Active learning in computer science education using meta-cognition (abstract only)

Courses that involve problem solving provide an opportunity to incorporate meta-cognition as an active learning strategy, where students reflect on their confidence levels on their solutions to problems. As compared to other typically used active learning strategies, meta-cognition provides concrete and comprehensive feedback about the students' learning. The data about confidence levels is potentially useful to both the instructor and the student: an instructor can utilize the data about confidence levels as a second measure of student learning (this is in addition to the scores obtained); a student gets valuable feedback with regards to his/her own comprehension of the topics when he/she examines the confidence levels. We have incorporated meta-cognition techniques in four computer science courses over two semesters at University of Michigan, Flint, including CS1, and also graduate level courses. By analyzing the data obtained, we are able to infer conclusions about (a) How correlated are the scores obtained to the confidence levels reported (b) Is meta-cognition assisting in learning, and (c) Are students more satisfied with a course that incorporates meta-cognition.