Online Collaborative Learning With Deep Learning In Fostering Critical Thinking And Motivation: Meta Synthetic Analysis
Abstract
Online learning has expanded rapidly, yet many digital courses still struggle to cultivate sustained motivation and higher-order thinking because interaction is often limited to content delivery, fragmented discussion, or individual task completion. This article develops an integrative meta-synthesis on how Online Collaborative Learning (OCL), when supported by deep-learning-enabled artificial intelligence (AI), can strengthen critical thinking and learner motivation in higher education. The review synthesizes theoretical and empirical literature on OCL, computer-supported collaborative learning, learning analytics, adaptive feedback, and self-determination theory. Rather than treating AI as a replacement for pedagogy, this article conceptualizes deep learning as an adaptive scaffolding layer that can support peer dialogue, feedback, grouping, early-warning signals, and personalized learning paths. The synthesis identifies four mechanisms through which OCL-AI integration may enhance learning: argumentative knowledge construction, socially shared regulation, adaptive formative feedback, and motivational need support. The article also argues that these benefits are conditional on ethical data governance, transparent algorithms, teacher facilitation, and equitable access to digital infrastructure. The proposed framework contributes to online pedagogy by clarifying the relationship between social collaboration and machine-supported personalization. It offers practical design principles for lecturers, instructional designers, and higher education institutions seeking to develop online learning environments that are cognitively challenging, motivationally supportive, and ethically responsible.
Downloads
References
Al-Fraihat, D., Joy, M., & Sinclair, J. (2020). Evaluating E-learning systems success: An empirical study. Computers in Human Behavior, 102, 67-86. https://doi.org/10.1016/j.chb.2019.08.004
Bakhshinategh, B., Zaiane, O. R., ElAtia, S., & Ipperciel, D. (2021). Educational data mining applications and tasks: A survey of the last 10 years. Education and Information Technologies, 26, 753-794. https://doi.org/10.1007/s10639-020-10359-2
Bond, M., Buntins, K., Bedenlier, S., Zawacki-Richter, O., & Kerres, M. (2020). Mapping research in student engagement and educational technology: A systematic evidence map. Educational Technology Research and Development, 68(2), 555-600. https://doi.org/10.1007/s11423-019-09677-8
Broadbent, J., & Poon, W. L. (2020). Self-regulated learning strategies & academic achievement in online higher education learning environments: A systematic review. The Internet and Higher Education, 27, 100728. https://doi.org/10.1016/j.iheduc.2020.100728
Chen, C.-M., & Li, M.-C. (2022). Personalised context-aware ubiquitous learning system for supporting motivation and critical thinking. Interactive Learning Environments, 30(2), 236-252. https://doi.org/10.1080/10494820.2019.1636071
Chen, C. H., Chiu, C. H., & Lai, H. Y. (2021). Supporting collaborative problem-solving in online environments: The role of feedback and peer interaction. Interactive Learning Environments, 29(6), 923-940. https://doi.org/10.1080/10494820.2019.1619590
Chen, L., Chen, P., & Lin, Z. (2020). Artificial intelligence in education: A review. IEEE Access, 8, 75264-75278. https://doi.org/10.1109/ACCESS.2020.2988510
Chen, X., Zou, D., Cheng, G., & Xie, H. (2020). Detecting latent topics and trends in educational technologies over four decades using structural topic modeling: A retrospective of all volumes of Computers & Education. Computers & Education, 151, 103855. https://doi.org/10.1016/j.compedu.2020.103855
Chen, X., Zou, D., Cheng, G., & Xie, H. (2023). Artificial intelligence–enhanced online collaborative learning: A systematic review. Educational Technology Research and Development, 71(5), 2235-2256. https://doi.org/10.1007/s11423-022-10161-y
Chiu, T. K. F., & Hew, T. K. F. (2023). Artificial intelligence–enabled feedback for online collaborative learning: Effects on motivation and performance. Computers & Education, 194, 104659. https://doi.org/10.1016/j.compedu.2022.104659
Dabbagh, N., & Fake, H. (2022). The role of collaborative learning in fostering 21st-century skills: A systematic review. Educational Research Review, 36, 100456. https://doi.org/10.1016/j.edurev.2022.100456
Deci, E. L., & Ryan, R. M. (2020). Self-determination theory: Basic psychological needs in motivation, development, and wellness. Guilford Press.
Deng, L., Benckendorff, P., & Gannaway, D. (2022). Learner engagement in MOOCs: Scale development and validation. British Journal of Educational Technology, 53(1), 1-18. https://doi.org/10.1111/bjet.13155
García-Peñalvo, F. J., & Corell, A. (2023). Artificial intelligence in higher education: A bibliometric review. Education and Information Technologies, 28(2), 1237-1259. https://doi.org/10.1007/s10639-022-11391-y
García-Sánchez, S., García-Sánchez, E., & Ramos, J. L. (2023). Intelligent tutoring systems with deep learning in higher education: Enhancing personalization and engagement. Education and Information Technologies, 28(5), 5563-5581. https://doi.org/10.1007/s10639-023-11529-4
Harasim, & L. (2017). Learning theory and online technologies. Routledge.
Holmes, W., Bialik, M., & Fadel, C. (2021). Artificial Intelligence in Education: Promises and Implications for Teaching and Learning. Center for Curriculum Redesign.
Holmes, W., Porayska-Pomsta, K., Holstein, K., Sutherland, E., & Baker, T. (2021). Ethics of AI in education: Towards a community-wide framework. British Journal of Educational Technology, 52(4), 1623-1637. https://doi.org/10.1111/bjet.13102
Hsu, Y. C., Ching, Y. H., & Grabowski, B. L. (2023). Online collaborative learning: Current trends and future directions. The Internet and Higher Education, 57, 100870. https://doi.org/10.1016/j.iheduc.2023.100870
Ifenthaler, D., & Yau, J. Y. K. (2020). Utilising learning analytics for study success: Reflections on current empirical findings. Research and Practice in Technology Enhanced Learning, 15(1), 1-13. https://doi.org/10.1186/s41039-020-00138-1
Kent, C., Laslo, E., & Rafaeli, S. (2021). Interactivity in online discussions and learning outcomes. Computers & Education, 161, 104056. https://doi.org/10.1016/j.compedu.2020.104056
Kim, J., & Xu, B. (2024). AI-driven personalized learning and its impact on student motivation in higher education. Journal of Educational Computing Research, 62(3), 447-469. https://doi.org/10.1177/07356331231156782
Kizilcec, R. F., & Halawa, S. (2021). Improving student engagement in MOOCs with AI-driven interventions. Journal of Learning Analytics, 8(1), 1-19. https://doi.org/10.18608/jla.2021.6811
Kumi-Yeboah, A., Dogbey, J., & Yuan, G. (2020). Online collaborative learning activities: The perceptions of culturally diverse graduate students. Journal of Educators Online, 17(2), 1-21. https://doi.org/10.9743/jeo.2020.17.2.4
Lee, J., & Martin, L. (2020). Investigating students’ perceptions of motivation, engagement, and learning in a flipped learning environment. International Journal of Educational Technology in Higher Education, 17(1), 1-13. https://doi.org/10.1186/s41239-020-00234-x
Lee, J., & Martin, L. (2023). Enhancing student motivation and engagement in online learning environments: The role of collaborative tools and adaptive technologies. Computers and Education: Artificial Intelligence, 4, 100124. https://doi.org/10.1016/j.caeai.2023.100124
Li, Q., & Wang, F. (2024). Deep learning for personalized recommendation in education: A review and future perspectives. Computers in Education, 195, 104671. https://doi.org/10.1016/j.compedu.2024.104671
Noroozi, O., & de Jong, F. (2020). Incorporating argumentation into online learning environments: Conceptual and empirical foundations. International Journal of Computer-Supported Collaborative Learning, 15(2), 129-135. https://doi.org/10.1007/s11412-020-09323-5
Oliveira, A. W., & Brown, A. O. (2024). The role of artificial intelligence in sustaining learner motivation in collaborative online education. British Journal of Educational Technology, 55(3), 812-828. https://doi.org/10.1111/bjet.13352
Ryan, R. M., & Deci, E. L. (2020). Intrinsic and extrinsic motivation from a self-determination theory perspective: Definitions, theory, practices, and future directions. Contemporary Educational Psychology, 61, 101860. https://doi.org/10.1016/j.cedpsych.2020.101860
Smyrnova-Trybulska, E., Morze, N., Kommers, P., & Sekret, I. (2022). Teachers’ digital competence and professional development in the context of AI-based education. International Journal of Continuing Engineering Education and Life-Long Learning, 32(1), 23-41. https://doi.org/10.1504/IJCEELL.2022.120547
Sun, A., & Chen, X. (2021). Online education and its effective practice: A research review. Journal of Information Technology Education: Research, 20, 157-190. https://doi.org/10.28945/4781
Sun, Y., & Chen, X. (2022). Personalized learning paths based on deep learning in online education. Interactive Learning Environments, 30(6), 1012-1026. https://doi.org/10.1080/10494820.2019.1709218
Van Dijk, J. (2020). The digital divide. Polity Press.
Viberg, O., Hatakka, M., Bälter, O., & Mavroudi, A. (2020). The current landscape of learning analytics in higher education. Computers in Human Behavior, 89, 98-110. https://doi.org/10.1016/j.chb.2018.07.027
Wang, Y., Lu, J., & Liu, Z. (2022). Enhancing critical thinking through online collaborative learning: Evidence from higher education. Educational Technology Research and Development, 70(3), 1401-1420. https://doi.org/10.1007/s11423-022-10059-7
Wu, Z., Luo, Y., & He, L. (2021). Enhancing students’ critical thinking skills through collaborative online discussions. Interactive Learning Environments, 29(7), 1050-1065. https://doi.org/10.1080/10494820.2019.1709215
Xu, B., Chen, N. S., & Chen, G. (2024). Adaptive learning with AI: A meta-analysis of personalization in higher education. Computers & Education, 210, 104815. https://doi.org/10.1016/j.compedu.2023.104815
Yang, Y., & Li, X. (2022). Deep learning for adaptive learning systems: A review and future directions. Computers and Education: Artificial Intelligence, 3, 100065. https://doi.org/10.1016/j.caeai.2022.100065
Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2020a). Systematic review of research on artificial intelligence applications in higher education. International Journal of Educational Technology in Higher Education, 17(1), 1-27. https://doi.org/10.1186/s41239-020-00218-x
Zhang, J., Scardamalia, M., Lamon, M., Messina, R., & Reeve, R. (2020). Socio-cognitive dynamics in knowledge building: A longitudinal study. Instructional Science, 48(2), 143-167. https://doi.org/10.1007/s11251-019-09502-7
Zheng, L., Li, X., & Chen, F. (2022). Effects of online collaborative learning on critical thinking disposition: A meta-analysis. British Journal of Educational Technology, 53(2), 285-303. https://doi.org/10.1111/bjet.13164
Zhou, M., & Yu, A. (2021). Academic motivation, engagement, and achievement: Investigating reciprocal relations in two cultural contexts. Learning and Instruction, 71, 101395. https://doi.org/10.1016/j.learninstruc.2020.101395
Copyright (c) 2026 Novarita Novarita, Abdurrahman Abdurrahman, Cucu Sutarsyah, Budi Kadaryanto, Bambang Setiyadi
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with ISEJ: Indonesian Science Education Journal agree to the following terms:
- Authors retain copyright and grant the ISEJ: Indonesian Science Education Journal right of first publication with the work simultaneously licensed under Creative Commons Attribution License (CC BY 4.0) that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors can enter into separate, additional contractual arrangements for the non-exclusive distribution of the published version of the work (e.g., post it to an institutional repository or edit it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) before and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
_(3).png)
