Ricerc@Sapienza

Peer assessment has increasingly proven its benefits for the learning process and several educational platforms have been proposed to support it. Rather than developing yet another standalone tool, in this paper we aim to integrate an existing Bayesian Network-based peer evaluation approach in a widely used learning management system, Moodle. This allows to capitalize both on the successful peer assessment model and on the broad range of educational functionalities provided by the learning management system.

Supporting peer assessment in learning management systems is an important educational issue. The widespread Moodle platform relies on a plugin called Workshop for providing such peer evaluation functionality. In a previous work, we proposed an extension of the plugin with student modeling capabilities, based on a Bayesian Network approach. In the current paper we aim to experimentally validate this Enhanced Workshop module, by using it in the context of an Introduction to Computer Programming course.

The contribution describes and problematizes the use of learning analytics within a blended university course based on a socio-constructivist approach and aimed at constructing artefacts and knowledge. Specifically, the authors focus on the assessment system adopted in the course, deliberately inspired by the principles of formative assessment: an ongoing assessment in the form of feedback shared with the students, and which integrates the teacher’s assessment with self-assessment and peer-assessment.