文章基本信息

标题：Predicting Student Success Using Data Generated in Traditional Educational Environments
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作者：Marian Bucos ; Bogdan Drăgulescu
期刊名称：TEM Journal
印刷版ISSN：2217-8309
电子版ISSN：2217-8333
出版年度：2018
卷号：7
期号：3
页码：617-625
DOI：10.18421/TEM73-19
语种：English
出版社：UIKTEN
摘要：Educational Data Mining (EDM) techniques offer unique opportunities to discover knowledge from data generated in educational environments. These techniques can assist tutors and researchers to predict future trends and behavior of students. This study examines the possibility of only using traditional,already available,course report data, generated over years by tutors,to apply EDM techniques. Based on five algorithms and two crossvalidation methods we developed and evaluated five classification models in our experiments to identify the one with the best performance. A time segmentation approach and specific course performance attributes, collected in a classical manner from course reports, were used to determine students' performance. The models developed in this study can be used early in identifying students at risk and allow tutors to improve the academic performance of the students. By following the steps described in this paper other practitioners can revive their old data and use it to gain insight for their classes in the next academic year.
关键词：Classification;Educational Data Mining;predicting student performance;traditional educational environments.