Abstract
Predicting students’ performance is one of the most concerned issues in education data mining (EDM), which has received more and more attentions. Feature selection is the key step to build prediction model of students’ performance, which can improve the accuracy of prediction and help to identify factors that have significant impact on students’ performance. In this paper, a hybrid feature selection method named rank and heuristic (RnkHEU) was proposed. This novel feature selection method generates the set of candidate features by scoring and ranking firstly and then uses heuristic method to generate the final results. The experimental results show that the four major evaluation criteria have similar performance in predicting students’ performance, and the heuristic search strategy can significantly improve the accuracy of prediction compared with forward search method. Because the proposed RnkHEU integrates ranking-based forward and heuristic search, it can further improve the accuracy of predicting students’ performance with commonly used classifiers about 10% and improve the precision of predicting students’ academic failure by up to 45%.
Highlights
Intelligent and personalized education has developed rapidly with the development of big data, artificial intelligence, Internet of things (IoT), and other new generations of information technologies in recent year [1], which is one of the most important directions of sustainable development of education
We proposed a hybrid feature selection method named rank and heuristic (RnkHEU) to improve the accuracy of predicting students’ performance. is novel feature selection method generates the set of candidate features by ranking and Naive Bayes (NB) classifier firstly and uses heuristic method to generate the final results of feature selection
We have conducted an empirical study on the performance of the feature selection methods using different evaluation criteria and search strategies in the performance prediction of students and proposed a hybrid feature selection method named RnkHEU which improves the accuracy of prediction. rough experiments, firstly, we find that evaluation criteria based on dependence, distance, information metric, and consistence all work well in predicting students’ performance
Summary
Intelligent and personalized education has developed rapidly with the development of big data, artificial intelligence, Internet of things (IoT), and other new generations of information technologies in recent year [1], which is one of the most important directions of sustainable development of education. Researchers train the prediction model based on supervised learning classification algorithm using labeled students’ historical academic data. In order to improve the accuracy of predicting students’ performance, researchers have used many feature selection methods in previous studies. E results of these researches show that feature selection methods can effectively improve the accuracy of predicting students’ performance, but there is no comparative analysis of different feature selection methods used in this field. We proposed a hybrid feature selection method named RnkHEU to improve the accuracy of predicting students’ performance. (3) We propose a hybrid feature selection method named RnkHEU, which can further improve the accuracy of predicting students’ performance.
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