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

Conference paper

Chen, D and Elliott, G (2013). Determining Key (Predictor) Modules for Early Identification of Students At-Risk. International Conference on Advanced Information Engineering and Education Science (ICAIEES 2013). Beijing, China 19 - 20 Dec 2013 Atlantis Press. https://doi.org/10.2991/icaiees-13.2013.22
AuthorsChen, D and Elliott, G
TypeConference paper
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 treebased 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.

KeywordsEducational data mining; Student retention; Decision tree induction; Key performance indicator
Year2013
PublisherAtlantis Press
Digital Object Identifier (DOI)https://doi.org/10.2991/icaiees-13.2013.22
Accepted author manuscript
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Open
Publication dates
Print19 Dec 2013
Publication process dates
Deposited05 Dec 2017
Accepted13 Sep 2013
ISBN978-90-78677-94-9
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https://openresearch.lsbu.ac.uk/item/878q0

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