Automatic assignment of reviewers in an online peer assessment task based on social interactions. Issue 4 (12th April 2019)

• Record Type: Journal Article
• Authors: Anaya, Antonio R.; Luque, Manuel; Letón, Emilio; Hernández‐del‐Olmo, Félix
• Other Names: Jiménez Alfredo guestEditor.; Herrero Álvaro guestEditor.
• Is Part Of: Expert systems. Volume 36:Issue 4(2019)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2019-04-12
• Subjects: e‐learning -- influence diagram -- network analysis -- social interactions; Expert systems (Computer science); 006.33
• DOI: 10.1111/exsy.12405
• Languages: English
• ISSNs: 0266-4720

Abstract

Abstract: Online peer assessment tasks are very popular and have unique characteristics that improve learning and encourage social interactions in a distance education environment. Unfortunately, social factors have usually been ignored in the process of selecting reviewers for online peer assessment tasks. We hypothesise that this fact could have some influence on the lack of engagement and participation by some learners. For this reason, we propose an approach in which social network analysis techniques, expert criteria, and Bayesian reasoning are applied to select reviewers with the objective of increasing participation in peer review tasks. The approach is divided into two elements. On the one hand, we have developed an influence diagram template that structures a set of proposed social network analysis variables in accordance with expert criteria. This influence diagram template can be easily updated for any course simply by eliciting a minimal set of parameters. On the other hand, we have instantiated the proposed influence diagram template to produce an influence diagram network to quantify the quality of reviewer assignment for an online peer assessment task. In an online experiment, we verified that the consideration of social factors can increase participation in a peer assessment task.