Volume 15, Issue 5

COREGAP: Concept Gap Detection System for Personalized Learning

Author

Dr. K. Prem Kumar¹, Shaik Noureesh²*, Tanisha Gundu³, Suniganti Venkateshwar Reddy⁴, Satai Vishal Kumar⁵

Abstract

Traditional educational systems primarily evaluate students based on examination scores, which often fail to measure actual conceptual understanding. Many students rely on memorization techniques rather than developing a strong foundation of concepts, leading to poor understanding of prerequisite topics and difficulties in advanced learning stages. Existing evaluation methods provide limited personalized feedback and are unable to identify specific conceptual weaknesses effectively. This paper proposes COREGAP, a Concept Gap Detection System designed to analyze student understanding at the concept level using diagnostic assessments, concept mapping, and prerequisite relationship analysis. The proposed system integrates machine learning techniques, rule-based analysis, and performance tracking to identify weak concepts, missing prerequisite knowledge, and learning patterns in real time. Based on the analysis, the system generates personalized feedback and adaptive learning recommendations to improve conceptual clarity and learning efficiency. Experimental observations indicate that the proposed approach provides deeper insights into student learning behavior compared to traditional evaluation methods. The system can play a significant role in intelligent educational platforms and personalized learning environments.

Keywords: Concept Gap Detection, Personalized Learning, Machine Learning, Educational Technology, Diagnostic Systems, Concept Mapping

DOI Link: https://doi.org/10.62226/ijarst20262689

Google Scholar: https://scholar.google.com/COREGAP-Concept-Gap-Detection-System-for-Personalized-Learning

Europub: https://europub.co.uk/articles/789985

Indexcopernicus :https://journals.indexcopernicus.com/search/article?articleId=4879346

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DOI

https://doi.org/10.62226/ijarst20262689

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