Automated Assessment of Students' Attitudes and Academic Resilience Through Learning Management System Data Integration

One of the online platforms used to support learning activities is the Learning Management System (LMS). LMS supports the evaluation of cognitive aspects but not affective ones. As learning is mostly asynchronous, it presents challenges for lecturers in assessing students' attitudes. However, LMS generates data that can potentially be used to evaluate student attitudes and academic resilience. With the system log on the LMS, we can process data into information on the level of understanding, learning attitude, and persistence of students. This research is related to the design of an analytic dashboard that is integrated with LMS using Moodle. The dashboard will display an evaluation of student learning activities from the attitudinal aspects of motivation, discipline, responsibility, and academic resilience aspects consisting of persistence, reflecting and asking for help, and negative affect and emotional response. The study involved 160 new students, with data collected from 130 participants. The method used is a Research and Development (R&D) approach, which includes three stages: introduction, development, and testing. Data triangulation was used for validation by comparing online assessments with student self-evaluations. The final stage visualizes the results of system log analysis through the analytic dashboard. Findings showed that motivation scores from self-assessment correlated with LMS data, while discipline and responsibility assessments yielded different results. Academic resilience measures also showed discrepancies between student and mentor assessments. This research highlights the potential to visualize affective learning aspects within LMS platforms, providing lecturers with valuable insights into both cognitive and affective dimensions of student performance.