Recommender System for STEM Enrolment in Universities Using Machine Learning Algorithms - Case of Kenyan Universities

Aug 3, 2026

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Technology, Engineering, and Mathematics (STEM) enrolment has become an active research area due to rising demand for STEM skills and the need for systems that increase enrolment. The study investigates recommender systems for university STEM enrolment using machine learning algorithms to help students select courses aligned with their attributes. It evaluates SVM, artificial neural networks, and Naïve Bayes using accuracy and validation methods. Results show ANN performs best and that high school grades and interest in STEM are key predictive features, supporting guidance for course emphasis and funding priorities.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages12

Reading Time30 min

AccessFree to download

Introduction

Recommender systems and STEM enrolment

Machine learning approaches for recommendation

Recommender System For Stem Enrolment In Universities Using Machine Learning Algorithms: Case Of Kenyan Universities Benard Ondiek1 *, Lucy Waruguru2, Stephen Njenga3 1,2 School of Technology, KCA University, Nairobi, Kenya 3 Department of Computer Science, Murang’a University of Technology, Muranga, Kenya *Corresponding Author: [Email: ](Email: benmacondiek@gmail.com)[benmacondiek@gmail.com](Email: benmacondiek@gmail.com) Abstract. Technology, Engineering, and Mathematics (STEM) enrolment has gained a lot of research interest. The increase in demand for STEM-based skill sets has contributed to the need for systems that could potentially increase enrolments in the field. The purpose of this study was to investigate recommender systems for STEM enrolment in universities using machine learning algorithms. Students face challenges while selecting STEM courses that match their attributes. This article aims to provide a recommender system for STEM enrolment using machine learning algorithms. The article investigates three machine learning algorithms which include Support Vector Machine (SVM), Artificial Neural Network (ANN), and Naïve Bayes. Accuracy and validation techniques were applied to test the algorithms. The results demonstrated that our work performed better than that of the published research, with the ANN outperforming other classification methods. The results position ANN as an important algorithm in building a recommender model for STEM higher education enrolment. The study also identifies high school grades and Interest in STEM courses as important features in predicting STEM course enrolment in higher education. The study will guide policy on the courses to lay more emphasis on, as well asfor the funding authorities to prioritize funding allocation for STEM-based courses. Keywords: Machine Learning, Higher Education, Support Vector Machine, Engineering and Mathematics. I. INTRODUCTION Despite the need for a more skilled workforce, STEM has not attracted many students. This has been attributed to the challenge of misalignments of students’ capabilities with the courses they choose in Higher Education [1] . According to He and Jang [2], there is a requirement for national efforts to establish STEM education due to the worldwide desire for STEM workers. Technological innovation and the search for "need-based and practical solutions" drive the four distinct STEM disciplines" [3] . As presented by Sustainable Development Goal(SDG) 4 [4] there is a drive for students to select courses that will result in more innovations. As stated by [5], researchers are actively looking for ways to improve STEM courses since they believe these courses can help students develop "21st-century skills"[6] . This has been reinforced by Tawbush et al. [7] who stress that researchers are increasingly concentrating on STEM because of the development of technology[8] and the rise in environmental threats. The growth experienced in STEM courses would be attributed to the need to reduce the gap between academia and the industry [9] . Science, Technology, Engineering and Mathematics comes in handy to propel the economy in the ever-increasing competition [3], [10] . Despite the numerous choices a student must make while selecting courses, getting the appropriate courses is still daunting [11] . A recommender system can be defined as a platform that helps match students’ interests with the STEM courses they are applying to [12], [13] . According to Girase et al. [14], the Recommender system encompasses several artificial intelligence techniques. Some techniques widely used with recommender systems include machine learning and data mining [15] . Recommendation comprises three phases which include information, the learning phase, and finally the prediction [16] . Recommender systems' primary goal is to reduce users' information overload while allowing the personalization of recommendations for better decision-making[17] . Recommend

FAQs

What problem does the study address for STEM enrolment in universities?

Students often struggle to choose STEM courses that match their attributes, contributing to low enrolment. The study targets this mismatch by using a recommender system to guide course selection.

Which machine learning algorithms are evaluated in the study?

The study investigates Support Vector Machine (SVM), Artificial Neural Network (ANN), and Naïve Bayes. It applies accuracy and validation techniques to test their performance.

What features are identified as important for predicting STEM course enrolment?

High school grades and students’ interest in STEM courses are identified as key features for predicting STEM enrolment in higher education.

Aug 3, 2026

Upload by Levi