A multi-peer assessment platform for programming language learning: considering group non-consensus and personal radicalness |
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Authors: | Yanqing Wang Yaowen Liang Ying Liu |
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Institution: | 1. School of Management, Harbin Institute of Technology, Harbin, People's Republic of China;2. Department of Information Systems, College of Business Administration, California State University Long Beach, Long Beach, CA, USA |
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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. |
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Keywords: | interactive learning environments peer code review multi-peer assessment programming languages learning learning communities cooperative learning |
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