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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM64210
Volume:
10 |
5 |
May 2026
Pages:
1-4
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Department. Of Computer Science Engineering
Adarsh Institute Of Technology And Research Centre, Vita, Maharashtra, India
Department. Of Computer Science Engineering
Adarsh Institute Of Technology And Research Centre, Vita, Maharashtra, India
Department. Of Computer Science Engineering
Adarsh Institute Of Technology And Research Centre, Vita, Maharashtra, India
Department. Of Computer Science Engineering
Adarsh Institute Of Technology And Research Centre, Vita, Maharashtra, India
Department. Of Computer Science Engineering
Adarsh Institute Of Technology And Research Centre, Vita, Maharashtra, India
Department. Of Computer Science Engineering
Adarsh Institute Of Technology And Research Centre, Vita, Maharashtra, India
ProLearn is an intelligent adaptive web-based learning and recommendation system developed to enhance personalized digital education. The primary objective of this study is to design and implement a system that analyzes learner performance and dynamically recommends targeted study resources based on individual strengths and weaknesses. The proposed system follows a client–server architecture using React.js for the frontend and Firebase for secure authentication and cloud data management. Learners attempt domain-specific multiple-choice assessments, and performance metrics such as accuracy and topic-wise scores are computed. A Decision Tree algorithm is employed to classify overall learner proficiency levels, while a Support Vector Machine (SVM) model identifies strong and weak topics. Based on classification outcomes, the recommendation module generates personalized learning content. Experimental evaluation demonstrates accurate learner classification and improved engagement through adaptive content delivery. The results indicate that integrating machine learning techniques within web-based educational platforms significantly enhances personalization, progress monitoring, and overall learning effectiveness.
KEYWORDS
This paper presented ProLearn, an intelligent web-based personalized learning and recommendation system designed to overcome the limitations of traditional e-learning platforms. By integrating machine learning techniques with web technologies, the system provides adaptive learning experiences based on individual learner performance. ProLearn supports early identification of learning gaps, targeted resource recommendation, and continuous progress tracking. The proposed system demonstrates the potential of intelligent learning platforms in improving learning efficiency and educational outcomes.
1. D. Pugh, A. De Champlain, M. J. Gierl, H. Lai and C. Touchie, “Can automated item generation be used to develop high quality MCQs that assess application of knowledge?”, Research and Practice in Technology Enhanced Learning, 2020. 2. Semere Kiros Bitew, Amir Hadifar, Lucas Sterckx, “Learning to Reuse Distractors to support Multiple Choice Question Generation in Education.”, arXiv, 2022 3. Aisha Mohammed Ahmed. “AI-Powered Learning Analytics Improving Personalized Education in E-Learning Environments.” International Journal of Academic Publishing in Educational Sciences and Humanities (IJAPESH), 2025 4. Y. Yang, Y. Zhong and M. Woźniak, “Improvement of Adaptive Learning Service Recommendation Algorithm Based on Big Data”, Mobile Networks and Applications, 2021 5. H. Lai, M. J. Gierl, “Using Automatic Item Generation to Improve the Quality of MCQ Distractors”, Teaching and Learning in Medicine, vol. 29, no. 4, pp. 1–10, 2017.
Publication Date
6/1/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
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