|Appears in Collections:||Faculty of Social Sciences Journal Articles|
|Peer Review Status:||Refereed|
|Title:||Small data, online learning and assessment practices in higher education: a case study of failure?|
Thompson, Terrie Lynn
teaching excellence framework (TEF)
|Citation:||Watson C, Wilson A, Drew V & Thompson TL (2017) Small data, online learning and assessment practices in higher education: a case study of failure?. Assessment and Evaluation in Higher Education, 42 (7), pp. 1030-1045. https://doi.org/10.1080/02602938.2016.1223834|
|Abstract:||In this paper we present an in-depth case study of a single student who failed an online module which formed part of a masters programme in Professional Education and Leadership. We use this case study to examine assessment practices in higher education in the online environment. In taking this approach we go against the current predilection for Big Data which has given rise to ‘learning analytics’, a data-intensive approach to monitoring learning. In particular we draw attention to the model of the learner produced by learning analytics and to issues of ‘dataveillance’ in online learning. We also use the case to examine assessment in higher education more broadly, exploring the tensions between the requirements for certification and the need for learning. We conclude that assessment practices in higher education may have more to do with ‘quality assurance’ and regulatory frameworks than with ‘enhancing the student experience’ and inculcating the qualities that mark out higher education as an ethical project.|
|Rights:||This item has been embargoed for a period. During the embargo please use the Request a Copy feature at the foot of the Repository record to request a copy directly from the author. You can only request a copy if you wish to use this work for your own research or private study. This is an Accepted Manuscript of an article published by Taylor & Francis Group in Assessment & Evaluation in Higher Education on 25 Aug 2016, available online: http://www.tandfonline.com/10.1080/02602938.2016.1223834.|
|SmallDataFinalRevised.pdf||Fulltext - Accepted Version||471.41 kB||Adobe PDF||View/Open|
This item is protected by original copyright
Items in the Repository are protected by copyright, with all rights reserved, unless otherwise indicated.
If you believe that any material held in STORRE infringes copyright, please contact firstname.lastname@example.org providing details and we will remove the Work from public display in STORRE and investigate your claim.