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

DOI

10.2991/icaiees-13.2013.22How to use a DOI?

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.

Copyright

© 2013, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Daqing Chen
AU  - Geoffrey Elliott
PY  - 2013/12
DA  - 2013/12
TI  - Determining Key (Predictor) Course Modules for Early Identification of Students At-Risk
BT  - Proceedings of the 2013 International Conference on Advanced Information Engineering and Education Science (ICAIEES 2013)
PB  - Atlantis Press
SP  - 74
EP  - 79
SN  - 1951-6851
UR  - https://doi.org/10.2991/icaiees-13.2013.22
DO  - 10.2991/icaiees-13.2013.22
ID  - Chen2013/12
ER  -