Investigating prompts for supporting students' self-regulation – A remaining challenge for learning analytics approaches?

To perform successfully in higher education learners are considered to engage in self-regulation. Prompts in digital learning environments aim at activating self-regulation strategies that learners know but do not spontaneously show. To investigate such interventions learning analytics approaches can be applied. This quasi-experimental study (N = 110) investigates whether different prompts based on theory of self-regulated learning (e.g., cognitive, metacognitive, motivational) impact declarative knowledge and transfer, perceptions as well as online learning behavior, and whether trace data can inform learning performance. Findings indicate small effects of prompts supporting the performance in a declarative knowledge and transfer test. In addition, the prompted groups showed different online learning behavior than the control group. However, trace data in this study were not capable of sufficiently explaining learning performance in a transfer test. Future research is required to investigate adaptive prompts using trace data in authentic learning settings as well as focusing on learners' reactions to distinct prompts. •Utilizing prompts to enhance self-regulated learning.•Investigating the power of trace data for predicting learning performance.•Prompts need to be adaptive to better support self-regulated learning.