Evaluation of student academic performance using e-learning with the association rules method and the importance of performance analysis

Abstract

Abstract. In managing the student study process, there is a pattern that occurs repeatedly every year. The recurring data will produce information in the form of student profiles, entry paths, student academic results, study period, average graduate-level and much other information as long as students take the process of teaching and learning activities. This research was conducted to predict student performance and e-learning satisfaction at the Computer Science department in Mulawarman University using the Association Rules method and Importance Performance Analysis. The sample in this study was 389 data of computer science graduate students. Based on the results of the research that has been done, it can be concluded that the graduation rate of computer science students of the Faculty of Computer Science and Information Technology, as follows; students who have graduated most have a GPA interval of 2.76 - 3.50 with male gender and take a study period of more than 6 years which has a support value of 0.321 and a confidence value of 0.628. Their perception of e-learning according to IPA coordinates community, collaboration, materials, social media, knowledge, synthesis, application, understanding, multimedia, evaluation, video, and news in quadrant II has a high level of importance with a relatively high level of performance and must be maintained.