The Influence of Adaptive Multimedia Learning on Student Motivation and Retention in Online Higher Education
Keywords:
Adaptive Multimedia, ARCS Model, Knowledge Retention, Learning Motivation, Online Higher EducationAbstract
Online higher education continues to face challenges related to low student motivation, knowledge retention, and active participation. This study aimed to analyze the effect of adaptive multimedia learning on student motivation and retention in online higher education using a mixed-methods approach with a sequential explanatory design. The sample consisted of 324 students from four open universities selected through stratified random sampling. Research instruments included an ARCS-based learning motivation questionnaire, a knowledge retention test, and in-depth interview guidelines. Quantitative data were analyzed using ANCOVA, multiple regression, and path analysis, while qualitative data were analyzed thematically. The findings revealed that students in the experimental group using adaptive multimedia achieved significantly higher motivation scores than the control group (4.37 vs. 3.24; F = 47.83; p < 0.001; Cohen’s d = 0.89). Knowledge retention was also higher in the experimental group (78.6%) compared to the control group (54.2%) with F = 52.16, p < 0.001, and Cohen’s d = 0.94. Active participation reached 87.3% in the experimental group, while the control group achieved only 62.1%. Path analysis showed that content personalization, adaptive feedback, and difficulty adjustment explained 61.4% of the variance in learning motivation, while motivation explained 45.9% of the variance in knowledge retention. These findings confirm that adaptive multimedia improves the effectiveness of online learning through more personalized, relevant, and responsive learning experiences and reinforces the integration of the Cognitive Theory of Multimedia Learning and the ARCS model in adaptive online learning.
References
Al-shaer, N. M., Hattab, M. K., Khlaif, Z. N., Alsher, T. N., Adas, N. O., Al-kilani, J. A., Fatayer, O. S., Khaled, G. S., & Odeh, M. S. (2025). Digital transformation in higher education for achieving sustainable development goals in conflict zones : a case study of An-Najah National University. Frontiers in Human Dynamics, 30(09), 1–14. https://doi.org/10.3389/fhumd.2025.1585538
Alam, A., & Mohanty, A. (2023). Educational technology : Exploring the convergence of technology and pedagogy through mobility , interactivity , AI , and learning tools Educational technology : Exploring the convergence of technology and pedagogy. Cogent Engineering, 10(2), 2–21. https://doi.org/10.1080/23311916.2023.2283282
Alshaikh, R., Al-malki, N., & Almasre, M. (2024). The implementation of the cognitive theory of multimedia learning in the design and evaluation of an AI educational video assistant utilizing large language models. Heliyon, 10(3), 2–10. https://doi.org/10.1016/j.heliyon.2024.e25361
Anthony D. Harris, M., Mph, J. C., Mcgregor, Phd, E. N., Perencevich, Md, M., Jon P. Furuno, P., Jingkun Zhu, M., E. Peterson, Md, Mph, Joseph Finkelstein, M. (2025). The Use and Interpretation of Quasi-Experimental Studies in Medical Informatics. Journal of the American Medical Informatics Association, 13(1), 16–23. https://doi.org/10.1197/jamia.M1749.Background
Bachtiar, & Sylvia. (2026). Personalized Learning in Distance Education : The Impact of AI-Powered Tools on Engagement and Self- Regulation. Internatipnal Council for Open and Distance Education, 18(1), 146–165. https://doi.org/10.55982/openpraxis.18.1.925
Bakirova, Z., Tasova, A., Absatova, M., Sadirbekova, D., Auezov, B., & Meirbekova, G. (2026). Enhancing digital competence and learning motivation of master ’ s students : a new educational model for Kazakhstan ’ s higher education system. Frontiers in Education, 12(02), 1–19. https://doi.org/10.3389/feduc.2026.1675872
Bali, C., Tasdelen, B., Bandi, S., & Zsidó, A. (2026). Understanding the cognitive cost of multimedia learning : effects of visual load and language proficiency. Cognitive Research: Principles and Implications, 2(11), 1–16. https://doi.org/10.1186/s41235-025-00699-2
Bauer, E., Richters, C., Pickal, A. J., Klippert, M., Sailer, M., & Stadler, M. (2025). Effects of AI- ¬ generated adaptive feedback on statistical skills and interest in statistics : A field experiment in higher education. British Journal of Educational Technology, 56(07), 1735–1757. https://doi.org/10.1111/bjet.13609
Bygstad, B., Egil, Ø., Ludvigsen, S., & Dæhlen, M. (2022). From dual digitalization to digital learning space : Exploring the digital transformation of higher education. Computers & Education, 182(02), 1–49. https://doi.org/10.1016/j.compedu.2022.104463
Candido, V., & Cattaneo, A. (2025). Applying cognitive theory of multimedia learning principles to augmented reality and its effects on cognitive load and learning outcomes. Computers in Human Behavior Reports, 18(05), 100678. https://doi.org/10.1016/j.chbr.2025.100678
Casteleijn, D., & Franzsen, D. (2024). Personalized adaptive learning in higher education : A scoping review of key characteristics and impact on academic performance and engagement. Heliyon, 10(21), e39630. https://doi.org/10.1016/j.heliyon.2024.e39630
Çeken, B., & Taşkın, N. (2022). Multimedia learning principles in different learning environments: a systematic review. Smart Learning Environments, 9(1), 2–22. https://doi.org/10.1186/s40561-022-00200-2
Chang, Y. S. (2022). Applying the arcs motivation theory for the assessment of ar digital media design learning effectiveness. Sustainability (Switzerland), 13(21), 2–24. https://doi.org/10.3390/su132112296
Contrino, M. F., Millán, M. R., Villegas, P. V., & Hernández, J. M. (2024). Using an adaptive learning tool to improve student performance and satisfaction in online and face to face education for a more personalized approach. Contrino et Al. Smart Learning Environments, 6(11), 2–24. https://doi.org/10.1186/s40561-024-00292-y
Dash, G., & Paul, J. (2021). CB-SEM vs PLS-SEM methods for research in social sciences and technology forecasting. Technological Forecasting & Social Change, 173(07), 121092. https://doi.org/10.1016/j.techfore.2021.121092
Djatmiko, G. H., Sinaga, O., & Pawirosumarto, S. (2025). Digital Transformation and Social Inclusion in Public Services : A Qualitative Analysis of E-Government Adoption for Marginalized Communities in Sustainable Governance. Susrtainabillity, 10(2908), 1–28. https://doi.org/10.3390/su17072908
El-Sabagh, H. A. (2021). Adaptive e-learning environment based on learning styles and its impact on development students’ engagement. International Journal of Educational Technology in Higher Education, 18(1), 2–24. https://doi.org/10.1186/s41239-021-00289-4
Firdaus, R., Hernadi, & Pratiwi, W. O. (2024). The Impact of Authoring Tools-Based Interactive Media Implementation on Student Learning Motivation. 2024 Ninth International Conference on Informatics and Computing (ICIC), 1–7. https://doi.org/10.1109/ICIC64337.2024.10957322.
Fitrianto, I. (2024). Innovation and Technology in Arabic Language Learning in Indonesia : Trends and Implications. International Journal of Post Axial: Futuristic Teaching and Learning, 2(3), 134–150.
Gkintoni, E., Antonopoulou, H., & Sortwell, A. (2025). Challenging Cognitive Load Theory : The Role of Educational Neuroscience and Artificial Intelligence in Redefining. Brain Sciences, 15(203), 22–43. https://doi.org/10.3390/brainsci15020203
Gligorea, I., Cioca, M., Tudorache, P., Oancea, R., Gorski, A.-T., & Gorski, H. (2023). Adaptive Learning Using Artificial Intelligence in e-Learning: A Literature Review. Education Science, 13(3), 3–27. https://doi.org/10.3390/educsci13121216
Hagos, T., & Andargie, D. (2023). Effects of Technology-integrated Formative Assessment on Students’ Retention of Conceptual and Procedural Knowledge in Chemical Equilibrium Concepts. Science Education International, 33(4), 383–391. https://doi.org/10.33828/sei.v33.i4.5
Hamdani, S. A., Prima, E. C., Agustin, R. R., Feranie, S., & Sugiana, A. (2022). Development of Android-based Interactive Multimedia to Enhance Critical Thinking Skills in Learning Matters. Journal of Science Learning, 5(1), 103–114. https://doi.org/10.17509/jsl.v5i1.33998
Hasanah, N., Inganah, S., Prasetyo, B., & Mariyanto, A. (2023). Learning in the 21st Century Education Era : Problems of Mathematics Teachers in the Use of Information and Communication Technology-Based Media. JEMS (Journal of Mathematics and Science Education), 11(1), 275–285. https://doi.org/DOIs: 10.25273/jems.v11i1.14734
Herianto, & Wilujeng, I. (2021). Increasing the attention, relevance, confidence and satisfaction (Arcs) of students through interactive science learning multimedia. Research in Learning Technology, 29(1063519), 1–13. https://doi.org/10.25304/rlt.v29.2383
Holmes, H., & Burgess, G. (2022). Digital exclusion and poverty in the UK : How structural inequality shapes experiences of getting online. Digital Geography and Society, 3(06), 100041. https://doi.org/10.1016/j.diggeo.2022.100041
Hussain, T., Khan, A. A., Pillai, J. R., & Siyabi, M. Al. (2023). Adaptive Learning in Higher Education: Implementation, Challenges, and Opportunities. Studies on Education, Science and Technology, 3(5), 1–28.
Javier, V., Isabel, A., Herrero-rold, S., Rodriguez-besteiro, S., Mart, I., Mart, A., & Tornero-aguilera, J. F. (2024). Digital Device Usage and Childhood Cognitive Development : Exploring Effects on Cognitive Abilities. Children, 11(1299), 1–27. https://doi.org/10.3390/children11111299
Kanchon, K. H., Sadman, M., Nabila, K. F., & Tarannum, R. (2024). Enhancing personalized learning : AI-driven identification of learning styles and content modification strategies. International Journal of Cognitive Computing in Engineering, 5(7), 269–278. https://doi.org/10.1016/j.ijcce.2024.06.002
Khairi, M. I., Susanti, D., & Sukono. (2021). Study on Structural Equation Modeling for Analyzing Data. International Journal of Ethno-Sciences and Education Research, 1(3), 52–60. https://doi.org/10.46336/ijeer.v1i3.295
Langenfeld, T., Burstein, J., & von Davier, A. A. (2022). Digital-First Learning and Assessment Systems for the 21st Century. Frontiers in Education, 7(05), 1–16. https://doi.org/10.3389/feduc.2022.857604
Lin, Y., Luo, Z., Ye, Z., Zhong, N., Zhao, L., Zhang, L., & Li, X. (2025). Applications , Challenges , and Prospects of Generative Artificial Intelligence Empowering Medical Education : Scoping Review. JMIR MEDICAL EDUCATION, 11(e71125), 2–29. https://doi.org/10.2196/71125
Major, L., Francis, G. A., & Tsapali, M. (2021). The effectiveness of technology- ¬ supported personalised learning in low- ¬ and income countries : A meta- ¬ analysis. British Journal of Educational Technology, 52(5), 1935–1964. https://doi.org/DOI: 10.1111/bjet.13116
Mayer, R. E. (2022). Multimedia l e a r n i n g. In Multimedia Learning (Vol. 41, pp. 1–55).
Mcanally, K., & Hagger, M. S. (2024). Self-Determination Theory and Workplace Outcomes : A Conceptual Review and Future Research Directions. Behavioral Sciences, 14(428), 2–20. https://doi.org/10.3390/bs14060428
Mejeh, M., & Rehm, M. (2024). Taking adaptive learning in educational settings to the next level: leveraging natural language processing for improved personalization. Educational Technology Research and Development, 72(3), 1597–1621. https://doi.org/10.1007/s11423-024-10345-1
Meng, N., Deli, M. M., Ajirah, U., & Rauf, A. (2025). Educational Technology and AI : Bridging Cognitive Load and Learner Engagement for Effective Learning. SAGE Open, 1(21), 1–21. https://doi.org/10.1177/21582440251395930
Mhlongo, S., Mbatha, K., Ramatsetse, B., & Dlamini, R. (2023). Challenges , opportunities , and prospects of adopting and using smart digital technologies in learning environments : An iterative. Heliyon, 9(6), e16348. https://doi.org/10.1016/j.heliyon.2023.e16348
Mirea, C., Bologa, R., Toma, A., Clim, A., Bobocea, A., & Pl, D. (2025). Transforming Learning with Generative AI : From Student Perceptions to the Design of an Educational Solution. Appled Sciences, 15(5785), 1–32. https://doi.org/10.3390/app1510578
Mudzakkir, & Darmawan, D. (2024). The Influence Of Teacher Teaching Styles and Leaning Motivation on The Learning Achievement. Edu-Riligia: Jurnal Kajian Pendidikan Islam Dan Keagamaan, 8(1), 79–91. https://doi.org/http://jurnal.uinsu.ac.id/index.php/eduriligia
Naseer, F., & Khawaja, S. (2025). Mitigating Conceptual Learning Gaps in Mixed-Ability Classrooms : A Learning Analytics-Based Evaluation of AI-Driven Adaptive Feedback for Struggling Learners. Applied Sciences, 15(4473), 1–29. https://doi.org/10.3390/ app15084473
Negi, S. K. (2024). Exploring the impact of virtual reality and augmented reality technologies in sustainability education on green energy and sustainability behavioral change: A qualitative analysis. Procedia Computer Science, 236(23), 550–557. https://doi.org/10.1016/j.procs.2024.05.065
Noeryanti, A. T., & Rejekiningsih, T. (2023). Learning Innovation through the Development of Interactive Multimedia Based on Local Wisdom for Sociology Learning in the Digital Era. Jurnal Edutech Undiksh, 11(1), 41–53. https://doi.org/https://doi.org/10.23887/jeu.v11i1.60441 Learning
Pelánek, R. (2024). Adaptive Learning is Hard: Challenges, Nuances, and Trade-offs in Modeling. International Journal of Artificial Intelligence in Education, 35(304), 304–329. https://doi.org/10.1007/s40593-024-00400-6
Rahim, F. R., Nabila, P., Sari, S. Y., Suherman, D. S., & Riyasni, S. (2022). Interactive Learning Media for Critical and Creative Thinking Skills Development. Pillar of Physics Education, 15(4), 235. https://doi.org/10.24036/14085171074
Ratinho, E. (2023). The role of gamified learning strategies in student ’ s motivation in high school and higher education : A systematic review. Heliyon, 9(8), 2–16. https://doi.org/10.1016/j.heliyon.2023.e19033
Sánchez-garcía, A. B., Zárate-santana, Z., & Patino-alonso, C. (2025). A Multivariate Analysis with MANOVA-Biplot of Learning Approaches in Health Science Students. Social Sciences, 14(403), 1–13. https://doi.org/10.3390/ socsci14070403
Sari, H. E., Tumanggor, B., & Efron, D. (2024). Improving Educational Outcomes Through Adaptive Learning Systems using AI. International Transactions on Artificial Intelligence (ITALIC), 3(1), 21–31. https://doi.org/10.33050
Sari, V. N. I., Suwandi, S., & Sumarwati, S. (2022). Multimedia-Based Interactive Learning Media in The Text Material of The Observation Report. ICHSS (International Conference of Humanities and Social Science), 1(1), 224–230. https://doi.org/10.4108/eai.8-12-2021.2322747
Shi, P., & Liu, W. (2025). Adaptive learning oriented higher educational classroom teaching strategies. Scientific Reports, 7(9), 1–13. https://doi.org/10.1038/s41598-025-00536-y 1
Sibley, L., Fabian, A., Plicht, C., Pagano, L., Ehrhardt, N., Wellert, L., Bohl, T., & Lachner, A. (2025). Adaptive teaching with technology enhances lasting learning. Learning and Instruction, 99(06), 102141. https://doi.org/10.1016/j.learninstruc.2025.102141
Singh, M., James, P. S., Paul, H., & Bolar, K. (2022). Impact of cognitive-behavioral motivation on student engagement. Heliyon, 8(7), e09843. https://doi.org/10.1016/j.heliyon.2022.e09843
Smyrnova-Trybulska, E., Morze, N., & Varchenko-Trotsenko, L. (2022). Adaptive learning in university students’ opinions: Cross-border research. In Education and Information Technologies (Vol. 27, Issue 5). Springer US. https://doi.org/10.1007/s10639-021-10830-7
Staneviciene, E., & Žekien˙e, G. (2025). The Use of Multimedia in the Teaching and Learning Process of Higher Education : A Systematic Review. Sustainability, 17(8859), 1–26. https://doi.org/10.3390/ su17198859
Surbakti, R., Umboh, S. E., Ming Pong, & Dara, S. (2024). Cognitive Load Theory : Implications for Instructional Design in Digital Classrooms. International Journal of Educational Narrative, 2(6), 483–493. https://doi.org/10.70177/ijen.v2i6.1659
Taqiyya, W., Utami, R. D., Samsuri, M., & Siswanto, H. (2025). Strategies of Deep Learning to Foster Meaningful and Sustainable Education in the 21st Century. Journal of Deep Learning, 01(02), 127–138.
Tong, C., & Ren, C. (2025). Deep knowledge tracing and cognitive load estimation for personalized learning path generation using neural network architecture. Scientific Reports, 15(24925), 1–14. https://doi.org/10.1038/s41598-025-10497-
Vasilaki, E., & Mavrogianni, A. (2025). Extending Cognitive Load Theory : The CLAM Framework for Biometric , Adaptive , and Ethical Learning. Psychology International, 7(40), 1–25. https://doi.org/10.3390/ psycholint7020040
Wafik, H. M. A., Mahbub, S., & Das, J. (2024). Optimizing Strategies for Enhanced Effectiveness in Blended Learning Models. Cognizance Journal of Multidisciplinary Studies, 4(7), 197–219. https://doi.org/10.47760/cognizance.2024.v04i07.017
Wang, C., Zhang, Y., Ding, H., Z. (2023). Design of an online interactive teaching platform for rural music education based on artificial intelligence. Applied Mathematics and Nonlinear Sciences, 8(2), 3383–3392. https://doi.org/https://doi.org/10.2478/amns.2023.2.00944
Wang, F., Havard, D., & Barnhart, T. (2026). Approaching learning satisfaction : Factors influencing educational video learning. Education and Information Technologies, 27(04), 2–34. https://doi.org/10.1007/s10639-026-13996-0
Zamiri, M., & Esmaeili, A. (2024). Strategies, Methods, and Supports for Developing Skills within Learning Communities: A Systematic Review of the Literature. Administrative Sciences, 14(9), 2–33. https://doi.org/10.3390/admsci14090231
Zou, Y., Kuek, F., Feng, W., & Cheng, X. (2025). Digital learning in the 21st century: trends, challenges, and innovations in technology integration. Frontiers in Education, 10(3), 2. https://doi.org/10.3389/feduc.2025.1562391
Downloads
Published
Issue
Section
License
Copyright (c) 2026 rangga firdaus

This work is licensed under a Creative Commons Attribution 4.0 International License.














