Improving Mathematical Learning Outcomes Through Automatic Reassessment and Relearning

• In Event: Poster Session 11
  In Poster Session: Assessment and Teacher Knowledge in Math

Abstract

The current study used a randomized controlled experiment to investigate the extent to which automatically reassessing student knowledge and providing relearning opportunities improved long-term retention of mathematical knowledge. Over the course of the 2010-2011 school year, 128 8th grade students achieved “mastery” on two skill sets. After mastery, students received reassessment and relearning opportunities on one skill set, and on the other set they did not. Results revealed that students perform substantially better on skills that were reassessed and relearned than skills that were not. Furthermore, when skills received reassessment and relearning, students were able to maintain mastery performance levels, whereas skills that did not receive such treatment showed a decrease in performance levels. The educational implications will be discussed.

Authors

• Neil T. Heffernan, Worcester Polytechnic Institute

• Cristina L. Heffernan, University of Pittsburgh

• Kevin Dietz, University of Illinois at Chicago

• Deena Soffer Goldstein, University of Illinois at Chicago

• James W. Pellegrino, University of Illinois at Chicago

• Susan R. Goldman, University of Illinois at Chicago

• Matthew Dailey, Worcester Polytechnic Institute