Analysis of Student Performance Based on LMS Activities with Learning Analytics Approach - Dawam Dwi Jatmiko Suwawi; Hafizh Jihaad Husni; Kusuma Ayu Laksitowening (JURIKOM (Jurnal Riset Komputer), 2021)
Good performance for a student during a course is important because it can affect the student's final index. However, in general, student performance can only be seen at the end of the semester, so if a student has a poor performance before the course ends, they do not get enough opportunity to improve. Therefore, it is necessary to have an early analysis of students who have poor performance in a course. Since most student learning activities in this pandemic era are currently on the LMS, the LMS activity log can reflect student performance. The main objective of this study is to classify the LMS activity log in a course using a probabilistic classifier algorithm. This study chose Naïve Bayes to classify student performance into three categories – good, satisfactory, and poor. The dataset is separated into two scenarios – the half-semester and the full-semester - in the Modeling and Database Implementation course at Telkom University. The results show that the Naïve Bayes Algorithm successfully predicts student performance early and provides information about students experiencing changes in performance with the highest accuracy of 93%. The practical implication of this study is that teachers can use the LMS activity log for early prediction of student success in passing a course. The learning analytics developed in this study prove that Naïve Bayes has a fairly good performance for small dataset sizes based on recall and accuracy to classify student performance. However, as the study focuses solely on a specific course and small dataset sizes, it lacks generalizability. Therefore, it needs to be tested for other courses and larger dataset sizes.