Abstract
This study investigates the predictive relationship between academic self-efficacy and timely degree progression among Arab university students in the UAE. Using binary logistic regression on data from 1,420 students, results show modest predictive value and highlight cultural variables influencing academic timelines. The findings underscore the need for AI-enhanced, context-sensitive student support systems. This research contributes to educational policy and practice by aligning predictive analytics with equity-focused retention strategies.
References
- Bandura A. “Self-efficacy: Toward a unifying theory of behavioral change”. Psychological Review 84.2 (1977): 191-215.
- Bandura A. “Social foundations of thought and action: A social cognitive theory”. Prentice-Hall (1986).
- Bean JP. “Dropouts and turnover: The synthesis and test of a causal model of student attrition”. Research in Higher Education 12.2 (1980): 155-187.
- Benkwitz A., et al. “Using student data: Student-staff collaborative development of compassionate pedagogic interventions based on learning analytics and mentoring”. Journal of Hospitality, Leisure, Sport & Tourism Education 25 (2019): 100202.
- Darvishi A., et al. “Incorporating AI and learning analytics to build trustworthy peer assessment systems”. British Journal of Educational Technology 53.4 (2022): 844-875.
- David N. AI-powered virtual assistant solution for supporting international students (2024).
- Deetjen-Ruiz R. Self-efficacy’s relationship with untimely university graduation and student retention: A quantitative regression study [Doctoral dissertation, American College of Education] (2023).
- Deetjen-Ruiz R, Toman T and England D. “Rethinking higher education in the AI era: Some thoughts from the United Arab Emirates”. Policy Briefing (2024).
- Hilty DM, Cheng Y and Luxton DD. “Artificial Intelligence and Predictive Modeling in Mental Health”. In Digital Mental Health: The Future is Now. Cham: Springer Nature Switzerland (2025): 323-350.
- Juszkiewicz J. “Trends in community college enrollment and completion data”. American Association of Community Colleges (2017).
- Ka Zenzile M. Decolonizing visualities: changing cultural paradigms, freeing ourselves from Western-centric epistemes (2017).
- Latif G., et al. “Identifying “at-risk” students: An AI-based prediction approach”. International Journal of Computing and Digital Systems 11.1 (2022).
- Ministry of Artificial Intelligence. UAE National Strategy for Artificial Intelligence. In National Program for Artificial Intelligence (2018).
- Singh H., et al. “Framework for suggesting corrective actions to help students intended at risk of low performance based on experimental study of college students using explainable machine learning model”. Education and Information Technologies 29.7 (2024): 7997-8034.
- Singh H., et al. “Framework for suggesting corrective actions to help students intended at risk of low performance based on experimental study of college students using explainable machine learning model”. Education and Information Technologies 29.7 (2024): 7997-8034.
- Spady WG. “Dropouts from higher education: An interdisciplinary review and synthesis”. Interchange 1.1 (1970): 64-85.
- Tinto V. “Leaving college: Rethinking the causes and cures of student attrition (2nd ed.)”. University of Chicago Press (1993).
- UniquelyZU. (n.d.). zu.ac.ae. (2025).
- Yokoyama S. “Academic self-efficacy and academic performance in online learning: A mini review”. Frontiers in psychology 9 (2019): 2794.
- Zander L, Höglund P and Rantatalo O. “Self-efficacy and academic resilience: Predicting academic performance and persistence”. Journal of Educational Psychology 110.7 (2018): 970-985.