee, P., Leekitchwatana, P., Kongpiboon, W., & Kantathanawat, T. (2023). Predicting student-teacher self-directed learning using intrinsic and extrinsic factors: A theory of planned behavior adoption. Frontiers in Psychology, 14. https://doi.org/10.3389/fpsyg.2023.1211594
  42. Sun, J. C. Y., & Rueda, R. (2012). Situational interest, computer self-efficacy and self-regulation: Their impact on student engagement in distance education. British Journal of Educational Technology, 43(2), 191-204. https://doi.org/10.1111/j.1467-8535.2010.01157.x
  43. Sung, J. S., & Huang, W.-H. D. (2024). Motivational design for inclusive digital learning: Women college engineering students’ motivation for online STEM learning. Contemporary Educational Technology, 16(1), Article ep489. https://doi.org/10.30935/cedtech/14047
  44. Theobald, M. (2021). Self-regulated learning training programs enhance university students’ academic performance, self-regulated learning strategies, and motivation: A meta-analysis. Contemporary Educational Psychology, 66, Article 101976. https://doi.org/10.1016/j.cedpsych.2021.101976
  45. Volkotrubova, A., Kasymova, A., Hbur, Z., Kichuk, A., Koshova, S., & Khodakivska, S. (2024). An integrative approach to organizing the formation of students’cognitive independence in conditions of intensification of learning activities. Strategies for Policy in Science & Education/Strategii na Obrazovatelnata i Nauchnata Politika, 32(6), 682-706. https://doi.org/10.53656/str2024-6-2-int
  46. Wang, J., Tigelaar, D. E., Zhou, T., & Admiraal, W. (2023). The effects of mobile technology usage on cognitive, affective, and behavioural learning outcomes in primary and secondary education: A systematic review with meta-analysis. Journal of Computer Assisted Learning, 39(2), 301-328. https://doi.org/10.1111/jcal.12759
  47. Wong, A., Lee, W. L., Chan, M. S. L., Tan, Y. E., Huang, J. M. K., & Lee, Y. H. (2025). Digital learning resources and student success: Analyzing engagement and academic performance. Journal of Applied Learning and Teaching, 8(Sp. Iss. 2), 45-54. https://doi.org/10.37074/jalt.2025.8.S2.3
  48. Zajonc, R. B., Pietromonaco, P., & Bargh, J. (2014). Independence and interaction of affect and cognition. In M. S. Clark, & S. T. Fiske (Eds.), Affect and cognition (pp. 211-227). Psychology Press.
  49. Zeidner, M., & Stoeger, H. (2019). Self-regulated learning (SRL): A guide for the perplexed. High Ability Studies, 30(1-2), 9-51. https://doi.org/10.1080/13598139.2019.1589369
  50. Zhang, M., & Hashim, A. M. (2025). Research on gamification and perceptual improvement of online course interaction design in children’s online education. Multidisciplinary Science Journal, 7(10), 2025477-2025477. https://doi.org/10.31893/multiscience.2025477
  51. Zhang, P. (2021). Understanding digital learning behaviors: Moderating roles of goal setting behavior and social pressure in large-scale open online courses. Frontiers in Psychology, 12. https://doi.org/10.3389/fpsyg.2021.783610
  52. 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