Projects per year
Abstract
Background: Online learning has grown significantly during the past two decades, and COVID-19 pandemic has expedited this process. However, previous research has shown how academic dishonesty is more prevalent under these modalities. Therefore, there is the challenge of performing trustworthy remote assessments, in order to obtain valid and reliable measures of students' knowledge. Objectives: The research question that drove this research was: what actions have been proposed in contemporary research to improve remote assessment trustworthiness from a technological and pedagogical perspective?. Methods: We analysed the papers accepted for the special issue titled ‘Trustworthy Assessment and Academic Integrity in Remote Learning’ following a deductive qualitative category coding methodology to find the main approaches. Results and conclusions: We identified eight approaches to improve trustworthiness in remote assessment: four for exams and high-stake tests, one exclusively for performance-based assessments, and three for any type of assessment. Our findings shift attention from academic dishonesty to trustworthy assessment, integrating recent findings of papers accepted to this special issue. Implications: Our findings deepen current understanding of trustworthy remote assessments, inviting practitioners and researchers to explore different types of assessment methods and different moments related to assessing learning.
Original language | English |
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Pages (from-to) | 1507-1520 |
Number of pages | 14 |
Journal | Journal of Computer Assisted Learning |
Volume | 38 |
Issue number | 6 |
DOIs | |
Publication status | Published - Dec 2022 |
Keywords
- academic dishonesty
- remote learning
- technology-enhanced learning
- trustworthy assessment
- typology
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Data analytics-based tools and methods to enhance self-regulated learning
Gasevic, D., Dawson, S., Sheard, J., Mirriahi, N., Martinez-Maldonado, R., Khosravi, H., Chen, G. & Winne, P. H.
1/08/22 → 31/07/25
Project: Research
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Human-centred Teamwork Analytics
Gasevic, D., Martinez-Maldonado, R., Buckingham Shum, S. J., Elliott, D. J., Gasevic, D. & Ilic, D.
1/07/21 → 30/06/24
Project: Research