Assessing collaborative learning: big data, analytics and university futures

Williams, Peter

Abstract

Traditionally, assessment in higher education has focused on the performance of individual students. This focus has been a practical as well as an epistemic one: methods of assessment are constrained by the technology of the day, and in the past they required the completion by individuals under controlled conditions, of set-piece academic exercises. Recent advances in learning analytics, drawing upon vast sets of digitally-stored student activity data, open new practical and epistemic possibilities for assessment and carry the potential to transform higher education. It is becoming practicable to assess the individual and collective performance of team members working on complex projects that closely simulate the professional contexts that graduates will encounter. In addition to academic knowledge this authentic assessment can include a diverse range of personal qualities and dispositions that are key to the computer-supported cooperative working of professionals in the knowledge economy. This paper explores the implications of such opportunities for the purpose and practices of assessment in higher education, as universities adapt their institutional missions to address 21st Century needs. The paper concludes with a strong recommendation for university leaders to deploy analytics to support and evaluate the collaborative learning of students working in realistic contexts.

Citation

Williams, P. (2017). Assessing collaborative learning: big data, analytics and university futures. Assessment and Evaluation in Higher Education, 42(6), 978-989. https://doi.org/10.1080/02602938.2016.1216084

• Acceptance Date: Jul 20, 2016
• Online Publication Date: Jul 28, 2016
• Publication Date: Aug 18, 2017
• Deposit Date: Jul 26, 2016
• Publicly Available Date: Feb 1, 2018
• Journal: Assessment and evaluation in higher education
• Print ISSN: 0260-2938
• Electronic ISSN: 1469-297X
• Publisher: Routledge
• Peer Reviewed: Peer Reviewed
• Volume: 42
• Issue: 6
• Pages: 978-989
• DOI: https://doi.org/10.1080/02602938.2016.1216084
• Keywords: Social learning analytics; Situated learning; Collaborative learning; Authentic assessment
• Public URL: https://hull-repository.worktribe.com/output/441735
• Publisher URL: http://www.tandfonline.com/doi/full/10.1080/02602938.2016.1216084
• Additional Information: This is an Accepted Manuscript of an article published by Taylor & Francis in Assessment and evaluation in higher education on 28/07/2016, available online: http://www.tandfonline.com/10.1080/02602938.2016.1216084