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

Shadi Atalla, Mohammad Daradkeh, Amjad Gawanmeh, Hatim Khalil, Wathiq Mansoor, Sami Miniaoui and Yassine Himeur ()
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Shadi Atalla: College of Engineering and IT, University of Dubai, Dubai 14143, United Arab Emirates
Mohammad Daradkeh: College of Engineering and IT, University of Dubai, Dubai 14143, United Arab Emirates
Amjad Gawanmeh: College of Engineering and IT, University of Dubai, Dubai 14143, United Arab Emirates
Hatim Khalil: General Undergraduate Curriculum Requirements, University of Dubai, Dubai 14143, United Arab Emirates
Wathiq Mansoor: College of Engineering and IT, University of Dubai, Dubai 14143, United Arab Emirates
Sami Miniaoui: College of Engineering and IT, University of Dubai, Dubai 14143, United Arab Emirates
Yassine Himeur: College of Engineering and IT, University of Dubai, Dubai 14143, United Arab Emirates

Mathematics, 2023, vol. 11, issue 5, 1-25

Abstract: 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.

Keywords: recommender systems; curriculum design; computing curriculum; degree completion time; graduation rate; prerequisite network; student performance prediction (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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