Leveraging CPTs in a Bayesian Approach to Grade Open Ended Answers

Here we discuss a framework (OpenAnswer) providing support to the teacher's activity of grading answers to open ended questions. OpenAnswer implements a teacher mediated peer-evaluation approach: the marking results obtained from peer assessments are tuned by the grades explicitly assigned by the teacher, the teacher grades only a subset of the answers, suggested by the system. When a termination criterion is met, for the process managing the amount of teacher grading work, the remaining answers are automatically graded. A Bayesian Network is designed to represent the information related to students' models, peer assessments, and teacher's grading. The model parameters are many, here we report the results of investigations on a particularly tricky aspect of the framework, that is the modeling and optimization of the Conditional Probability Tables that are an important part of the Bayesian underlying model. In fact, they express the hypothesized relation between items of information that are relevant for evidence propagation through the network. Results suggest that this optimization improves OpenAnswer's performance, i.e. its capability to infer correct grades. We also show evidence of the influence of the teacher's assessing style on the grading process