While Learning Analytics Dashboards (LADs) gain relevance in higher education for supporting student self-regulation, evaluation of such tools remain limited. This study examines the impact of adding course-level predictions to a program-completion LAD on students’ perceptions of course difficulty, readiness, workload, and anxiety. A controlled study with 39 undergraduate engineering students indicates that predictive features significantly reduces the perceived course difficulty when the predicted probability of passing is 50% or higher. Furthermore, there is evidence suggesting that predictions below 50% were associated with increased workload estimates, while those above 50% corresponded to reduced anxiety. This study contributes to the design of predictive analytics that support informed student decision making in course enrollment.