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Prediction of Students’ Performances Using Course Analytics Data: A Case of Water Engineering Course at the University of South Australia

School of Natural and Built Environments, University of South Australia, Adelaide, SA 5095, Australia
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Educ. Sci. 2019, 9(3), 245; https://doi.org/10.3390/educsci9030245
Received: 29 August 2019 / Revised: 12 September 2019 / Accepted: 15 September 2019 / Published: 19 September 2019
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. View Full-Text
Keywords: course analytics; learn-online site login; students’ performances; statistical tests course analytics; learn-online site login; students’ performances; statistical tests
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Ahammed, F.; Smith, E. Prediction of Students’ Performances Using Course Analytics Data: A Case of Water Engineering Course at the University of South Australia. Educ. Sci. 2019, 9, 245.

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