SHUBHANK CHATURVEDI,
- Research scholar, Department of Education, Maharaja Agrasen Himalayan Garhwal University, Pauri Garhwal, Uttarakhand, India
Abstract
The integration of predictive analytics into higher education has significantly transformed institutional decision-making processes. However, prevailing implementations remain predominantly performance-centered, focusing on dropout prediction and grade forecasting rather than holistic developmental outcomes. Concurrently, higher education systems worldwide are confronting escalating concerns regarding student mental health, disengagement, career uncertainty, and labor market volatility. These intersecting challenges necessitate a broader theoretical reconceptualization of predictive analytics—one that integrates psychological well-being and long-term occupational success as central educational outcomes. This paper advances a comprehensive developmental systems framework positioning predictive
analytics as an ethically governed, adaptive guidance architecture. Drawing upon learning analytics, educational data mining, positive psychology, self-determination theory, human capital theory, social cognitive career theory, and career construction theory, the proposed model conceptualizes
educational trajectories as dynamic, multidimensional systems influenced by cognitive, socio-emotional, behavioral, and contextual variables. The framework comprises five interdependent components: multidimensional data integration, predictive modeling architecture, adaptive intervention systems, developmental feedback loops, and occupational alignment pathways. Ethical governance is embedded as a structural layer throughout. Four theoretical propositions are articulated to guide empirical testing. The paper contributes to the literature by reframing predictive analytics from a surveillance-based risk detection mechanism into
a developmental empowerment system capable of enhancing both well-being and long-term employability. Institutional design implications, governance considerations, and a future research agenda are discussed.
Keywords: Predictive analytics, student well-being, occupational success, learning analytics, AI in education, career adaptability, developmental systems, ethical AI
SHUBHANK CHATURVEDI. Predictive Analytics for Student Well-Being and Occupational Success. International Journal of Education Sciences. 2026; 03(01):-.
SHUBHANK CHATURVEDI. Predictive Analytics for Student Well-Being and Occupational Success. International Journal of Education Sciences. 2026; 03(01):-. Available from: https://journals.stmjournals.com/ijes/article=2026/view=246861
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International Journal of Education Sciences
| Volume | 03 |
| 01 | |
| Received | 31/03/2026 |
| Accepted | 28/05/2026 |
| Published | 29/05/2026 |
| Publication Time | 59 Days |
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