Analysis of Learning Patterns of Job Training Class Users on the Masadepan.ku Platform

Abstract

Masadepan.ku platform provides digital-based job training classes; however, the utilization of user data to enhance learning effectiveness remains suboptimal. This study aims to analyze user learning patterns based on completion rates, final scores, and participation across various fields of study. The methods used include pearson correlation analysis and data visualization through dashboards. The findings indicate no significant correlation between final scores and class completion time, with a correlation value of -0.0896. Fields of study with high participation and completion rates include social sciences, which have a participation rate of 99% and a completion rate of 97%, as well as entrepreneurship, with a participation rate of 93% and a completion rate of 95%, followed by religion, law, and administration. Fields of study with low participation and low completion rates include education and others. Additionally, fields of study that also exhibit low participation and low completion rates include technology and accounting. In terms of user satisfaction, the health category recorded the highest average rating of 4.96, indicating a very high level of satisfaction among participants. Meanwhile, the engineering and automotive category registered the lowest average rating of 4.76. Fields of study that require improvement based on low completion rates and low ratings include automation, with a completion rate of 85% and an average rating of 4.79, and tourism, with a completion rate of 83% and an average rating of 4.85, as well as the others, design, technology, and accounting categories.