Citation

Sifat, Hasin Almas and Saron, Ashiqur Rahman and Ahmmed, Md. Mortuza and Miah, Md Saef Ullah and Liew, Tze Hui (2025) Exploring the Barriers to Undergraduate Student Engagement with Research Papers: A Machine Learning and Statistical Analysis Approach. In: 2025 Multimedia University Engineering Conference, MECON 2025, 21 July 2025 - 23 July 2025, Cyberjaya, Malaysia.

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Abstract

University students often have significant obstacles to participating in research publications, motivated by characteristics such as language difficulties, approach problems, help from instructors, and content distribution options. This study discovered these characteristics and their influence on students' commitment to reading. A complete survey is done, concentrating on variables including text difficulty, accessibility problems, teacher encouragement, topic preferences, and study environment. To investigate the data, a multivariable linear regression model was applied to evaluate the connections between independent characteristics and student engagement. Preliminary studies have shown that the combination of these components has a significant impact on the participation decisions. Several advanced machine learning models such as Gradient Boosting Machines (GBM), Random Forest, and Support Vector Machines (SVM) were deployed. The Random Forest and GBM models achieved 95% accuracy in predicting student engagement. Machine Learning models were employed to identify and predict the key cognitive, motivational, and accessibility-related factors. This study emphasized the importance of solving these challenges to improve research skills, improve access, and establish a solid relationship between students and academic resources. In short, we can say that the results give practical advice to educational institutions to design a more effective approach to increase student interaction with research publications.

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	Student engagement, machine learning, educational barriers
Subjects:	L Education > LB Theory and practice of education > LB2300 Higher Education Q Science > Q Science (General) > Q300-390 Cybernetics
Divisions:	Faculty of Information Science and Technology (FIST)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	18 Mar 2026 08:03
Last Modified:	19 Mar 2026 01:00
URII:	http://shdl.mmu.edu.my/id/eprint/15572

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