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Scaling up and automating formative assessment in computer science

Gordon, Neil

Authors



Contributors

Sam Elkington
Editor

Alastair Irons
Editor

Abstract

The rise in student numbers in computer science creates a challenge for delivery. Computer science has some of the worst attainment and retention profiles across subjects. Given its technology focus, it is a subject where digital technologies have long been adopted and so passed through the digital age earlier than others. But there is sometimes a tension between the use of technology in teaching computer science and the teaching of computer science technology. The examples in this case study explore approaches that allow for flexible, scalable, and effective teaching solutions, enabled by technology. A catalyst for some of this was the experiences of teaching during the COVID-19 pandemic, where the nature and use of digital-based education shifted and adapted due to the unprecedented situation. Since then, there has been a move towards more hybrid teaching and learning, where campus-based education has returned for many institutions but supported by new digital content and approaches. Many of the practices for computer science explored here are applicable in other disciplines.

Citation

Gordon, N. (2025). Scaling up and automating formative assessment in computer science. In S. Elkington, & A. Irons (Eds.), Formative Assessment and Feedback in Post-Digital Learning Environments: Disciplinary Case Studies in Higher Education (172-178). Routledge. https://doi.org/10.4324/9781003360254-22

Online Publication Date Mar 26, 2025
Publication Date Mar 26, 2025
Deposit Date Mar 10, 2025
Publicly Available Date Sep 27, 2026
Publisher Routledge
Peer Reviewed Peer Reviewed
Pages 172-178
Book Title Formative Assessment and Feedback in Post-Digital Learning Environments: Disciplinary Case Studies in Higher Education
Chapter Number 22
ISBN 9781032418933 ; 9781032418940
DOI https://doi.org/10.4324/9781003360254-22
Public URL https://hull-repository.worktribe.com/output/5075305
Contract Date Jan 8, 2025