Study on Dominant Factor for Academic Performance Prediction using Feature Selection Methods

Abstract

All educational institutions always try to investigate the learning behaviors of students and give early prediction toward student's outcomes for interventing and improving their learning performance. Educational data mining (EDM) offers various effective prediction models to predict student performance. Simultaneously, feature selection (FS) is a method of EDM that is utilized to determine the dominant factors that are needed and sufficient for the target concept. FS method extracts high-quality data that reduce the complexity of the prediction task that can increase the robustness of decision rule. In this paper, we provide a comparative study of feature selection methods for determining dominant factors that highly affect classification performance and improve the performance of prediction models. A new feature selection CHIMI based on ranked vector score is proposed. Analysis of feature sets of each FS method to get the dominant set is executed. The experimental results show that by using the dominant set of the proposed CHIMI method, the classification performance of the proposed models is significantly improved.

Supervised Learning through Classification Learner Techniques for the Predictive System of Personal and Social Attitudes of Engineering Students

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“…Within the two supervised learning techniques, is the classification, the classification algorithms look for patterns that will then allow them to classify the elements and determine which groups or classes they belong to. It should be mentioned that the values for these algorithms must be discrete values [27]. Among the classification algorithms is Kernel, which extends the regular logistic regression, used for binary classification, to deal with data that are not linearly separable [28].…”

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