Abstract
This study investigates a practical solution that supports teachers, learners, and administrators in open distance learning through academic analytics. The objective is to enhance self-regulation and motivations by providing personalized and adaptive insight to learners by designing and implementing a prototype intelligent student facing learning analytics dashboard (SF-LAD) grounded in educational theory, leveraging a multi-tier computational pipeline in developing and employing an iterative, user-centered design (UCD) methodology. The system architecture integrates learning management system (LMS) data with an analytics engine utilizing a sentiment classification model (XLNet) and a predictive model (K-means clustering). The dashboard is intelligent in the sense that it embeds machine-learning models that autonomously interpret data to drive downstream analytical behavior. Research finding demonstrates that intelligent SF-LAD has decent usability. The study exemplifies the potential of intelligent frameworks and provides a foundation for long-term research into the impact of automated insights on distance learner success.Article Preview
Top2. Literature Review
Historically, business intelligence (BI) and data visualization within academic institutions have primarily addressed transactional processes such as student enrollment and admission, rather than examining students' learning behaviors. However, nowadays, there is a growing trend that emphasizes supporting students by analyzing their behavioral patterns and academic progress (Durall & Gros, 2014). Recent randomized experiments empirically support this shift by demonstrating that well-designed dashboards can have a major impact on students’ engagement and success in online environments (Borrella, 2025). Furthermore, development of ecological classroom teaching models, which leverage big data to construct systematic paths for data-driven instruction, also support this shift (Jin, 2025).