Series:

Proceedings of the 2013 International Conference on Advanced Information Engineering and Education Science (ICAIEES 2013)

Determining Key (Predictor) Course Modules for Early Identification of Students At-Risk

Authors
Daqing Chen, Geoffrey Elliott
Corresponding author
Chen
Keywords
Educational data mining, Student retention, Decision tree induction, Key performance indicator
Abstract
This paper addresses the problem of early identification of at-risk students, and seeks to determine modules on a given course, referred to as predictor modules, in which a student’s performance is implicitly correlated to the end-of-the-first-year performance of the student. Such predictor modules may therefore be used to predict the likelihood of a student’s year progression. A data mining project has been conducted for this study, and decision tree-based predictive models have been created using various historical records of students’ grades and year progressions. The study reveals that a key predictor module exists, and the pass rate of the key predictor module can be used to predict students’ year progression rate. A set of recommendations is given based on the key predictor module identified from the management point of view in relation to improving student retention. The study also suggests that a students’ performance in a key predictor module can be directly linked to both key performance indicator and key result indicator in course management and student support.
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