Experimental Evaluation of Open Answer, a Bayesian Framework Modeling Peer Assessment

Abstract

The analysis of answers to open-ended questions provides greatly accurate assessment, being in turn demanding for the teacher. Here we show an approach exploiting peer assessment to partially relieve the teacher, and to provide information on the meta-cognitive ability of students of making correct evaluations on their peers. Open Answer handles a Bayesian model for each student, representing her/his learning state and judgment capability. The students' sub-networks are connected through peer-assessment. The process end up with a full set of grades for all students' answers, after the teacher had actually graded only part of them. We present experimental data and simulations aiming at identifying the best strategies to exploit the available information.