Abstract
Educational data mining (EDM) has enhanced one of the essential fields nowadays because, with technology improvement, students’ difficulties are also expanding. To grab these difficulties and encourage students, educational data mining has come into continuation. Educational data mining is the process to evaluate the student’s academic performance. Learning analytics apply machine learning techniques to have better and accurate interpretation out of it. Several researchers used it as a prediction system to predict student performance. This article focuses on various techniques used in EDM for classification and analysing of this data to build strong recommendation system. Numerous machine learning algorithm has used in different existing systems and predicts the classification accordingly. Moreover, it also analysed the challenges identified when EDM deals with large datasets. In this discussion, we evaluate the few algorithms and tried to propose new methodology to generate better classification and recommendation.
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Toradmal, M.B., Mehta, M., Mehendale, S. (2023). Machine Learning Approaches for Educational Data Mining. In: Suma, V., Lorenz, P., Baig, Z. (eds) Inventive Systems and Control. Lecture Notes in Networks and Systems, vol 672. Springer, Singapore. https://doi.org/10.1007/978-981-99-1624-5_55
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DOI: https://doi.org/10.1007/978-981-99-1624-5_55
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