Examining of Learners’ Dashboard Interaction in Computer Classification Testing Environment

Technology-and analytics-enhanced self-assessments may provide multiple benefits for learners. However, data analytics approaches currently fail to make full use of educational technology and data for self-assessment. This research focuses on the implementation of an environment for self-assessment including a data-driven dashboard in the context of higher education and examines learners’ usage behaviors of N= 100 learners in three experimentally varied conditions. Findings indicate an intensive use of the self-assessments and data-driven dashboards throughout the semester. However, no differences in the interaction of learners with the different dashboard conditions were found. In conclusion, the design of data-driven dashboards for self-assessments requires valid information about learners, assessment processes, and the context of the assessment to better support the current needs of the learner and provide meaningful feedback to foster learning processes and outcomes.