Data-driven generation of rubric criteria from an educational programming environment
Nicholas Diana, Michael John Eagle, John C. Stamper, Shuchi Grover, Marie A. Bienkowski, Satabdi Basu · 2018
We demonstrate that, by using a small set of hand-graded student work, we can automatically generate rubric criteria with a high degree of validity, and that a predictive model incorporating these rubric criteria is more accurate than a previously reported model. We present this method as one approach to addressing the often challenging problem of grading assignments in programming environments. A classic solution is creating unit-tests that the student-generated program must pass, but the rigid, structured nature of unit-tests is suboptimal for assessing the more open-ended assignments students encounter in introductory programming environments like Alice. Furthermore, the creation of unit-tests requires predicting the various ways a student might correctly solve a problem - a challenging and time-intensive process. The current study proposes an alternative, semi-automated method for generating rubric criteria using low-level data from the Alice programming environment.