Scalable authentic assessment of collaborative work assignments in wikis

Abstract

Abstract Wikis are appropriate tools for deploying authentic assessment experiences for learning and work scenarios in which a group of users are asked to develop a shared task. However, when the number of wiki users increases, the number of contributions can grow at a pace whereby accurately assessing them becomes a complex and non-scalable task. While different quantitative approaches have been shown to be scalable, they are usually coarse-grained and provide limited feedback about the assessment. This work proposes a scalable assessment methodology for wiki-based tasks, based on qualitative self- and peer assessment of wiki contributions. The methodology is implemented using a software tool and is applied as part of an undergraduate course, complementing a quantitative assessment approach. Positive evidence on the scalability of the method and how it implements a more fine-grained qualitative assessment than the regular quantitative approach is found, providing indicators for assessing both individual and group generic skills.

Keywords

• Assessment
• Authentic learning
• Learning analytics
• Scalability
• Wiki