Implementation of Learning Analytics Indicators for Increasing Learners' Final Performance

This study aims to determine indicators that affect students' final performance in an online learning environment using predictive learning analytics in an ICT course and Turkey context. The study takes place within a large state university in an online computer literacy course (14 weeks in one semester) delivered to freshmen students (n = 1209). The researcher gathered data from Moodle engagement analytics (time spent in course, number of clicks, exam, content, discussion), assessment grades (pre-test for prior knowledge, final grade), and various scales (technical skills and "motivation and attitude" dimensions of the readiness, and self-regulated learning skills). Data analysis used multi regression and classification. Multiple regression showed that prior knowledge and technical skills predict the final performance in the context of the course (ICT 101). According to the best probability, the Decision Tree algorithm classified 67.8% of the high final performance based on learners' characteristics and Moodle engagement analytics. The high level of total system interactions of learners with low-level prior knowledge increases their probability of high performance (from 40.4 to 60.2%). This study discussed the course structure and learning design, appropriate actions to improve performance, and suggestions for future research based on the findings.