Date
Publisher
arXiv
The rapidly increasing student population has posed serious challenges to the traditional academic advising process. This study designs and implements a multi-purpose intelligent system to support students' academic progress, based on a two-part hybrid framework: (1) an advanced model for grade prediction and (2) a rule-based recommendation engine. Using a dataset containing 416,558 educational records from the University of Birjand, students were first divided into homogeneous clusters using the Gaussian Mixture Model (GMM). Subsequently, a Stacking Ensemble model combining Random Forest, Gradient Boosting, and MLP was trained specifically for each cluster. Evaluation results demonstrated that the Stacking model outperformed base models across all clusters, achieving a final aggregated RMSE of 2.35. The second component is an expert system that provides intelligent recommendations by synergizing educational regulations with the grades predicted by the first component. This system has been implemented as a practical tool on the University of Birjand portal, offering students real-time feedback such as semester GPA prediction, probation risk warnings, and course suggestions for GPA improvement.
Study at a glance
- Question: Can combining machine learning grade predictions with a rule-based expert system improve academic advising and course recommendations?
- Setting and data: University of Birjand in Iran, 416,558 academic course records from 2021-2022 through 2024-2025, plus a survey of 50 students.
- Method: Machine learning grade prediction with a rule-based expert system, followed by a usability survey.
Why it matters
Traditional academic advising faces serious challenges scaling to growing student populations, while existing AI systems often function as black boxes with limited transparency and weak alignment with institutional regulations.
Key findings
- The cluster-based ensemble model achieved an overall root mean squared error of 2.35 and an R-squared of 0.53, outperforming a model trained without clustering.
- The ensemble approach consistently produced lower prediction errors than its individual base models across all ten student clusters.
- In a usability evaluation of 50 students, 88% agreed that the probation risk alerts were effective and timely, and 90% intended to use the tool again.
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