Gardner, John ORCID: 0000-0002-3844-7305, O'Leary, Michael ORCID: 0000-0002-6771-904X and Yuan, Li ORCID: 0000-0002-7144-9441 (2021) Artificial intelligence in educational assessment: ‘Breakthrough? Or buncombe and ballyhoo?’. Journal of Computer Assisted Learning, 37 (5). pp. 1207-1216. ISSN 0266-4909
Abstract
Artificial Intelligence is at the heart of modern society with computers now capable of making process decisions in many spheres of human activity. In education, there has been intensive growth in systems that make formal and informal learning an anytime, anywhere activity for billions of people through online open educational resources and massive online open courses. Moreover, new developments in Artificial Intelligence-related educational assessment are attracting increasing interest as means of improving assessment efficacy and validity, with much attention focusing on the analysis of the large volumes of process data being captured from digital assessment contexts. In evaluating the state of play of Artificial Intelligence in formative and summative educational assessment, this paper offers a critical perspective on the two core applications: automated essay scoring systems and computerized adaptive tests, along with the Big Data analysis approaches to machine learning that underpin them.
Metadata
Item Type: | Article (Published) |
---|---|
Refereed: | Yes |
Subjects: | Social Sciences > Education Social Sciences > Educational technology |
DCU Faculties and Centres: | DCU Faculties and Schools > Institute of Education > School of Policy & Practice Research Initiatives and Centres > Centre for Assessment Research, Policy and Practice in Education (CARPE) |
Publisher: | John Wiley & Sons Ltd |
Official URL: | https://doi.org/10.1111/jcal.12577 |
Copyright Information: | © 2021 The Authors |
Funders: | Centre for Assessment Research, Policy and Practice in Education (CARPE) is supported by a grant from Prometric Inc. |
ID Code: | 27777 |
Deposited On: | 22 Sep 2022 15:23 by Thomas Murtagh . Last Modified 22 Sep 2022 15:23 |
Documents
Full text available as:
Preview |
PDF
- Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
942kB |
Downloads
Downloads
Downloads per month over past year
Archive Staff Only: edit this record