An efficient course recommendation system for higher education students using machine learning techniques
Arcinas M.M. Meenakshi Bahalkar P.S. Bhaturkar D. Lalar S. Rane K.P. Garg S. Omarov B. Raghuvanshi A.
April 2025Institute of Advanced Engineering and Science
Bulletin of Electrical Engineering and Informatics
2025#14Issue 21468 - 1475 pp.
Education institutions and teachers are in desperate need of automated, non-intrusive means of getting student feedback that would allow them to better understand the learning cycle and assess the success of course design. Students would benefit from a framework that intelligently guides their actions and provides exercises or resources to support and enhance their learning. The recommender system framework is a software agent that learns the users preferences through a variety of channels and then utilizes that knowledge to provide product suggestions. A recommendation engine considers all potential user interests as background information, uses that knowledge to produce convincing recommendations, and then returns those ideas to the user. This article presents a feature selection and machine learning based course recommendation system for higher education students. principal component analysis (PCA) algorithm is used for feature selection. AdaBoost, k nearest neighbour (KNN), and Naïve Bayes algorithms are used to classify and predict student data. It is found that the AdaBoost algorithm is having better accuracy and F1 score for course recommendation to students. PCA AdaBoost is achieving an accuracy of 99.5%.
Accuracy , AdaBoost , Course recommendation , F1 measure , Feature selection , machine learning , Principal component analysis
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Department of Sociology and Behavioral Sciences, De La Salle University, Manila, Philippines
School of Journalism and Mass Communication, Apeejay Stya University, Sohna, India
Department of Artificial Intelligence and Data Science, Dr. D Y Patil Institute of Technology, Pune, India
Department of Information Technology, Savitribai Phule University, International Institute of Information Technology, Pune, India
Department of Engineering and Technology, Gurugram University, Gurugram, India
Department of Electronics and Telecommunications Engineering, Bharati Vidyapeeth College of Engineering, Navi Mumbai, India
Amity Business School, Amity University Madhya Pradesh, Gwalior, India
International Information Technology University, Al-Farabi Kazakh National University, Almaty, Kazakhstan
Faculty of Computer Engineering, Mahakal Institute of Technology, Ujjain, India
Department of Sociology and Behavioral Sciences
School of Journalism and Mass Communication
Department of Artificial Intelligence and Data Science
Department of Information Technology
Department of Engineering and Technology
Department of Electronics and Telecommunications Engineering
Amity Business School
International Information Technology University
Faculty of Computer Engineering
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