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Decision Trees for Very Early Prediction of Student's Achievement

机译：非常早期地预测学生成绩的决策树

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The prediction of students' academic achievement is crucial to be conducted in a university for early detection of students at risk. This paper aims to present data mining models using classification methods based on Decision Trees (DT) algorithms to predict students' academic achievement after preparatory year, and to identify the algorithm that yields best performance. The students' academic achievement is defined as High, Average, or Below Average based on graduation CGPA. Three classifiers (J48, Random Tree and REPTree) are applied on a newly created dataset consisting of 339 students and 15 features, at the College of Computer Science and Information Technology (CCSIT). The outcome showed the J48 algorithm had an overall superior performance compared to other algorithms. Feature selection algorithms were used to reduce the feature vectors from 15 to 4 resulting in improvements in performance and computational efficiency. Finally, the results obtained help to pinpoint the preparatory year courses that impact graduation CGPA. Timely warnings, and preemptive counseling towards improving academic achievement is possible now.
机译：对学生学习成绩的预测对于在一所大学中进行，以及早发现处于危险之中的学生至关重要。本文旨在介绍使用基于决策树（DT）算法的分类方法进行数据挖掘的模型，以预测学生在预科一年后的学习成绩，并确定产生最佳性能的算法。根据毕业CGPA，将学生的学业成绩定义为“高”，“平均”或“低于平均”。计算机科学与信息技术学院（CCSIT）将三个分类器（J48，随机树和REPTree）应用于由339个学生和15个特征组成的新创建的数据集。结果表明，与其他算法相比，J48算法具有总体上优越的性能。使用特征选择算法将特征向量从15减少到4，从而提高了性能和计算效率。最后，获得的结果有助于查明影响毕业CGPA的预科课程。现在可以及时发出警告，并为提高学业成绩提供先发制人的咨询服务。

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