An Intelligent Recommendation System for Automating Academic Advising Based on Curriculum Analysis and Performance Modeling

The explosive increase in educational data and information systems has led to new teaching practices, challenges, and learning processes. To effectively manage and analyze this information, it is crucial to adopt innovative methodologies and techniques. Recommender systems (RSs) offer a solution for advising students and guiding their learning journeys by utilizing statistical methods such as machine learning (ML) and graph analysis to analyze program and student data. This paper introduces an RS for advisors and students that analyzes student records to develop personalized study plans over multiple semesters. The proposed system integrates ideas from graph theory, performance modeling, ML, explainable recommendations, and an intuitive user interface. The system implicitly implements many academic rules through network analysis. Accordingly, a systematic and comprehensive review of different students' plans was possible using metrics developed in the mathematical graph theory. The proposed system systematically assesses and measures the relevance of a particular student's study plan. Experiments on datasets collected at the University of Dubai show that the model presented in this study outperforms similar ML-based solutions in terms of different metrics. Typically, up to 86% accuracy and recall have been achieved. Additionally, the lowest mean square regression (MSR) rate of 0.14 has been attained compared to other state-of-the-art regressors.

Automated summary

The increase in educational data and information systems has created new challenges and learning processes. Recommender systems, utilizing statistical methods like machine learning and graph analysis, provide personalized study plans for students by analyzing their records. This proposed system integrates graph theory, ML, and explainable recommendations to assess and measure the relevance of study plans. Experiments show that it outperforms similar ML-based solutions, achieving up to 86% accuracy and recall, with a low mean square regression rate.