Supporting Group Coursework Assessment in Large Computing Classes through an Open-Source Web Application
Supporting Group Coursework Assessment in Large Computing Classes through an Open-Source Web Application
Group coursework in computing often suffers from uneven workload distribution, poor communication, and limited visibility for staff, especially in large cohorts. We present an open-source web application that addresses these challenges through automated team allocation (via genetic algorithms), student skill self-assessment, meeting tracking, peer review, and supervisor tools. The system is embedded in a year-long second-year undergraduate module on software design and development (with 300 students in 2025/26) and is replacing manual, ad-hoc processes with structured, data-driven support. Informal trials suggest it helps staff identify struggling teams early and supports fairer marking. Data collection from the first cohort is ongoing. We share findings, reflect on design choices, and discuss implications for adoption in other computing courses.
group work, peer assessment, team formation, computing education, collaborative learning, educational technology
Association for Computing Machinery
Kitson, Sam
cd52b584-1aa8-4d6a-87e2-65ccbbcf1082
Wilde, Adriana Gabriela
4f9174fe-482a-4114-8e81-79b835946224
Gomer, Richard
71c5969f-2da0-47ab-b2fb-a7e1d07836b1
7 January 2026
Kitson, Sam
cd52b584-1aa8-4d6a-87e2-65ccbbcf1082
Wilde, Adriana Gabriela
4f9174fe-482a-4114-8e81-79b835946224
Gomer, Richard
71c5969f-2da0-47ab-b2fb-a7e1d07836b1
Kitson, Sam, Wilde, Adriana Gabriela and Gomer, Richard
(2026)
Supporting Group Coursework Assessment in Large Computing Classes through an Open-Source Web Application.
In CEP 2026: Computing Education Practice 2026.
Association for Computing Machinery.
4 pp
.
(doi:10.1145/3772338.3772348).
Record type:
Conference or Workshop Item
(Paper)
Abstract
Group coursework in computing often suffers from uneven workload distribution, poor communication, and limited visibility for staff, especially in large cohorts. We present an open-source web application that addresses these challenges through automated team allocation (via genetic algorithms), student skill self-assessment, meeting tracking, peer review, and supervisor tools. The system is embedded in a year-long second-year undergraduate module on software design and development (with 300 students in 2025/26) and is replacing manual, ad-hoc processes with structured, data-driven support. Informal trials suggest it helps staff identify struggling teams early and supports fairer marking. Data collection from the first cohort is ongoing. We share findings, reflect on design choices, and discuss implications for adoption in other computing courses.
Text
cep2026_10
- Accepted Manuscript
More information
Submitted date: 11 September 2025
Accepted/In Press date: 13 October 2025
Published date: 7 January 2026
Venue - Dates:
Computing Education Practice 2026, Durham University, Durham, United Kingdom, 2026-01-07 - 2026-01-08
Keywords:
group work, peer assessment, team formation, computing education, collaborative learning, educational technology
Identifiers
Local EPrints ID: 507283
URI: http://eprints.soton.ac.uk/id/eprint/507283
PURE UUID: 5dcac676-5b2c-4491-b43d-3c787b4a87b3
Catalogue record
Date deposited: 03 Dec 2025 17:31
Last modified: 08 Jan 2026 02:57
Export record
Altmetrics
Contributors
Author:
Sam Kitson
Author:
Adriana Gabriela Wilde
Author:
Richard Gomer
Download statistics
Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.
View more statistics