Providing individual student feedback at scale for mathematical disciplines

Robert Stanyon; Austin Tomlinson; Manjinder Kainth; Nicola Wilkin

doi:10.1145/3491140.3528313

Providing individual student feedback at scale for mathematical disciplines

Robert Stanyon, Austin Tomlinson, Manjinder Kainth, Nicola Wilkin

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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Abstract

This WIP discusses the preliminary results of a paid-for-pilot, at the University of Birmingham, of a new assessment and feedback platform --- Graide. Graide uses machine learning and AI to assist educators in the grading process. It has been shown to increase both the detail of feedback for individual students and consistency of feedback across the cohort. Graide enables increased oversight of the assessment process whilst providing opportunities for continuous training of markers, whilst also reducing the time taken to grade work by up to 89%.

Original language	English
Title of host publication	L@S 2022 - Proceedings of the 9th ACM Conference on Learning @ Scale
Subtitle of host publication	Proceedings of the Ninth ACM Conference on Learning @ Scale
Place of Publication	New York
Publisher	Association for Computing Machinery (ACM)
Pages	400-404
Number of pages	5
Volume	2022
ISBN (Electronic)	978-1-4503-9158-0
DOIs	https://doi.org/10.1145/3491140.3528313
Publication status	Published - 1 Jun 2022
Event	L@S '22: Ninth (2022) ACM Conference on Learning @ Scale - Cornell Tech, New York, United States Duration: 1 Jun 2022 → 3 Jun 2022

Publication series

Name	L@S 2022 - Proceedings of the 9th ACM Conference on Learning @ Scale

Conference

Conference	L@S '22
Country/Territory	United States
City	New York
Period	1/06/22 → 3/06/22

Bibliographical note

Funding Information:
This research was funded in whole or in part by the Funder [Grant number EP/N509590/1]. For the purpose of Open Access, the author has applied a CC BY public copyright licence to any Author Accepted Manuscript (AAM) version arising from this submission.

Publisher Copyright:
© 2022 ACM.

Keywords

machine-learning
feedback
stem

ASJC Scopus subject areas

Software
Computer Networks and Communications

Access to Document

10.1145/3491140.3528313Licence: None: All rights reserved

StanyonR2022ProvidingAccepted author manuscript, 1.09 MBLicence: Creative Commons: Attribution (CC BY)

Cite this

Stanyon, R., Tomlinson, A., Kainth, M., & Wilkin, N. (2022). Providing individual student feedback at scale for mathematical disciplines. In L@S 2022 - Proceedings of the 9th ACM Conference on Learning @ Scale: Proceedings of the Ninth ACM Conference on Learning @ Scale (Vol. 2022, pp. 400-404). (L@S 2022 - Proceedings of the 9th ACM Conference on Learning @ Scale). Association for Computing Machinery (ACM). https://doi.org/10.1145/3491140.3528313

Stanyon, Robert ; Tomlinson, Austin ; Kainth, Manjinder et al. / Providing individual student feedback at scale for mathematical disciplines. L@S 2022 - Proceedings of the 9th ACM Conference on Learning @ Scale: Proceedings of the Ninth ACM Conference on Learning @ Scale. Vol. 2022 New York : Association for Computing Machinery (ACM), 2022. pp. 400-404 (L@S 2022 - Proceedings of the 9th ACM Conference on Learning @ Scale).

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Stanyon, R, Tomlinson, A, Kainth, M & Wilkin, N 2022, Providing individual student feedback at scale for mathematical disciplines. in L@S 2022 - Proceedings of the 9th ACM Conference on Learning @ Scale: Proceedings of the Ninth ACM Conference on Learning @ Scale. vol. 2022, L@S 2022 - Proceedings of the 9th ACM Conference on Learning @ Scale, Association for Computing Machinery (ACM), New York, pp. 400-404, L@S '22, New York, New York, United States, 1/06/22. https://doi.org/10.1145/3491140.3528313

Providing individual student feedback at scale for mathematical disciplines. / Stanyon, Robert; Tomlinson, Austin; Kainth, Manjinder et al.
L@S 2022 - Proceedings of the 9th ACM Conference on Learning @ Scale: Proceedings of the Ninth ACM Conference on Learning @ Scale. Vol. 2022 New York: Association for Computing Machinery (ACM), 2022. p. 400-404 (L@S 2022 - Proceedings of the 9th ACM Conference on Learning @ Scale).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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Stanyon R, Tomlinson A, Kainth M, Wilkin N. Providing individual student feedback at scale for mathematical disciplines. In L@S 2022 - Proceedings of the 9th ACM Conference on Learning @ Scale: Proceedings of the Ninth ACM Conference on Learning @ Scale. Vol. 2022. New York: Association for Computing Machinery (ACM). 2022. p. 400-404. (L@S 2022 - Proceedings of the 9th ACM Conference on Learning @ Scale). doi: 10.1145/3491140.3528313

Providing individual student feedback at scale for mathematical disciplines

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this