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- W4312843730 abstract "Most of the higher learning institutions are shifting to e-learning platforms as a practical medium to impart knowledge. Therefore, in line with the increasing use of e-learning, it is necessary to analyze students’ learning interactions and learning performances in such a platform by using effective educational data mining techniques. Premised in this context, this study aims to apply K-Means clustering to determine student’s learning interactions in a Virtual Learning Environment (VLE) based on their respective groups. In principle, such a technique is used to categorize data into appropriate clusters such that data with similar patterns are grouped in a same cluster. Also, an important step of K-Means clustering is to pre-define the number of clusters, k. In this study, two methods were used to determine k, namely the Elbow Method and Average Silhouette Method, which was revealed to be 2. In this study, the clustering results were implemented in the R Studio software, which were classified into two categories of students, namely active and inactive. Specifically, the clustering result with the percentage of 53.7% highlighted the compactness of clustering performed. It was also observed that students with active and more learning interactions tended to have greater learning performances as compared to those with less active and fewer learning interactions. Given this promising finding, teaching practitioners could use such a technique to analyze their student data to reveal the impact of students’ learning interactions in a VLE on students’ learning performances and to determine those with learning difficulties who may require appropriate guidance or counsel." @default.
- W4312843730 created "2023-01-05" @default.
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- W4312843730 date "2022-01-01" @default.
- W4312843730 modified "2023-09-25" @default.
- W4312843730 title "Mining e-learning interactions using K-Means clustering" @default.
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- W4312843730 doi "https://doi.org/10.1063/5.0104447" @default.
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