A Framework for Designing Student-Facing Learning Analytics to Support Self-Regulated Learning

Student-facing learning analytics (SFLA) hold promise for supporting the development of self-regulated learning (SRL) skills students need for academic success, especially in online learning. However, the promise of SFLA for supporting SRL is unrealized because current SFLA design methods are technocentric, with little attention to learning science theory and student needs, and there is little guidance for SFLA designers. Based on insights from literature and a survey with learning analytics (LA) experts, we have developed a framework to guide the design of SFLA. The framework is built around three questions that designers should address to generate SFLA features for supporting SRL: What are the students’ self-regulated learning support needs based on self-regulated learning theory? What are the students’ perspectives of SFLA in meeting their SRL needs? What SFLA features are appropriate to support students’ self-regulated learning based on their SRL needs ? A set of activities that should be conducted to respond to each question is identified within the framework. To evaluate the framework, a focus group discussion with LA experts was conducted, and feedback obtained was used to revise the framework. The framework provides a rationale for understanding student needs as informed by theory.