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AI-Based MCA Student Performance Prediction and Personalized Learning System
Subject area: Science,Engineering and Technology · Area of research: Artificial Intelligence
DOI: 10.64388/IREV10I4-1724135
Abstract
Early identification of academic difficulty is most useful when it leads to timely, relevant support. This paper proposes an integrated framework for students in the Master of Computer Applications (MCA) programme. The framework is designed to estimate end-of-semester performance from internal assessments, assignments, attendance, prior results and study behaviours; identify students who may need support; locate subject-level learning gaps; and present tailored study suggestions through a dashboard reviewed by instructors. The study has two components. Phase 1 reports descriptive findings from a questionnaire completed by 102 MCA students. Thirty-three respondents reported attendance below 75%, 40 reported studying no more than two hours per day outside college, and 78 of 101 respondents expressed interest in personalised study recommendations, particularly video lectures and practice questions. Exploratory Spearman analyses using previous-semester CGPA (97 usable records) found a weak positive association with internal assessment marks; this association remained below the Bonferroni-adjusted threshold. No clear association was observed for study hours, revision regularity or programming practice. Phase 2 is proposed, not completed: it specifies candidate machine-learning models, an evaluation plan and recommendation rules, but no model has been trained because final SGPA outcomes have not yet been collected.
Keywords
student performance prediction; educational data mining; personalized learning; learning recommendations; at-risk students; explainable artificial intelligence; MCA
References
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[2] Albreiki B, Zaki N, Alashwal H. A systematic literature review of students’ performance prediction using machine learning techniques. Education Sciences. 2021;11(9):552. https://doi.org/10.3390/educsci11090552
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How to cite this paper
@article{1724135,
author = {Prof. Sarika Kore, Gopal Solanki, Aarti Tevar},
title = {AI-Based MCA Student Performance Prediction and Personalized Learning System},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {10},
number = {4},
pages = {2815-2829},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1724135.pdf},
abstract = {Early identification of academic difficulty is most useful when it leads to timely, relevant support. This paper proposes an integrated framework for students in the Master of Computer Applications (MCA) programme. The framework is designed to estimate end-of-semester performance from internal assessments, assignments, attendance, prior results and study behaviours; identify students who may need support; locate subject-level learning gaps; and present tailored study suggestions through a dashboard reviewed by instructors. The study has two components. Phase 1 reports descriptive findings from a questionnaire completed by 102 MCA students. Thirty-three respondents reported attendance below 75%, 40 reported studying no more than two hours per day outside college, and 78 of 101 respondents expressed interest in personalised study recommendations, particularly video lectures and practice questions. Exploratory Spearman analyses using previous-semester CGPA (97 usable records) found a weak positive association with internal assessment marks; this association remained below the Bonferroni-adjusted threshold. No clear association was observed for study hours, revision regularity or programming practice. Phase 2 is proposed, not completed: it specifies candidate machine-learning models, an evaluation plan and recommendation rules, but no model has been trained because final SGPA outcomes have not yet been collected.},
keywords = {student performance prediction; educational data mining; personalized learning; learning recommendations; at-risk students; explainable artificial intelligence; MCA},
month = {October},
doi = {https://doi.org/10.64388/IREV10I4-1724135}
}