This is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.
Rupali Jadhav,
Piyush Girase,
- PhD scholar, Department of Master of Computer Science, Atmiya University Rajkot, Gujarat, India
- Software engineer, Department of Computer Science, Cognizant Technology Solutions, Tamil Nadu, India
Abstract
Most contemporary digital learning environments encounter persistent challenges when it comes to accurately identifying students who are at risk of academic underperformance. These challenges often arise due to limited visibility into learners’ engagement levels and gaps in conceptual understanding, particularly during the early stages of a course. To address this issue, the present study proposes an early prediction framework that leverages comprehensive student-related data through the application of machine learning techniques. The framework aims to detect at-risk students before their performance significantly declines, enabling timely interventions.
In this research, five classification algorithms—Random Forest, Logistic Regression, Support Vector Machine (SVM), and K-Nearest Neighbors (KNN)—were trained using a student performance dataset. The models were evaluated using standard performance metrics, including accuracy, precision, recall, and F1-score, to ensure a balanced and reliable comparison. Among all the tested approaches, tree-based ensemble methods, particularly the Random Forest classifier, demonstrated superior predictive performance across most evaluation criteria. This highlights the effectiveness of ensemble learning in capturing complex patterns within educational data.
The findings suggest that integrating learning analytics with machine learning techniques can significantly enhance the early identification of academically vulnerable students. Such predictive systems can support educators by enabling timely academic assistance, personalized learning strategies, and targeted resource allocation. Despite certain limitations, such as dataset size and class imbalance, the study underscores the strong potential of data-driven approaches to minimize learning gaps and promote improved student outcomes in real-world educational settings.
Keywords: Machine Learning, Learning Analytics, At-Risk Student Performance, Random Forest, Classification Models, Academic Risk Detection, Predictive Modeling
Rupali Jadhav, Piyush Girase. Predictive Analytics and Adaptive Learning: A Machine Learning Framework for Reducing Learning Gaps. International Journal of Algorithms Design and Analysis Review. 2026; 04(01):-.
Rupali Jadhav, Piyush Girase. Predictive Analytics and Adaptive Learning: A Machine Learning Framework for Reducing Learning Gaps. International Journal of Algorithms Design and Analysis Review. 2026; 04(01):-. Available from: https://journals.stmjournals.com/ijadar/article=2026/view=241136
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International Journal of Algorithms Design and Analysis Review
| Volume | 04 |
| 01 | |
| Received | 22/12/2025 |
| Accepted | 06/01/2026 |
| Published | 27/04/2026 |
| Publication Time | 126 Days |
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