article · Applied Computational Intelligence and Soft Computing
This research explores the use of machine learning algorithms to predict student academic performance, a critical aspect for higher education institutions, especially in e-learning environments. The study first employed K-means clustering, using Davies' Bouldin method, to identify key features influencing student outcomes. Subsequently, it evaluated the predictive capabilities of Support Vector Machine (SVM), Decision Tree, Naive Bayes, and KNN classifiers. After parameter adjustment, the SVM algorithm demonstrated the highest prediction accuracy at 96%. The findings indicate that parameter tuning significantly improved the accuracy of all tested models, while the Naive Bayes model showed the lowest accuracy due to its assumption of feature independence.
Predicting student performance allows educational institutions to proactively identify students who may need support, tailor teaching methods, and improve overall academic success. This is particularly relevant for the growing field of e-learning, where direct interaction can be limited, helping to enhance the quality of online education.
This research demonstrates the potential for machine learning models to predict student academic performance with high accuracy. Such models could be integrated into existing e-learning platforms, intelligent tutoring systems, or learning management systems to develop adaptive learning paths, automatic grading, or early warning systems for students at risk. This represents applied research with clear pathways for development within educational technology.
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Education is crucial for a productive life and providing necessary resources. With the advent of technology like artificial intelligence, higher education institutions are incorporating technology into traditional teaching methods. Predicting academic success has gained interest in education as a strong academic record improves a university’s ranking and increases student employment opportunities. Modern learning institutions face challenges in analyzing performance, providing high‐quality education, formulating strategies for evaluating students’ performance, and identifying future needs. E‐learning is a rapidly growing and advanced form of education, where students enroll in online courses. Platforms like Intelligent Tutoring Systems (ITS), learning management systems (LMS), and massive open online courses (MOOC) use educational data mining (EDM) to develop automatic grading systems, recommenders, and adaptative systems. However, e‐learning is still considered a challenging learning environment due to the lack of direct interaction between students and course instructors. Machine learning (ML) is used in developing adaptive intelligent systems that can perform complex tasks beyond human abilities. Some areas of applications of ML algorithms include cluster analysis, pattern recognition, image processing, natural language processing, and medical diagnostics. In this research work, K‐means, a clustering data mining technique using Davies’ Bouldin method, obtains clusters to find important features affecting students’ performance. The study found that the SVM algorithm had the best prediction results after parameter adjustment, with a 96% accuracy rate. In this paper, the researchers have examined the functions of the Support Vector Machine, Decision Tree, naive Bayes, and KNN classifiers. The outcomes of parameter adjustment greatly increased the accuracy of the four prediction models. Naïve Bayes model’s prediction accuracy is the lowest when compared to other prediction methods, as it assumes a strong independent relationship between features.
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DOI: 10.1155/2024/4067721
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