Student Retention in Fully Online Programmes at Two South African Universities
Kimera Moodley, Mari van Wyk, Donald Tive, Izak van Zyl, Johannes Cronjé
Abstract
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Kimera Moodley, Mari van Wyk, Donald Tive, Izak van Zyl, Johannes Cronjé
Abstract
Open-access reader
Literature indicates that low retention rates are evident in online modules and programmes. However, a much higher student retention rate than anticipated was observed in two fully online programmes at two South African universities. To explore this retention phenomena, a non-experimental quantitative study was done. The study uses descriptive statistics to identify trends and patterns in student retention and applies non-parametric statistics to test the significance of the observed patterns. Typically, students in a fully online carousel model programme drop out after the first module, specifically in the first two weeks of the module. After the third module, student retention stabilises. Similar patterns are visible in the year model, where students typically drop out within the first three months of their first year. Dropout continues in the second year, resulting in two distinct linear phases of dropout. However, in year 3, approximately 50% of the retained students continued. Findings reveal critical dropout periods. Further studies can be conducted to determine the causes of dropout so that measures can be implemented to improve student retention in higher education.
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Literature indicates that low retention rates are evident in online modules and programmes. However, a much higher student retention rate than anticipated was observed in two fully online programmes at two South African universities. To explore this retention phenomena, a non-experimental quantitative study was done. The study uses descriptive statistics to identify trends and patterns in student retention and applies non-parametric statistics to test the significance of the observed patterns. Typically, students in a fully online carousel model programme drop out after the first module, specifically in the first two weeks of the module. After the third module, student retention stabilises. Similar patterns are visible in the year model, where students typically drop out within the first three months of their first year. Dropout continues in the second year, resulting in two distinct linear phases of dropout. However, in year 3, approximately 50% of the retained students continued. Findings reveal critical dropout periods. Further studies can be conducted to determine the causes of dropout so that measures can be implemented to improve student retention in higher education.
Key concepts: Dropout (neural networks), Drop out, Retention rate, Descriptive statistics, Psychology, Mathematics education, Online learning, Computer science