An exploration of automated grading of complex assignments

Abstract

Automated grading is essential for scaling up learning. In this paper, we conduct the first systematic study of how to automate grading of a complex assignment using a medical case assessment as a test case. We propose to solve this problem using a supervised learning approach and introduce three general complementary types of feature representations of such complex assignments for use in supervised learning. We first show with empirical experiments that it is feasible to automate grading of such assignments provided that the instructor can grade a number of examples. We further study how to integrate an automated grader with human grading and propose to frame the problem as learning to rank assignments to exploit pairwise preference judgments and use NDPM as a measure for evaluation of the accuracy of ranking. We then propose a sequential pairwise online active learning strategy to minimize the effort of human grading and optimize the collaboration of human graders and an automated grader. Experiment results show that this strategy is indeed effective and can substantially reduce human effort as compared with randomly sampling assignments for manual grading.