Prediction of Students’ Performances Using Course Analytics Data: A Case of Water Engineering Course at the University of South Australia
An association between students’ learn-online engagement and academic performance was investigated for a third-year Water Resources Systems Design course at the University of South Australia in 2017. As the patterns of data were non-parametric, Mann-Whitney and Kruskal-Wallis tests were pe...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-09-01
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| Series: | Education Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2227-7102/9/3/245 |
| Summary: | An association between students’ learn-online engagement and academic performance was investigated for a third-year Water Resources Systems Design course at the University of South Australia in 2017. As the patterns of data were non-parametric, Mann-Whitney and Kruskal-Wallis tests were performed using SPSS. It was revealed from the test results that distributions of students’ logins to learn-online site for all categories and sub-categories including gender, international/domestic students and grades were almost similar. Therefore, it is relatively unrealistic to use lean-online engagement data to predict students’ performances. A correlation test was further performed to validate the hypothesis testing results and a weak relationship (Pearson’s r = 0.29) between login to learn-online site and grade was observed. The smaller F ratios of one way ANOVA also validated the test results. Mann-Whitney and Kruskal-Wallis tests can be applied to course analytics data for face-to-face and online courses to understand a better picture about the uses of learn-online engagement data. |
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| ISSN: | 2227-7102 |