Conference Agenda

The Student workload is a growing concern in higher education, particularly in engineering, where increasing curricular demands may affect students’ self-regulation and time management. While Learning Analytics has proposed indicators derived from Learning Management System (LMS) data to characterize workload, these measures may not fully reflect how educational stakeholders understand and use such information. This work-in-progress study aims to identify student workload indicators in higher education and examine how they may support student self-regulation and instructional design. To this end, a workshop was conducted in Chile with 40 higher education managers and academics, organized into eight groups, using open and closed card sorting activities. In the open card sorting, participants generated and grouped indicators they considered relevant for supporting students. In the closed card sorting, LMS-based indicators were classified according to the moment of the academic period in which they were perceived as most useful. Preliminary findings show that time-related indicators were the most salient across groups, followed by personal, goal-oriented, activity-based, and perception indicators. Participants mainly associated system-based indicators with monitoring during the academic period, while comparative and perception-based indicators were also seen as useful for earlier decision-making stages. These results suggest that meaningful workload indicators should combine behavioral, contextual, and subjective dimensions.