Model Machine Learning untuk Memprediksi Kesuksesan Mahasiswa dalam Pembelajaran Daring

The model is based on students' activities data in learning management systems (LMS) i.e., the number of accessing materials, student participation in discussion forum, and assignment scores. Data are collected from 129 Universitas Terbuka's students who attend a course. Recently, machine learning in Learning Analytics (LA) has been implemented to measure student success in a course, including in online learning. By using student activity data, machine learning can be used to help lecturers to predict and classify student success based on their performance during the learning process. The ability to predict student success is beneficial as early warning for lecturers as well as for the students. In this research, a machine learning model based on Fuzzy KNN method is used to predict student success. The development of the model is based on students' activities data in learning management systems (LMS) i.e., the number of accessing materials, student participation in discussion forum, and assignment scores. Data are collected from 129 Universitas Terbuka's students who attend a course. Those data are used for data training and data testing to build the model. In order to improve the result and to overcome the imbalanced data, the SMOTE method is applied. The model performance is measured based on accuracy, recall, precision, and F1-score with various numbers of neighbors in Fuzzy KNN.

Keywords: Learning Analytic, Machine Learning, Learning Management Systems, ICE Institute, Learning Analytic, Machine Learning, Fuzzy KNN, Student Success

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ICE Institute telah melakukan penelitian kolaborasi dengan Universitas Terbuka (UT) dan Universitas Indonesia (UI) dengan judul:

Model Machine Learning untuk Memprediksi Kesuksesan Mahasiswa dalam Pembelajaran Daring

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