Determining Appropriate Classification Method Based on Influential Factors for Predicting Students’Academic Success

Dafid, Ermatita
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Abstract

The need for accuracy in a prediction is a non-negotiable thing. One of the determinants of the accuracy of a prediction model is the classification method. Data mining offers various classification methods for predicting. Therefore, determining appropriate classification methods that produce high accuracy prediction model is a must. Several previous studies have shown excellent results based on influential factors for predicting students’ academic success. However, the research only focuses on one influential factor category rather than a combination of multiple influential factor categories. It becomes a serious issue since there are influential factors on the dataset that not only have one influential factor category but mostly multiple factor categories. Therefore, the best classification method for a multiple influential factor category has not been known yet. This research analyzes the performance of classification methods based on multiple categories of influential factors. The result will help the researcher find the best combination of factor category and classification method should they used. Among multiple factor category and classification methods have been tested show combination of certain classification method give the best result for certain multiple factor category.
基于影响因素的学生学业成功预测分类方法的确定
对预测准确性的要求是不容置疑的。预测模型准确性的决定因素之一是分类方法。数据挖掘为预测提供了各种分类方法。因此,确定合适的分类方法,产生高精度的预测模型是必须的。先前的几项研究已经显示出基于影响因素预测学生学业成功的出色结果。然而,研究只关注一个影响因素类别,而不是多个影响因素类别的组合。由于数据集上的影响因素不仅有一个影响因素类别,而且大多数是多个影响因素类别,因此这成为一个严重的问题。因此,对于多影响因素类别的最佳分类方法尚未可知。本研究分析了基于多类影响因素的分类方法的性能。研究结果将有助于研究人员找到最佳的因素类别组合和分类方法。其中对多因素分类和分类方法进行了试验,表明某一分类方法组合对某一多因素分类效果最好。
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