EDUCATIONAL DATA MINING IN CLUSTERIZATION WITH DATA ON STUDENT HABITS IN ONLINE LEARNING

UNSPECIFIED EDUCATIONAL DATA MINING IN CLUSTERIZATION WITH DATA ON STUDENT HABITS IN ONLINE LEARNING.

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Abstract

As technology develops in the world, technology also develops in various sectors such as agriculture and government. The education sector is no exception. One of the technological developments in the education sector is the application of Blended Learning (BL). BL is a learning method that combines face-to-face meetings with online material in a harmonious and mutually integrated manner. There are 5 main keys in implementing this BL learning, namely live events, self-paced learning, collaboration, assessment, and performance support materials. To identify what BL actually looks like, we can use a data-driven approach. One of the clustering approaches of Educational Data Mining (EDM) is the latent class analysis method. With this method, we can extract common activity features from hundreds of courses at universities by using a dataset of student behavior in these online classes. For that, the authors will divide the existing types into 4 clusters. With the existing cluster division, decision makers at the university can take actions in accordance with the data generated in this paper.

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