When Algorithms Manage Learning: A Self-Determination Theory Lens on Student Engagement
DOI:
https://doi.org/10.47067/ramss.v9i1.656Keywords:
Algorithmic Management, Learners Engagement, Salience Priming and affect Nudging. Moderated Mediation, Process Macro, AMOSAbstract
This study investigates how algorithmic management, which is integrated into university online portals and LMS systems, influences learner engagement. Based on Self-Determination Theory, the study examines whether competence is necessary for establishing this link and how salience priming and affect nudging influence it. Despite the fact that algorithmic systems are widely discussed in business, little is known about how they impact learner engagement in higher education. The data was submitted by students who regularly use digital portals at public and private schools and universities. Standardized questionnaires were used to measure algorithmic management, competency needs, nudging exposure, and engagement. Following the validation of the measurement models, a moderated mediation analysis was employed to test the suggested connections. The findings confirmed the mediation theory by showing that algorithmic management improves learners' competency needs. The moderation hypothesis was supported by the strong interplay between algorithmic management, salience priming, and affective nudging, which significantly influenced competence need. The demand for gradually greater engagement in competence served as a foundation for the moderated mediation framework in general. Overall, the study found that the design of digital portals had a considerable impact on learners' involvement with their academic work. Nudging tactics, such as reminders, visual progress indicators, and individualized feedback, can help learners feel more capable and stay focused in their studies. In addition to pointing to future research on additional psychological demands and long-term effects on learning, the study broadens the discussion around algorithmic management in higher education.
References
Zcemoglu, D., Johnson, S., & Robinson, J. A. (2005). Institutions as a fundamental cause of long-run growth. Handbook of Economic Growth, 1(4), 385–472. https://doi.org/10.1086/674872
Almusfar, L. A. (2025). Improving learning management system performance: a comprehensive approach to engagement, trust, and adaptive learning. IEEE Access.
Autin, K. L., Herdt, M. E., Garcia, R. G., & Ezema, G. N. (2022). Basic psychological need satisfaction, autonomous motivation, and meaningful work: A self-determination theory perspective. Journal of Career Assessment, 30(1), 78-93.
Benlian, A., Wiener, M., Cram, W. A., Krasnova, H., Maedche, A., Möhlmann, M.,& Remus, U. (2022). Algorithmic management: bright and dark sides, practical implications, and research opportunities. Business & Information Systems Engineering, 64(6), 825-839.
Chatterjee, O., Sundar, A., Choudhary, A., Bernard, C. R., & Garcia, D. (2025, February). Auto Remind: Improving Student Academic Performance Through a Personalized and Automated Notification System. In Proceedings of the 56th ACM Technical Symposium on Computer Science Education V. 2 (pp. 1411-1412).
Chen, S. (2025). Perceived teacher support on international students’ engagement and psychological well-being in AI-based learning: The mediating role of motivation through the lens of self-determination theory. Learning and Motivation, 91, 102165.
Chetry, K. K. (2024). Transforming education: How AI is revolutionizing the learning experience. International Journal of Research Publication and Reviews, 5(5), 6352-6356.
Cho, H. J., Levesque-Bristol, C., & Yough, M. (2023). How autonomy-supportive learning environments promote Asian international students’ academic adjustment: A self-determination theory perspective. Learning Environments Research, 26(1), 51-76.
David, L., & Weinstein, N. (2024). Using technology to make learning fun: technology use is best made fun and challenging to optimize intrinsic motivation and engagement. European Journal of Psychology of Education, 39(2), 1441-1463.
Demir, E., & Akb?y?k, A. (2025). Examining the impact of digital nudging on user interaction with E-platforms: a study of working professionals in distance learning. Education and Information Technologies, 1-32.
Er-radi, H., Touis, B., & Aammou, S. (2024). Machine learning in adaptive online learning for enhanced learner engagement. In Technological tools for innovative teaching (pp. 43-63). IGI Global Scientific Publishing.
Fenuku, S. D., & Akpaku, E. (2024). The Impact of Standardized Teaching Methodologies on Student Learning and Teacher Autonomy in Ghanaian Schools. Journal of Linguistics, Literature, and Language Teaching (JLLLT), 3(2), 83-97.
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of marketing research, 18(1), 39-50.
Fryer, L. K., Bovee, H. N., Witkin, N., & Matthews, P. (2023). Nudging students' interest in learning a new language: An experimental proof of concept for an online informational-nudge. System, 119, 103162.
Godwin Olaoye, J. O., Flypaper, D., Oluwaseyi, J., & Brightwood, S. (2024). Ai-driven adaptive learning systems: enhancing student engagement.
Gupta, P., & Prashar, A. (2025). Learners’ psychological needs in online learning environment for executive education: role of cognitive overload and learning self-efficacy. Behaviour & Information Technology, 44(9), 1942-1963.
Harder, T., & Janneck, M. (2024, June). Digital nudging in online-learning environments: enhancing self-regulation and decision through usability-centric design. In International Conference on Human-Computer Interaction (pp. 3-18). Cham: Springer Nature Switzerland.
Hayes, A. F. (2018). Partial, conditional, and moderated moderated mediation: Quantification, inference, and interpretation. Communication monographs, 85(1), 4-40.
Hair, J., & Alamer, A. (2022). Partial Least Squares Structural Equation Modeling (PLS-SEM) in second language and education research: Guidelines using an applied example. Research Methods in Applied Linguistics, 1(3), 100027.
Izah, S. C., Sylva, L., & Hait, M. (2023). Cronbach's alpha: A cornerstone in ensuring reliability and validity in environmental health assessment. ES Energy & Environment, 23, 1057.
Jeno, L. M., Nylehn, J., Hole, T. N., Raaheim, A., Velle, G., & Vandvik, V. (2023). Motivational determinants of students’ academic functioning: The role of autonomy-support, autonomous motivation, and perceived competence. Scandinavian Journal of Educational Research, 67(2), 194-211.
Ji, H., Suo, L., & Chen, H. (2024). AI performance assessment in blended learning: mechanisms and effects on students’ continuous learning motivation. Frontiers in Psychology, 15, 1447680.
Jobst, L. J., Bader, M., & Moshagen, M. (2023). A tutorial on assessing statistical power and determining sample size for structural equation models. Psychological Methods, 28(1), 207.
Khan, A. (2024). Exploring the role of machine learning in enhancing student engagement and performance. AI EDIFY Journal, 1(2), 24-35.
Khan, A. A., Yousaf, A., & Sher, G. (2025). The Impact of Digital Marketing on Impulsive Buying Behavior: The Mediating Role of Peer Influence, Evidence from University Students in Karachi, Pakistan. Bulletin of Management Review, 2(4), 47-71.
Kostopoulos, G., Gkamas, V., Rigou, M., & Kotsiantis, S. (2025). Agentic AI in Education: State of the Art and Future Directions. IEEE Access.
Kuadey, N. A., Mahama, F., Ankora, C., Bensah, L., Maale, G. T., Agbesi, V. K., & Adjei, L. (2023). Predicting students’ continuance use of learning management system at a technical university using machine learning algorithms. Interactive Technology and Smart Education, 20(2), 209-227.
Kadolkar, I., Kepes, S., & Subramony, M. (2025). Algorithmic management in the gig economy: A systematic review and research integration. Journal of Organizational Behavior, 46(7), 1057-1080.
Nopas, D. S. (2025). Algorithmic learning or learner autonomy? Rethinking AI’s role in digital education. Qualitative Research Journal.
Parent-Rocheleau, X., Gagné, M., & Bujold, A. (2024). A self-determination theory framework to develop motivation-enhancing algorithmic management. In Research handbook on human resource management and disruptive technologies (pp. 23-38). Edward Elgar Publishing.
Parker, S. K., & Grote, G. (2022). Automation, algorithms, and beyond: Why work design matters more than ever in a digital world. Applied psychology, 71(4), 1171-1204.
Plak, S., Van Klaveren, C., & Cornelisz, I. (2023). Raising student engagement using digital nudges tailored to students' motivation and perceived ability levels. British Journal of Educational Technology, 54(2), 554-580.
Podsakoff, P. M., MacKenzie, S. B., & Podsakoff, N. P. (2012). Sources of method bias in social science research and recommendations on how to control it. Annual review of psychology, 63(1), 539-569.
Poncio, T., & Butterworth, R. (2025). Today’s Learners, Tomorrow’s Leaders: Inspire Your Students to Succeed in School and beyond. ASCD.
Preacher, K. J., & Hayes, A. F. (2008). Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behavior research methods, 40(3), 879-891.
Richardson, J. M. (2024). A Nudge in the Right Direction: Nudging College Students in a Flipped Class to Become More Self-Regulatory (Doctoral dissertation, Boise State University).
Ryan, R. M., & Deci, E. L. (2023). Acting as one: self-determination theory’s scientific and existential import.
Sanchez-De Miguel, M., Orkaizagirre-Gomara, A., Izagirre-Otaegi, A., Badiola, I., Ortiz de Elguea-Díaz, F. J., Gomez-Gastiasoro, A. & Goudas, M. (2023). Association among university students’ motivation, resilience, perceived competence, and classroom climate from the perspective of self-determination theory. Education Sciences, 13(2), 147
Sarker, B. K., Sarker, D. K., Rani Shaha, S., Saha, D., & Sarker, S. (2024). Why apply SPSS, SmartPLS and AMOS: An essential quantitative data analysis tool for business and social science research investigations. SmartPLS and AMOS: An Essential Quantitative Data Analysis Tool for Business and Social Science Research Investigations (September 10, 2024).
Schaufeli, W. B., Bakker, A. B., & Salanova, M. (2006). The measurement of work engagement with a short questionnaire: A cross-National Study. Educational and Psychological Measurement, 66(4), 701–716
Schlegel, L., Schöbel, S., & Söllner, M. (2023). Nudging Digital Learning–An Experimental Analysis of Social Nudges to Manage Self-Regulated Learning and Online Learning Success.
Sekaran, U., & Bougie, R. (2020). Research methods for business: A skill building approach. John Wiley & Sons
Shen, H., Ye, X., Zhang, J., & Huang, D. (2024). Investigating the role of perceived emotional support in predicting learners’ well-being and engagement mediated by motivation from a self-determination theory framework. Learning and motivation, 86, 101968.
Silva, V. H., & Duarte, A. P. (2025). The role of work engagement and psychological safety in how socially responsible human resources management affects employee voice Behaviour. International Journal of Organizational Analysis, 33(8), 2704-2728.
Sovey, S., Osman, K., & Mohd-Matore, M. E. (2022). Exploratory and confirmatory factor analysis for disposition levels of computational thinking instrument among secondary school students. European Journal of Educational Research, 11(2), 639-652
Valta, M., & Maier, C. (2025). Digital nudging: a systematic literature review, taxonomy, and future research directions. ACM SIGMIS Database: The DATABASE for Advances in Information Systems, 56(1), 101-125.
Weijers, R., de Koning, B., Vermetten, Y., & Paas, F. (2023). Nudging autonomous learning behavior: Three field experiments. Education Sciences, 13(1), 49.
Wilson, A. L., Buckley, E., Buckley, J., & Bogomolova, S. (2016). Nudging healthier food and beverage choices through salience and priming: Evidence from a systematic review. Food Quality and Preference, 51, 47–64
Xu, X. (2025). AI optimization algorithms enhance higher education management and personalized teaching through empirical analysis. Scientific Reports, 15(1), 10157
Yadav, S. (2025). Leveraging AI to enhance teaching and learning in education: The role of artificial intelligence in modernizing classroom practices. In Optimizing research techniques and learning strategies with digital technologies (pp. 211-238). IGI Global Scientific Publishing.
Zayid, H., Alzubi, A., Berbero?lu, A., & Khadem, A. (2024). How Do Algorithmic Management Practices Affect Workforce Well-Being? A Parallel Moderated Mediation Model. Behavioral Sciences, 14(12), 1123.
