Identifying students at risk in academics Analysis of Korean language academic attrition at the Defense Language Institute Foreign Language Center
Abstract
Purpose – This paper aims to use a data-driven approach to identify the factors and metrics that provide
the best indicators of academic attrition in the Korean language program at the Defense Language Institute
Foreign Language Center.
Design methodology approach – This research develops logistic regression models to aid in the
identification of at-risk students in the Defense Language Institute’s Korean language school.
Findings – The results from this research demonstrates that this methodology can detect significant factors
and metrics that identify students at-risk. Additionally, this research shows that school policy changes can be
detected using logistic regression models and stepwise regression.
Originality value – This research represents a real-world application of logistic regression modeling
methods applied to the problem of identifying at-risk students for the purpose of academic intervention or
other negative outcomes. By using logistic regression, the authors are able to gain a greater understanding of
the problem and identify statistically significant predictors of student attrition that they believe can be
converted into meaningful policy change.
Description
The article of record as published may be found at http://dx.doi.org/10.1108/JDAL-05-2017-0008
Rights
This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.Collections
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