Psychological foundations of technology-mediated educational process management: a narrative review
DOI:
https://doi.org/10.55056/etq.1213Keywords:
educational technology, psychological foundations, learning management, cognitive load theory, self-determination theory, self-regulated learning, technology-enhanced learning, educational process management, integrative frameworkAbstract
The rapid proliferation of educational technologies has outpaced systematic understanding of the psychological principles that should inform their design and implementation. This narrative review synthesises research on the psychological foundations of technology-mediated educational process management, employing Scopus AI-assisted literature discovery to comprehensively map the theoretical landscape. The review examines how cognitive load theory, self-determination theory, self-regulated learning frameworks, social cognitive theory, and affective perspectives inform the design of learning management systems, adaptive learning platforms, intelligent tutoring systems, and learning analytics applications. It analyses how educational paradigms - constructivism, connectivism, humanism, and critical pedagogy - shape technology integration approaches. The synthesis reveals significant gaps between psychological research and educational technology practice, including under-theorisation of many technology implementations, methodological limitations in evaluation studies, and insufficient attention to diverse learner populations and cultural contexts. To address these gaps, the paper proposes the psychological integration framework for educational technology management (PI-ETM), a four-layer model that connects psychological foundations, educational paradigm alignment, technological instantiation, and contextual implementation. Seven guiding principles - theoretical explicitness, multidimensional integration, paradigmatic coherence, learner-centred adaptation, teacher empowerment, contextual sensitivity, and ethical grounding - structure framework application. Implications for researchers, designers, practitioners, and policymakers are articulated, alongside a future research agenda that addresses emerging technologies, including generative AI and extended reality. The review presents a unified conceptual architecture for psychologically informed educational technology, guiding both scholarship and practice.
Downloads
References
Afini Normadhi, N.B., Shuib, L., Md Nasir, H.N., Bimba, A., Idris, N. and Balakrishnan, V., 2019. Identification of personal traits in adaptive learning environment: Systematic literature review. Computers and Education, 130, pp.168–190. Available from: https://doi.org/10.1016/j.compedu.2018.11.005. DOI: https://doi.org/10.1016/j.compedu.2018.11.005
Ahn, B. and Harley, J.M., 2020. Facial expressions when learning with a Queer History App: Application of the Control Value Theory of Achievement Emotions. British Journal of Educational Technology, 51(5), pp.1563–1576. Available from: https://doi.org/10.1111/bjet.12989. DOI: https://doi.org/10.1111/bjet.12989
Alsaffar, R.D., 2025. The Role of AI-Driven Adaptive Multimedia Systems on Personalized Learning Paths. Asian Journal of University Education, 21(2), pp.380–402. Available from: https://doi.org/10.24191/ajue.v21i1.26.
Anoir, L., Khaldi, M. and Erradi, M., 2024. Intelligent Tutor Systems and the Personalization of Pedagogical Objects in Adaptive e-Learning. In: C.S. Gosavi, G. Bhutkar, A. Banubakode and E. Eilu, eds. Interactive Media with Next-Gen Technologies and Their Usability Evaluation. New York: Chapman and Hall/CRC. Available from: https://doi.org/10.1201/9781032664828. DOI: https://doi.org/10.1201/9781032664828-14
Azevedo, R., Bouchet, F., Duffy, M., Harley, J., Taub, M., Trevors, G. and Wiedbusch, M., 2022. Lessons Learned and Future Directions of Meta-Tutor: Leveraging Multichannel Data to Scaffold Self-Regulated Learning with an Intelligent Tutoring System. Frontiers in Psychology. Frontiers Media SA, vol. 7, p.813632. Available from: https://doi.org/10.3389/fpsyg.2022.813632. DOI: https://doi.org/10.3389/fpsyg.2022.813632
Bandura, A., 2001. Social Cognitive Theory: An Agentic Perspective. Annual Review of Psychology, 52(1), pp.1–26. Available from: https://doi.org/10.1146/annurev.psych.52.1.1. DOI: https://doi.org/10.1146/annurev.psych.52.1.1
Barton, E.A. and Dexter, S., 2020. Sources of teachers’ self-efficacy for technology integration from formal, informal, and independent professional learning. Educational Technology Research and Development, 68(1), pp.89–108. Available from: https://doi.org/10.1007/s11423-019-09671-6. DOI: https://doi.org/10.1007/s11423-019-09671-6
Bebell, D., Russell, M., O’Dwyer, L.M. and Hoffmann, T., 2010. Concerns, Considerations, and New Ideas for Data Collection and Research in Educational Technology Studies. Journal of Research on Technology in Education, 43(1), pp.29–52. Available from: https://doi.org/10.1080/15391523.2010.10782560. DOI: https://doi.org/10.1080/15391523.2010.10782560
Begolli, K.N., Bermudez, V.N., Lawrence, L., Acevedo-Farag, L.M., Valdez, S.V., Santana, E., Alvarez-Vargas, D., Ahn, J., Bailey, D., Rhodes, K., Richland, L.E. and Bustamante, A.S., 2024. Incorporating Design Based Implementation Research with a Randomized Controlled Trial to develop and evaluate the efficacy of playful rational number learning. Contemporary Educational Psychology, 78, p.102296. Available from: https://doi.org/10.1016/j.cedpsych.2024.102296. DOI: https://doi.org/10.1016/j.cedpsych.2024.102296
Bleck, M., Le, N.T. and Pinkwart, N., 2020. Physiology-Aware Learning Analytics Using Pedagogical Agents. CEUR Workshop Proceedings. vol. 2610, pp.17–22. Available from: https://ceur-ws.org/Vol-2610/paper4.pdf.
Bodily, R. and Verbert, K., 2017. Review of Research on Student-Facing Learning Analytics Dashboards and Educational Recommender Systems. IEEE Transactions on Learning Technologies, 10(4), pp.405–418. Available from: https://doi.org/10.1109/TLT.2017.2740172. DOI: https://doi.org/10.1109/TLT.2017.2740172
Bond, M., Zawacki-Richter, O. and Nichols, M., 2019. Revisiting five decades of educational technology research: A content and authorship analysis of the British Journal of Educational Technology. British Journal of Educational Technology, 50(1), pp.12–63. Available from: https://doi.org/10.1111/bjet.12730. DOI: https://doi.org/10.1111/bjet.12730
Braun, V. and Clarke, V., 2006. Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), pp.77–101. Available from: https://doi.org/10.1191/1478088706qp063oa. DOI: https://doi.org/10.1191/1478088706qp063oa
Bulfin, S., Henderson, M., Johnson, N.F. and Selwyn, N., 2014. Methodological capacity within the field of “educational technology” research: An initial investigation. British Journal of Educational Technology, 45(3), pp.403–414. Available from: https://doi.org/10.1111/bjet.12145. DOI: https://doi.org/10.1111/bjet.12145
Cárdenas-Robledo, L.A. and Peña-Ayala, A., 2019. A holistic self-regulated learning model: A proposal and application in ubiquitous-learning. Expert Systems with Applications, 123, pp.299–314. Available from: https://doi.org/10.1016/j.eswa.2019.01.007. DOI: https://doi.org/10.1016/j.eswa.2019.01.007
Castillo, J.M., Dorman, C., Gaunt, B., Hardcastle, B., Justice, K. and March, A.L., 2016. Design Research as a Mechanism for Consultants to Facilitate and Evaluate Educational Innovations. Journal of Educational and Psychological Consultation, 26(1), pp.25–48. Available from: https://doi.org/10.1080/10474412.2015.1039125. DOI: https://doi.org/10.1080/10474412.2015.1039125
Cheng, L., Antonenko, P.D. and Ritzhaupt, A.D., 2024. The impact of teachers’ pedagogical beliefs, self-efficacy, and technology value beliefs on 3D printing integration in K-12 science classrooms. Educational technology research and development, 72(1), pp.181–208. Available from: https://doi.org/10.1007/s11423-023-10276-3. DOI: https://doi.org/10.1007/s11423-023-10276-3
Chieu, V.M., 2007. An operational approach for building learning environments supporting cognitive flexibility. Educational technology and society, 10(3), pp.32–46. Available from: http://www.jstor.org/stable/jeductechsoci.10.3.32.
Csikszentmihalyi, M., 1997. Finding Flow: The Psychology of Engagement with Everyday Life. New York: Basic Books.
Dahl, J.E. and Mørch, A., 2025. A theoretical and empirical analysis of tensions between learning objects and constructivism. Education and Information Technologies, 30(15), pp.22101–22150. Available from: https://doi.org/10.1007/s10639-025-13636-z. DOI: https://doi.org/10.1007/s10639-025-13636-z
Deci, E.L. and Ryan, R.M., 1985. Intrinsic Motivation and Self-Determination in Human Behavior, Perspectives in Social Psychology. New York: Plenum Press. Available from: https://doi.org/10.1007/978-1-4899-2271-7. DOI: https://doi.org/10.1007/978-1-4899-2271-7_2
Ding, L. and Hong, Z., 2024. On the Relationship Between Pre-service Teachers’ Sense of Self-efficacy and Emotions in the Integration of Technology in Their Teacher Developmental Programs. The Asia-Pacific Education Researcher, 33(4), pp.869–878. Available from: https://doi.org/10.1007/s40299-023-00758-6. DOI: https://doi.org/10.1007/s40299-023-00758-6
D’Mello, S. and Graesser, A., 2013. AutoTutor and Affective AutoTutor: Learning by Talking with Cognitively and Emotionally Intelligent Computers that Talk Back. ACM Transactions on Interactive Intelligent Systems, 2(4), pp.23:1–23:39. Available from: https://doi.org/10.1145/2395123.2395128. DOI: https://doi.org/10.1145/2395123.2395128
Eom, S.B., 2012. Effects of LMS, self-efficacy, and self-regulated learning on LMS effectiveness in business education. Journal of International Education in Business, 5(2), p.129–144. Available from: https://doi.org/10.1108/18363261211281744. DOI: https://doi.org/10.1108/18363261211281744
Feyzi Behnagh, R. and Yasrebi, S., 2020. An examination of constructivist educational technologies: Key affordances and conditions. British Journal of Educational Technology, 51(6), pp.1907–1919. Available from: https://doi.org/10.1111/bjet.13036. DOI: https://doi.org/10.1111/bjet.13036
Frangou, S.M. and Körkkö, M., 2020. Model of Technology Enhanced Affective Learning. In: T.C. Huang, T.T. Wu, J. Barroso, F.E. Sandnes, P. Martins and Y.M. Huang, eds. Innovative Technologies and Learning. Cham: Springer International Publishing, Lecture Notes in Computer Science, vol. 12555, pp.582–590. Available from: https://doi.org/10.1007/978-3-030-63885-6_63. DOI: https://doi.org/10.1007/978-3-030-63885-6_63
Fuente, O. Pérez de la, 2025. Bridging the Digital Divide: On Overview. In: O. Pérez de la Fuente and J. Skrzypczak, eds. Bridging the Digital Divide: Perspectives on Inequality and Discrimination in the Digital Age. Cham: Springer Nature Switzerland, Palgrave Studies in Digital Inequalities, pp.1–21. Available from: https://doi.org/10.1007/978-3-031-83479-0_1. DOI: https://doi.org/10.1007/978-3-031-83479-0_1
Gong, Y., Wang, F., Zhang, Y. and Geng, J., 2025. TGEL-transformer: Fusing educational theories with deep learning for interpretable student performance prediction. PLOS ONE, 20(6), p.e0327481. Available from: https://doi.org/10.1371/journal.pone.0327481. DOI: https://doi.org/10.1371/journal.pone.0327481
Govender, D. and Govender, I., 2009. The Relationship between Information and Communications Technology (ICT) Integration and Teachers’ Self-efficacy Beliefs about ICT. Education as Change, 13(1), pp.153–165. Available from: https://doi.org/10.1080/16823200902943346. DOI: https://doi.org/10.1080/16823200902943346
Grima-Farrell, C.R., Long, J., Bentley-Williams, R. and Laws, C., 2014. A School System and University Approach to Reducing the Research to Practice Gap in Teacher Education: A Collaborative Special Education Immersion Project. Australian Journal of Teacher Education, 39(5). Available from: https://doi.org/10.14221/ajte.2014v39n5.2. DOI: https://doi.org/10.14221/ajte.2014v39n5.2
Han, I., Shin, W.S. and Ko, Y., 2017. The effect of student teaching experience and teacher beliefs on pre-service teachers’ self-efficacy and intention to use technology in teaching. Teachers and Teaching: Theory and Practice, 23(7), pp.829–842. Available from: https://doi.org/10.1080/13540602.2017.1322057. DOI: https://doi.org/10.1080/13540602.2017.1322057
Haque, M.S., Tripathi, A., Lau, S.L. and Porras, J., 2025. Do Metacognitive Strategies Work? Moodle as a BCSS Tool to Enhance Self-Regulated Learning Behavior in Software Engineering Students. In: H. Oinas-Kukkonen and S. Nabwire, eds. Proceedings of the 13th International Workshop on Behavior Change Support Systems (BCSS 2025), co-located with the 20th International Conference on Persuasive Technology 2025, Limassol, Cyprus, May 5, 2025. CEUR-WS.org, CEUR Workshop Proceedings, vol. 3965, pp.73–83. Available from: https://ceur-ws.org/Vol-3965/BCSS25_Paper6.pdf.
Havard, B., East, M.L., Prayaga, L. and Whiteside, A., 2016. Adaptable Learning Theory Framework for Technology-Enhanced Learning. In: Information Resources Management Association, ed. Leadership and Personnel Management: Concepts, Methodologies, Tools, and Applications. Hershey, PA: IGI Global Scientific Publishing, vol. 1, chap. 18, pp.384–406. Available from: https://doi.org/10.4018/978-1-4666-9624-2.ch018. DOI: https://doi.org/10.4018/978-1-4666-9624-2.ch018
Heinrich, E., 2024. Revolutionising educational technology: The imperative for authentic qualitative research. Social Sciences and Humanities Open, 10, p.101073. Available from: https://doi.org/10.1016/j.ssaho.2024.101073. DOI: https://doi.org/10.1016/j.ssaho.2024.101073
Hew, K.F., Lan, M., Tang, Y., Jia, C. and Lo, C.K., 2019. Where is the “theory” within the field of educational technology research? British Journal of Educational Technology, 50(3), pp.956–971. Available from: https://doi.org/10.1111/bjet.12770. DOI: https://doi.org/10.1111/bjet.12770
Hu, W. and Shao, Z., 2025. Design and evaluation of a GenAI-based personalized educational content system tailored to personality traits and emotional responses for adaptive learning. Computers in Human Behavior Reports, 19, p.100735. Available from: https://doi.org/10.1016/j.chbr.2025.100735. DOI: https://doi.org/10.1016/j.chbr.2025.100735
Husin, H.S., Ibrahim, I., Ibrahim, H.M., Mahari, M., Yue, Z. and Phang, S.K., 2024. Students’ Acceptance and Attitude of Using Learning Management System (LMS). 2024 International Visualization, Informatics and Technology Conference, IVIT 2024. pp.177–183. Available from: https://doi.org/10.1109/IVIT62102.2024.10692779. DOI: https://doi.org/10.1109/IVIT62102.2024.10692779
Huu, P.N., Tangworakitthaworn, P. and Gilbert, L., 2021. Towards self-regulated individual learning path generation using outcome taxonomies and constructive alignment. Tale 2021 - ieee international conference on engineering, technology and education, proceedings. Available from: https://doi.org/10.1109/TALE52509.2021.9678575. DOI: https://doi.org/10.1109/TALE52509.2021.9678777
Ifenthaler, D. and Yau, J.Y.K., 2022. Analytics for Supporting Teaching Success in Higher Education: A Systematic Review. IEEE Global Engineering Education Conference, EDUCON. pp.1721–1727. Available from: https://doi.org/10.1109/EDUCON52537.2022.9766734. DOI: https://doi.org/10.1109/EDUCON52537.2022.9766734
Joksimović, S., Dowell, N., Skrypnyk, O., Kovanović, V., Gašević, D., Dawson, S. and Graesser, A.C., 2015. How do you connect? Analysis of social capital accumulation in connectivist MOOCs. Proceedings of the Fifth International Conference on Learning Analytics And Knowledge. New York, NY, USA: Association for Computing Machinery, LAK ’15, p.64–68. Available from: https://doi.org/10. 1145/2723576.2723604. DOI: https://doi.org/10.1145/2723576.2723604
Kao, C.P., Wu, Y.T., Chang, Y.Y., Chien, H.M. and Mou, T.Y., 2020. Understanding Web-Based Professional Development in Education: The Role of Attitudes and Self-efficacy in Predicting Teachers’ Technology-Teaching Integration. The Asia-Pacific Education Researcher, 29(5), pp.405–415. Available from: https://doi.org/10.1007/s40299-019-00493-x. DOI: https://doi.org/10.1007/s40299-019-00493-x
Kew, S.N. and Tasir, Z., 2022. Developing a Learning Analytics Intervention in E-learning to Enhance Students’ Learning Performance: A Case Study. Education and Information Technologies, 27(5), pp.7099–7134. Available from: https://doi.org/10.1007/s10639-022-10904-0. DOI: https://doi.org/10.1007/s10639-022-10904-0
Kirschner, P.A., Sweller, J., Kirschner, F. and Zambrano R., J., 2018. From Cognitive Load Theory to Collaborative Cognitive Load Theory. International Journal of Computer-Supported Collaborative Learning, 13(2), pp.213–233. Available from: https://doi.org/10.1007/s11412-018-9277-y. DOI: https://doi.org/10.1007/s11412-018-9277-y
Koh, J., 2024. Critical Digital Pedagogy: A Collaborative Teaching Approach. In: M.M. Asad, P.P. Churi, F. Sherwani and R.B. Hassan, eds. Innovative Pedagogical Practices for Higher Education 4.0: Solutions and Demands of the Modern Classroom. Boca Raton: CRC Press, pp.320–331. Available from: https://doi.org/10.1201/9781003400691-20. DOI: https://doi.org/10.1201/9781003400691-20
Kop, R., 2011. The Challenges to Connectivist Learning on Open Online Networks: Learning Experiences during a Massive Open Online Course. The International Review of Research in Open and Distributed Learning, 12(3), pp.19–38. Available from: https://doi.org/10.19173/irrodl.v12i3.882. DOI: https://doi.org/10.19173/irrodl.v12i3.882
Kop, R. and Hill, A., 2008. Connectivism: Learning theory of the future or vestige of the past? International Review of Research in Open and Distance Learning, 9(3). Available from: https://doi.org/10.19173/irrodl.v9i3.523. DOI: https://doi.org/10.19173/irrodl.v9i3.523
Kruty, K., Zdanevych, L., Desnova, I., Blashkova, O. and Zameliuk, M., 2024. The Main Trends in the Formation of Psychological Competence in the Process of Teacher Training. Academia, (35-36), pp.50–72. Available from: https://doi.org/10.26220/aca.5002.
Lampropoulos, G., 2025. Combining Artificial Intelligence with Augmented Reality and Virtual Reality in Education: Current Trends and Future Perspectives. Multimodal Technologies and Interaction, 9(2), p.11. Available from: https://doi.org/10.3390/mti9020011. DOI: https://doi.org/10.3390/mti9020011
Linares-Pellicer, J., Izquierdo-Domenech, J., Ferri-Molla, I. and Aliaga-Torro, C., 2025. Breaking the Bottleneck: Generative AI as the Solution for XR Content Creation in Education. In: E. Vendrell Vidal, U.R. Cukierman and M.E. Auer, eds. Advanced Technologies and the University of the Future. Cham: Springer Nature Switzerland, Lecture Notes in Networks and Systems, vol. 1140, pp.9–30. Available from: https://doi.org/10.1007/978-3-031-71530-3_2. DOI: https://doi.org/10.1007/978-3-031-71530-3_2
Llantos, O.E. and Estuar, M.R.J.E., 2018. my.Eskwela: Designing An Enterprise Learning Management System to Increase Social Network and Reduce Cognitive Load. Procedia Computer Science. vol. 138, pp.595–602. Available from: https://doi.org/10.1016/j.procs.2018.10.080. DOI: https://doi.org/10.1016/j.procs.2018.10.080
Marzouk, Z., Rakovic, M., Liaqat, A., Vytasek, J.M., Samber, D., Stewart-Alonso, J., Ram, I., Woloshen, S., Navarro, P. and Nesbit, J.C., 2016. What if learning analytics were based on learning science? Australasian Journal of Educational Technology, 32(6), pp.1–18. Available from: https://doi.org/10.14742/ajet.3058. DOI: https://doi.org/10.14742/ajet.3058
Mayer, R.E., 2021. Evidence-Based Principles for How to Design Effective Instructional Video. Journal of Applied Research in Memory and Cognition, 10(2), pp.229–240. Available from: https://doi.org/10.1016/j.jarmac.2021.03.007. DOI: https://doi.org/10.1016/j.jarmac.2021.03.007
McCarthy, E.M., Liu, Y. and Schauer, K.L., 2020. Strengths-based blended personalized learning: An impact study using virtual comparison group. Journal of Research on Technology in Education, 52(3), pp.353–370. Available from: https://doi.org/10.1080/15391523.2020.1716202. DOI: https://doi.org/10.1080/15391523.2020.1716202
McGrew, K.S., 2022. The cognitive-affective-motivation model of learning (CAMML): Standing on the shoulders of giants. Canadian journal of school psychology, 37(1), pp.3–34. Available from: https://doi.org/10.1177/08295735211053973. DOI: https://doi.org/10.1177/08295735211054270
Molenda, M., 2008. Historical Foundations. In: M.J. Spector, M.D. Merrill, J. van Merrienboer and M.P. Driscoll, eds. Handbook of Research on Educational Communications and Technology. 3rd ed. New York: Routledge, pp.3–20. Available from: https://doi.org/10.4324/9780203880869. DOI: https://doi.org/10.4324/9780203880869
Murillo-Jiménez, H., Centeno-Alarcón, M., Buele, J. and Yumbla, F., 2025. Analyzing barriers to the effective implementation of technological tools in inclusive education: a scoping review. Frontiers in Education, 10, p.1687664. Available from: https://doi.org/10.3389/feduc.2025.1687664. DOI: https://doi.org/10.3389/feduc.2025.1687664
Ndjama, J.D.J.D.N. and Westhuizen, J. van der, 2025. A Systematic Review of the Challenges and Limitations of VR in Education. In: S.M. Hussain and A.N. Hakro, eds. Creating Immersive Learning Experiences Through Virtual Reality (VR). Hershey, PA: IGI Global Scientific Publishing, chap. 1, pp.1–31. Available from: https://doi.org/10.4018/979-8-3693-6407-9.ch001. DOI: https://doi.org/10.4018/979-8-3693-6407-9.ch001
Norwich, B., Koutsouris, G., Fujita, T. and Milton, F., 2016. Exploring knowledge bridging and translation in lesson study using an inter-professional team. International Journal for Lesson and Learning Studies, 5(3), pp.180–195. Available from: https://doi.org/10.1108/IJLLS-02-2016-0006. DOI: https://doi.org/10.1108/IJLLS-02-2016-0006
Orji, F.A. and Vassileva, J., 2021. Modelling and Quantifying Learner Motivation for Adaptive Systems: Current Insight and Future Perspectives. In: R.A. Sottilare and J. Schwarz, eds. Adaptive Instructional Systems. Adaptation Strategies and Methods. Cham: Springer International Publishing, Lecture Notes in Computer Science, vol. 12793, pp.79–92. Available from: https://doi.org/10.1007/978-3-030-77873-6_6. DOI: https://doi.org/10.1007/978-3-030-77873-6_6
Oulamine, A., Chakra, R., Ziky, R., Bahida, H., Gareh, F.E., Oubihi, I. and Massiki, A., 2025. A Systematic Literature Review of Barriers Affecting e-Learning in Higher Education. Educational Process: International Journal, 17, p.e2025396. Available from: https://doi.org/10.22521/edupij.2025.17.396. DOI: https://doi.org/10.22521/edupij.2025.17.396
Öztürk, H.T., 2015. Examining value change in MOOCs in the scope of connectivism and open educational resources movement. International review of research in open and distributed learning, 16(5), pp.1–25. Available from: https://doi.org/10.19173/irrodl.v16i5.2027. DOI: https://doi.org/10.19173/irrodl.v16i5.2027
Panadero, E., 2017. A Review of Self-regulated Learning: Six Models and Four Directions for Research. Frontiers in Psychology, 8, p.422. Available from: https://doi.org/10.3389/fpsyg.2017.00422. DOI: https://doi.org/10.3389/fpsyg.2017.00422
Pekrun, R., 2006. The Control-Value Theory of Achievement Emotions: Assumptions, Corollaries, and Implications for Educational Research and Practice. Educational Psychology Review, 18(4), pp.315–341. Available from: https://doi.org/10.1007/s10648-006-9029-9. DOI: https://doi.org/10.1007/s10648-006-9029-9
Pekrun, R., 2017. Emotion and Achievement During Adolescence. Child Development Perspectives, 11(3), pp.215–221. Available from: https://doi.org/10.1111/cdep.12237. DOI: https://doi.org/10.1111/cdep.12237
Peng, R., Razak, R.A. and Halili, S.H., 2024. Exploring the role of attitudes, self-efficacy, and digital competence in influencing teachers’ integration of ICT: A partial least squares structural equation modeling study. Heliyon, 10(8), p.e29549. Available from: https://doi.org/10.1016/j.heliyon.2024.e29549. DOI: https://doi.org/10.1016/j.heliyon.2024.e34234
Philip, A., 2024. Educating Generation Z: Adapting Humanistic Teaching in Blended Learning Environment. In: M.F.b. Romlie, S.H. Shaikh Ali, Z.B. Hari and M.C. Leow, eds. Proceedings of the International Conference on Advancing and Redesigning Education 2023. Singapore: Springer Nature Singapore, Lecture Notes in Educational Technology, pp.499–510. Available from: https://doi.org/10.1007/978-981-97-4507-4_54. DOI: https://doi.org/10.1007/978-981-97-4507-4_54
Pollini, A. and Giacobone, G.A., 2025. Unsustainability in Sustainability Education: Limits of Technology In Situ. Sustainability, 17(20), p.9178. Available from: https://doi.org/10.3390/su17209178. DOI: https://doi.org/10.3390/su17209178
Quick, J., Motz, B., Israel, J. and Kaetzel, J., 2020. What college students say, and what they do: aligning self-regulated learning theory with behavioral logs. Proceedings of the Tenth International Conference on Learning Analytics & Knowledge. New York, NY, USA: Association for Computing Machinery, LAK ’20, p.534–543. Available from: https://doi.org/10.1145/3375462.3375516. DOI: https://doi.org/10.1145/3375462.3375516
Rajamanickam, L., Riskhan, B., Tshering, U., Setiawan, I. and Ahmed, M.H., 2025. Integrating Emerging Technologies in Higher Education: Impact, Challenges, and Scalable Adoption Strategies. 2025 International Conference on Metaverse and Current Trends in Computing, ICMCTC 2025. Available from: https://doi.org/10.1109/ICMCTC62214.2025.11196713. DOI: https://doi.org/10.1109/ICMCTC62214.2025.11196713
Rugelj, J., Ciglarič, M., Krevl, A., Pančur, M. and Brodnik, A., 2012. Constructivist Learning Environment in a Cloud. In: L. Uden, E.S. Corchado Rodríguez, J.F. De Paz Santana and F. De la Prieta, eds. Workshop on Learning Technology for Education in Cloud (LTEC’12). Berlin, Heidelberg: Springer Berlin Heidelberg, Advances in Intelligent Systems and Computing, vol. 173, pp.193–204. Available from: https://doi.org/10.1007/978-3-642-30859-8_18. DOI: https://doi.org/10.1007/978-3-642-30859-8_18
Ryan, R.M. and Deci, E.L., 2017. Self-Determination Theory: Basic Psychological Needs in Motivation, Development, and Wellness. New York: The Guilford Press. Available from: https://doi.org/10.1521/978.14625/28806. DOI: https://doi.org/10.1521/978.14625/28806
Ryan, R.M. and Deci, E.L., 2020. Intrinsic and extrinsic motivation from a self-determination theory perspective: Definitions, theory, practices, and future directions. Contemporary Educational Psychology, 61, p.101860. Available from: https://doi.org/10.1016/j.cedpsych.2020.101860. DOI: https://doi.org/10.1016/j.cedpsych.2020.101860
Şahin, M. and Yurdugül, H., 2022. Learners’ Needs in Online Learning Environments and Third Generation Learning Management Systems (LMS 3.0). Technology, Knowledge and Learning, 27(1), pp.33–48. Available from: https://doi.org/10.1007/s10758-020-09479-x. DOI: https://doi.org/10.1007/s10758-020-09479-x
Sani, S.M., Aris, T.N.M., Mustapha, N. and Sulaiman, M.N., 2015. A fuzzy logic approach to manage uncertainty and improve the prediction accuracy in student model design. Journal of Theoretical and Applied Information Technology, 82(3), pp.366–377. Available from: https://www.jatit.org/volumes/Vol82No3/5Vol82No3.pdf.
Scherer, R., Tondeur, J., Siddiq, F. and Baran, E., 2018. The importance of attitudes toward technology for pre-service teachers’ technological, pedagogical, and content knowledge: Comparing structural equation modeling approaches. Computers in Human Behavior, 80, pp.67–80. Available from: https://doi.org/10.1016/j.chb.2017.11.003. DOI: https://doi.org/10.1016/j.chb.2017.11.003
Schneider, J., Limbu, B. and Kiesler, N., 2025. Of house of cards and air castles, a deep dive into the fertile fields of educational technologies and technology enhanced learning. Journal of Computing in Higher Education, 37(2), pp.561–613. Available from: https://doi.org/10.1007/s12528-025-09450-8. DOI: https://doi.org/10.1007/s12528-025-09450-8
Scrivner, O., Nguyen, A., Scrivner, J. and De Laat, M., 2025. Transforming Teaching and Learning: Ethical Considerations, Student Engagement, and Innovative Technologies. Proceedings of the Annual Hawaii International Conference on System Sciences. pp.2–3. Available from: https://scholarspace.manoa.hawaii.edu/server/api/core/bitstreams/7ba86119-280f-4ef9-91b5-c4824db192d9/content.
Sembey, R., Hoda, R. and Grundy, J., 2024. Emerging technologies in higher education assessment and feedback practices: A systematic literature review. Journal of Systems and Software, 211, p.111988. Available from: https://doi.org/10.1016/j.jss.2024.111988. DOI: https://doi.org/10.1016/j.jss.2024.111988
Shadiev, R., Wang, X., Wu, T.T. and Huang, Y.M., 2021. Review of Research on Technology-Supported Cross-Cultural Learning. Sustainability, 13(3), p.1402. Available from: https://doi.org/10.3390/su13031402. DOI: https://doi.org/10.3390/su13031402
Siemens, G., 2008. Learning and Knowing in Networks: Changing Roles for Educators and Designers. ITFORUM for Discussion. Available from: https://eclass.uoa.gr/modules/document/file.php/PPP233/%CE%AC%CF%81%CE%B8%CF%81%CE%B1%20%CE%B2%CE%B9%CE%B2%CE%BB%CE%B9%CE%BF%CE%B3%CF%81%CE%B1%CF%86%CE%AF%CE%B1%CF%82/Siemens%202008.pdf.
Skrypnyk, O., Joksimović, S., Kovanović, V., Gašević, D. and Dawson, S., 2015. Roles of course facilitators, learners, and technology in the flow of information of a cMOOC. International Review of Research in Open and Distributed Learning, 16(3), pp.188–217. Available from: https://doi.org/10.19173/irrodl.v16i3.2170. DOI: https://doi.org/10.19173/irrodl.v16i3.2170
Stahl, G., 2006. Group Cognition: Computer Support for Building Collaborative Knowledge. Cambridge, MA: MIT Press. Available from: https://doi.org/10.7551/mitpress/3372.001.0001. DOI: https://doi.org/10.7551/mitpress/3372.001.0001
Sweller, J., 1988. Cognitive Load During Problem Solving: Effects on Learning. Cognitive Science, 12(2), pp.257–285. Available from: https://doi.org/10.1207/s15516709cog1202_4. DOI: https://doi.org/10.1016/0364-0213(88)90023-7
Sweller, J., Merriënboer, J.J.G. van and Paas, F., 2019. Cognitive Architecture and Instructional Design: 20 Years Later. Educational Psychology Review, 31(2), pp.261–292. Available from: https://doi.org/10.1007/s10648-019-09465-5. DOI: https://doi.org/10.1007/s10648-019-09465-5
Tafrova-Grigorova, A., 2016. Historical roots and development of constructivism. Chemistry: Bulgarian Journal of Science Education, 25(1), pp.75–106.
Tamim, R.M., Borokhovski, E., Bernard, R.M., Schmid, R.F., Abrami, P.C. and Pickup, D.I., 2021. A study of meta-analyses reporting quality in the large and expanding literature of educational technology. Australasian Journal of Educational Technology, 37(4), pp.100–115. Available from: https://doi.org/10.14742/ajet.6322. DOI: https://doi.org/10.14742/ajet.6322
Teasley, S.D. and Lonn, S., 2007. Using learning management systems to support students’ collaborative learning in higher education. Proceedings of the 8th Iternational Conference on Computer Supported Collaborative Learning. International Society of the Learning Sciences, CSCL’07, p.718–720. DOI: https://doi.org/10.3115/1599600.1599731
Tshehla, M., 2023. The Role of Learning Management Systems to Enhance Learner Support. In: N. Eteokleous, D. Ktoridou and A. Kafa, eds. Emerging Trends and Historical Perspectives Surrounding Digital Transformation in Education. Hershey, PA: IGI Global Scientific Publishing, chap. 11, pp.254–266. Available from: https://doi.org/10.4018/978-1-6684-4423-8.ch011. DOI: https://doi.org/10.4018/978-1-6684-4423-8.ch011
Vansteenkiste, M., Aelterman, N., De Muynck, G.J., Haerens, L., Patall, E. and Reeve, J., 2018. Fostering Personal Meaning and Self-relevance: A Self-Determination Theory Perspective on Internalization. The journal of experimental education, 86(1), pp.30–49. Available from: https://doi.org/10.1080/00220973.2017.1381067. DOI: https://doi.org/10.1080/00220973.2017.1381067
Wang, Q., Mousavi, A. and Lu, C., 2022. A scoping review of empirical studies on theory-driven learning analytics. Distance Education, 43(1), pp.6–29. Available from: https://doi.org/10.1080/01587919.2021.2020621. DOI: https://doi.org/10.1080/01587919.2021.2020621
Warschauer, M., 2003. Dissecting the “Digital Divide”: A Case Study in Egypt. Information society, 19(4), pp.297–304. Available from: https://doi.org/10.1080/01972240309490. DOI: https://doi.org/10.1080/01972240309490
Wei, Z. and Yuan, M., 2023. Research on the Current Situation and Future Development Trend of Immersive Virtual Reality in the Field of Education. Sustainability, 15(9), p.7531. Available from: https://doi.org/10.3390/su15097531. DOI: https://doi.org/10.3390/su15097531
Widigdo, M.S.A., Ramli, S.A., Asrifan, A., Kadir, A., Sairin, S. and Madjid, A., 2025. Creating Holistic Learning Environments: Integrating AI, Education, and Psychological Principles. In: A. Asrifan, ed. Human-Centered Learning Design in the AI Era. Hershey, PA: IGI Global Scientific Publishing, chap. 2, pp.29–59. Available from: https://doi.org/10.4018/979-8-3373-5786-7.ch002. DOI: https://doi.org/10.4018/979-8-3373-5786-7.ch002
Wong, B.T.m. and Li, K.C., 2020. A review of learning analytics intervention in higher education (2011–2018). Journal of Computers in Education, 7(1), pp.7–28. Available from: https://doi.org/10.1007/s40692-019-00143-7. DOI: https://doi.org/10.1007/s40692-019-00143-7
Yildirim, S., Temur, N., Kocaman, A. and Göktaş, Y., 2004. What makes a good LMS: An analytical approach to assessment of LMSs. Proceedings of the Fifth International Conference on Information Technology Based Higher Education and Training, ITHET 2004. pp.125–130. Available from: https://doi.org/10.1109/ITHET.2004.1358150. DOI: https://doi.org/10.1109/ITHET.2004.1358150
Yokoyama, S., 2019. Academic Self-Efficacy and Academic Performance in Online Learning: A Mini Review. Frontiers in Psychology, 9, p.2794. Available from: https://doi.org/10.3389/fpsyg.2018.02794. DOI: https://doi.org/10.3389/fpsyg.2018.02794
Yuan, X., Ding, Z., Zhang, Z., Zhou, L., Zhou, Y. and Ge, Z., 2025. Personalized learning resources created by generative artificial intelligence: A systematic review. 2025 11th International Conference on Education and Training Technologies (ICETT). pp.84–89. Available from: https://doi.org/10.1109/ICETT66247.2025.11137092. DOI: https://doi.org/10.1109/ICETT66247.2025.11137092
Zimmerman, B.J., 2000. Attaining Self-Regulation: A Social Cognitive Perspective. In: M. Boekaerts, P.R. Pintrich and M. Zeidner, eds. Handbook of Self-Regulation. San Diego: Academic Press, chap. 2, pp.13–39. Available from: https://doi.org/10.1016/B978-012109890-2/50031-7. DOI: https://doi.org/10.1016/B978-012109890-2/50031-7
Downloads
Submitted
Published
Issue
Section
License
Copyright (c) 2025 Olena L. Pinska, Svitlana V. Shokaliuk, Halyna I. Alieka, Iryna S. Zakarliuka

This work is licensed under a Creative Commons Attribution 4.0 International License.
How to Cite
Accepted 2025-10-25
Published 2025-12-20
