A multi-peer assessment platform for programming language learning: considering group non-consensus and personal radicalness. Issue 8 (16th November 2016)

• Record Type: Journal Article
• Authors: Wang, Yanqing; Liang, Yaowen; Liu, Luning; Liu, Ying
• Is Part Of: Interactive learning environments. Volume 24:Issue 8(2016)
• Page Start: 2011
• Page End: 2031
• Publication Date: 2016-11-16
• Subjects: interactive learning environments -- peer code review -- multi-peer assessment -- programming languages learning -- learning communities -- cooperative learning; Educational technology -- United States -- Periodicals; 371.33
• DOI: 10.1080/10494820.2015.1073748
• Languages: English
• ISSNs: 1049-4820

Abstract

Abstract : Multi-peer assessment has often been used by teachers to reduce personal bias and make the assessment more reliable. This study reviews the design and development of multi-peer assessment systems that detect and solve two common issues in such systems: non-consensus among group members and personal radicalness in some assessments. A multi-peer assessment model is proposed to address these issues. The model captures roles, activities, and data structures in a typical multi-peer assessment setting that can be generalized to other scenarios. We implemented the model in a multi-peer code review system and conducted several empirical experiments in programming language classes. The studies showed that the model can significantly improve student learning outcomes than in a single-peer assessment. Also, we used statistical measures to detect non-consensus and radicalness issues that often occur in the model. The results reveal many insights and provide valuable guidance for teachers to implement a multi-peer assessment system.