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A Model to Predict Low Academic Performance at a Specific Enrollment Using Data Mining

机译：使用数据挖掘来预测特定入学时学习成绩低下的模型

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This paper presents the results of applying an educational data mining approach to model academic attrition (loss of academic status) at the Universidad Nacional de Colombia. Two data mining models were defined to analyze the academic and nonacademic data; the models use two classification techniques, naïve Bayes and a decision tree classifier, in order to acquire a better understanding of the attrition during the first enrollments and to assess the quality of the data for the classification task, which can be understood as the prediction of the loss of academic status due to low academic performance. The models aim to predict the attrition in the student’s first four enrollments. First, considering any of these periods, and then, at a specific enrollment. Historical academic records and data from the admission process were used to train the models, which were evaluated using cross-validation and previously unseen records from a full academic period. Experimental results show that the prediction of the loss of academic status is improved when the academic data are added.
机译：本文介绍了应用教育数据挖掘方法对哥伦比亚国立大学的学术减员（学术地位下降）进行建模的结果。定义了两种数据挖掘模型来分析学术和非学术数据。该模型使用两种分类技术，即朴素贝叶斯和决策树分类器，以更好地了解首次入学时的损耗并评估分类任务的数据质量，这可以理解为对分类的预测。由于学习成绩低下而导致的学术地位丧失。这些模型旨在预测学生的前四个招生中的人员流失。首先，考虑所有这些时期，然后再考虑特定的入学时间。使用历史学历记录和录取过程中的数据来训练模型，并使用交叉验证和整个学期以前未见过的记录对模型进行评估。实验结果表明，添加学术数据后，对学业丧失的预测得到了改善。

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