An Easy-to-read Visual Approach to Deal with Peer Reviews and Self-assessments in Virtual Learning Environments

Abstract

In teaching-learning process, the grade computation is an important part of the critical-thinking process. When faced to assessments of many students, teachers are confronted to a mass of smart data. Lately, the strategies of peer evaluations, in which the students express a critic on different understanding of the taught contents, have been substantially developed in the literature. This paper presents an easy-to-read visual approach integrated with a learning platform to analyze correlations and distances between the grades given by a teacher and the grades produced by students through self-assessments and peer reviews. We observed that the both aforementioned metrics can rank differently the students; hence we propose to aggregate them by producing a single rank. Thus, our framework supports the lecturer either by selecting possible candidates for future class monitors or by focusing on students who need to be more involved in the teaching-learning process. Finally, for the highest ranks, our framework offers a regression model to predict the teachers' grades values only based on the students feedbacks.