The Open UniversitySkip to content
 

Investigating Learners’ Views of Assessment Types in Massive Open Online Courses (MOOCs)

Papathoma, Tina; Blake, Canan; Clow, Doug and Scanlon, Eileen (2015). Investigating Learners’ Views of Assessment Types in Massive Open Online Courses (MOOCs). In: Design for Teaching and Learning in a Networked World, Springer, pp. 617–621.

Full text available as:
[img]
Preview
PDF (Accepted Manuscript) - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
Download (278kB) | Preview
DOI (Digital Object Identifier) Link: https://doi.org/10.1007/978-3-319-24258-3_72
Google Scholar: Look up in Google Scholar

Abstract

Massive Open Online Courses (MOOCs) are changing the contours of the teaching and learning landscape. Assessment covers an important part of this landscape and may be a key driver for learning. This paper presents preliminary results of a qualitative study that investigated learners’ views on assessment types within a MOOC. A thematic analysis of learners’ interactions in a MOOC Facebook Group and twelve online interviews of learners in the same MOOC reveal that participants identify benefits in peer assessment but they prefer automated assessment as an already-known type. Self-assessment was not preferred by these learners. They reported that clear guidance assists them to carry out peer assessment more effectively. Some learners favored the combination of assessment types, as each of them serves a different purpose for their learning. The learners’ socio-cultural context emerged as a theme affecting both their learning and assessment activities and will be considered for future research.

Item Type: Conference or Workshop Item
ISSN: 0302-9743
Keywords: Assessment; MOOCs; Learners’ views; Peer assessment; Self-assessment; Automated assessment
Academic Unit/School: Learning Teaching and Innovation (LTI) > Institute of Educational Technology (IET)
Learning Teaching and Innovation (LTI)
Interdisciplinary Research Centre: Centre for Research in Education and Educational Technology (CREET)
Item ID: 47287
Depositing User: Tina Papathoma
Date Deposited: 26 Sep 2016 15:31
Last Modified: 08 Feb 2017 19:33
URI: http://oro.open.ac.uk/id/eprint/47287
Share this page:

Altmetrics

Download history for this item

These details should be considered as only a guide to the number of downloads performed manually. Algorithmic methods have been applied in an attempt to remove automated downloads from the displayed statistics but no guarantee can be made as to the accuracy of the figures.

Actions (login may be required)

Policies | Disclaimer

© The Open University   contact the OU