Profiling students at risk of dropout at a university in South Africa
DOI:
https://doi.org/10.24085/jsaa.v10i2.4077Keywords:
Data mining, student dropout, weight of evidence, information value, risk profileAbstract
Background: Higher education authorities continue to be concerned about dropout rates among university students. Dropping out affects cost efficiency and tarnishes the reputation of the institution. As a result, profiling at risk student of dropout is crucial.
Purpose: To build a profile of students at risk of dropout using administrative university data.
Methods: The researcher employed a data mining technique in which predictors were chosen based on their weight of evidence (WOE) and information value (IV). The chosen predictors were then used to build a profile of students at risk of dropout.
Findings: According to the findings, the student is at risk if was born in the years 1931 to 1967 or 1994 to 2001; fails more than four modules in a year with a participation average mark of 43percent or less; and has joined the university in the second academic year.
Conclusions: This study concludes that matric scores had no bearing on the chance of a student dropping out for the cohorts of students who attended the institution between 2008 and 2018. However, the variables number of modules failed, participation average marks, entry level and year of birth had a bearing for building a profile for the students at risk of dropout for the cohorts of students who attended the institution between 2008 and 2018.
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