Using Students' Affective State as a Measure of CS Lab Risk in an Early Detection System

Bikanga Ada, M. and Sears, G. (2021) Using Students' Affective State as a Measure of CS Lab Risk in an Early Detection System. In: 2021 International Conference on Advanced Learning Technologies (ICALT), 12-15 Jul 2021, pp. 203-205. ISBN 9781665441063 (doi: 10.1109/ICALT52272.2021.00067)

Abstract

This paper presents a dual dashboard early warning system which uses students’ affective state as a measure of risk. Affective state has been shown to influence CS1 performance, and specific states such as frustration have been linked to attrition. The software administers affective surveys to students using a series of 2-dimensional grids. Students then complete a qualitative journal entry. Risk weights are assigned to students based on the journal response’s sentiment analysis and whether student’s 2-dimensional grid responses fall within configurable 'danger zone' bounds. The early warning system automatically flags students as needing support if the responses’ combined risk weights exceed configurable thresholds. Additionally, flags can be assigned manually, either by instructors or by students themselves.

• Item Type: Conference Proceedings
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Bikanga Ada, Dr Mireilla
• Authors: Bikanga Ada, M., and Sears, G.
• College/School: College of Science and Engineering > School of Computing Science
• ISSN: 2161-377X
• ISBN: 9781665441063
• Published Online: 02 August 2021
• Copyright Holders: Copyright © 2021 IEEE
• First Published: First published in Proceedings of 2021 International Conference on Advanced Learning Technologies (ICALT)
• Publisher Policy: Reproduced in accordance with the copyright policy of the publisher

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