USD Conference Systems, Seminar Nasional Sanata Dharma Berbagi 2022

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THE APPLICATION OF K-MEANS CLUSTERING ALGORITHM FOR INITIAL ANALYSIS OF STUDENTS ONLINE LEARNING
Paulina H. Prima Rosa, Johanes Eka Priyatma, Haris Sriwindono, Agnes Maria Polina

Last modified: 2022-11-09

Abstract


Even though it took place suddenly due to the Covid-19 pandemic, the online learning for 2 years had provided very valuable experiences. The success of students undergoing online learning is not solely determined by the availability and skills of using various information technology facilities such as Learning Management Systems, various video-based communication platforms, and various forms of social media. Online learning requires a variety of attitude changes compared to offline lectures. Using K-Means clustering algorithm towards 7000 students’ data, this paper discusses various factors that affect differences in student understanding and attitudes in participating in online learning. The results of the initial analysis using the Silhouette Score show that there are 2 clusters of students in understanding and having attitudes towards online learning. Based on the results of the box plot analysis of each data attribute, this paper identifies various factors that result to these 2 clusters. The results showed that there were differences between 2 groups of students in the technical level, namely technical matters regarding learning and LMS. However, from the paradigmatic level (conceptual and mental attitude) and the self managerial level, there is no significant difference. It can be concluded that if technical problems can be overcome, students will not be constrained in online learning.


Keywords


Covid-19, K-Means clustering, online learning

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