AI-Driven Learning Analytics and their Effect on Academic Persistence: The Mediating Role of Academic Self-Efficacy
DOI:
https://doi.org/10.47067/ramss.v9i1.616Keywords:
AI-driven Learning Analytics, Academic Persistence, Academic Self-Efficacy, Higher Education, Mediation Analysis, Student Retention, Educational Technology, Quantitative ResearchAbstract
The present study examined the effect of AI-driven learning analytics on academic persistence, with academic self-efficacy serving as a mediating variable among undergraduate students. A quantitative, cross-sectional research design was adopted, and data were collected from 245 university students using a structured questionnaire based on standardized scales. Stratified random sampling was used to ensure representation across academic disciplines and study levels. The SPSS was used to analyze data and provide descriptive statistics, Pearson correlation, multiple regression, and mediation analysis. These findings showed that academic persistence has a strong positive correlation with AI-driven learning analytics (r =.482, p <.01). Regression analysis showed that AI-based learning analytics were important predictors of academic self-efficacy (b =.536, p <.001) with 28.7% of the variance (R2 =.287). In addition, the mediation analysis showed that the academic self-efficacy was a significant predictor of the relationship between AI-driven learning analytics and academic persistence (F = 82.47, p < .001), which proves the mediating effect. The results indicate that AI-based feedback can be the reason both to improve persistence among students and to increase their academic self-efficacy, which subsequently leads to persistence in their commitment to academic objectives. The research is relevant to integration of technological innovation and motivational theory in higher education through highlighted psychological processes of AI-assisted learning contexts.
References
Altememy, H. A., Mohammed, B. A., Hsony, M. K., Hassan, A. Y., Mazhair, R., Dawood, I. I., ... & Sharif, H. R. (2023). The influence of the artificial intelligence capabilities of higher education institutions in Iraq on students’ academic performance: The role of AI-based technology application as a mediator. Eurasian Journal of Educational Research, 104(104), 267-282.
Arnold, K. E., & Pistilli, M. D. (2012, April). Course signals at Purdue: Using learning analytics to increase student success. In Proceedings of the 2nd international conference on learning analytics and knowledge (pp. 267-270).
Ashraf, A., Gulshan, F., Humza, A., & Aslam, M. A. (2025). The Incorporation of AI-Driven Study Support Tools to Promote Academic Self-Efficacy and Reduce Procrastination Behaviors in STEM Students. The Asian Bulletin of Big Data Management, 5(1), 5-1.
Baker, R. S., & Inventado, P. S. (2016). Educational data mining and learning analytics: Potentials and possibilities for online education. Emergence and innovation in digital learning: Foundations and applications, 83-98.
Bandura, A. (2006). Guide for constructing self-efficacy scales.
Bandura, A., & Wessels, S. (1997). Self-efficacy (Vol. 10). Cambridge: Cambridge University Press.
Batool, S., Khan, S. H., Jogezai, Y., & Mullazai, S. (2025). AI-Based Personalized Education: Exploring the Psychological Determinants of Student Achievement and Persistence. The Critical Review of Social Sciences Studies, 3(4), 854-867.
Bean, J. P., & Metzner, B. S. (1985). A conceptual model of nontraditional undergraduate student attrition. Review of educational Research, 55(4), 485-540.
Chemers, M. M., Hu, L. T., & Garcia, B. F. (2001). Academic self-efficacy and first year college student performance and adjustment. Journal of Educational psychology, 93(1), 55.
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.
Ferguson, R. (2012). Learning analytics: drivers, developments and challenges. International journal of technology enhanced learning, 4(5-6), 304-317.
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.
Khan, M. Z., Khan, A. Z., Ashraf, K. S., & Khan, S. Z. (2025). The Influence of AI-Enhanced Learning Analysis on Student Behavior and Academic Persistence from Higher Education in Pakistan. The Critical Review of Social Sciences Studies, 3(4), 2754-2767.
Lyu, W., & Salam, Z. A. (2025). AI-powered personalized learning: Enhancing self-efficacy, motivation, and digital literacy in adult education through expectancy-value theory. Learning and Motivation, 90, 102129.
Qadeer, A. (2025). The mediating impact of student engagement on the association between generative AI-based feedback and academic performance in higher education. Journal of Research, Innovation, and Strategies for Education (RISE), 2(1), 29-44.
Raza, S., Rais, L., & Ashraf, S. (2025). Al-Enhanced Learning Enjoyments an Empirical Analysis of Student Motivation Engagement Cognitive Outcomes and Academic Persistence. Review of Applied Management and Social Sciences, 8(4), 1659-1671.
Shi, S., & Zhang, H. (2025). EFL students’ motivation predicted by their self-efficacy and resilience in artificial intelligence (AI)-based context: From a self-determination theory perspective. Learning and Motivation, 91, 102151.
Siemens, G. (2013). Learning analytics: The emergence of a discipline. American behavioral scientist, 57(10), 1380-1400.
Siregar, H., Munir, M., Sobandi, A., Riza, L. S., & Nusratullo, S. (2025). Mediating Role of Artificial Intelligence in Linking Self-Efficacy and Learner Performance. Education and Human Development Journal, 10(2), 267-286.
Sufyan Ghaleb, M. M., & Alshiha, A. A. (2023). Empowering Self-Management: Unveiling the Impact of Artificial Intelligence in Learning on Student Self-Efficacy and Self-Monitoring. Eurasian Journal of Educational Research (EJER), (107).
Tempelaar, D., Rienties, B., & Nguyen, Q. (2020). Subjective data, objective data and the role of bias in predictive modelling: Lessons from a dispositional learning analytics application. PloS one, 15(6), e0233977.
Tinto, V. (1997). Classrooms as communities: Exploring the educational character of student persistence. The Journal of higher education, 68(6), 599-623.
Xu, Q., Liu, Y., & Li, X. (2025). Unlocking student potential: How AI-driven personalized feedback shapes goal achievement, self-efficacy, and learning engagement through a self-determination lens. Learning and Motivation, 91, 102138.
Yavich, R., Davidovitch, N., & Gerkerova, A. (2025). Exploring the Association between AI Tool Use and Academic Self-Efficacy among University Students in Israel. African Educational Research Journal, 13(3), 311-324.
Zafar, Q., Shahid, H. A., & Khan, S. M. (2025). AI Mediated Learning and its Influence on Psychological Resilience, Academic Performance, and Peer Interaction. Review of Applied Management and Social Sciences, 8(4), 1633-1644.
Zhang, X., Zhao, X., & Li, X. (2025). AI-driven academic engagement in fine arts education: the chain-mediating roles of self-efficacy and achievement emotions. Education and Information Technologies, 30(17), 25113-25136.
Zhu, Y. (2026). A study of the effect of AI usage on students' self-efficacy and academic performance in business education: Moderating effect of teacher support [Registered report-stage I]. Acta Psychologica, 263, 106256.
Zimmerman, B. J. (2000). Attaining self-regulation: A social cognitive perspective. In Handbook of self-regulation (pp. 13-39). Academic press
