Can Student-Facing Analytics Improve Online Students' Effort and Success by Affecting How They Explain the Cause of Past Performance?

Clickstream data have been used increasingly to present students in online courses with analytics about their learning process to support self-regulation. Drawing on self-regulated learning theory and attribution theory, we hypothesize that providing students with analytics on their own effort along with the effort and performance of relevant peers will help students attribute their performance to factors under their control and thus positively influence their subsequent behavior and performance. To test the effect of the analytics and verify the proposed mechanism, we conducted an experiment in an online undergraduate course in which students were randomly assigned to receive theoretically inert questions (control condition), attribution questions (active control condition), and the analytics with attribution questions (treatment condition). The intervention significantly increased effort attribution, reduced ability attribution, and improved subsequent effort for a subgroup of students who self-reported low performance, although there was no significant impact on their performance.