Collecting sensor-generated data for assessing teamwork and individual contributions in computing student teams

Dafoulas, George ORCID: https://orcid.org/0000-0003-2638-8771, Cardoso Maia, Cristiano ORCID: https://orcid.org/0000-0001-5487-2478, Ali, Almaas and Augusto, Juan Carlos ORCID: https://orcid.org/0000-0002-0321-9150 (2018) Collecting sensor-generated data for assessing teamwork and individual contributions in computing student teams. EDULEARN18 Proceedings. In: EDULEARN18: 10th annual International Conference on Education and New Learning Technologies, 02-04 July 2018, Palma de Mallorca, Spain. ISBN 9788409027095. ISSN 2340-1117 [Conference or Workshop Item] (doi:10.21125/edulearn.2018.2759)

Preview	PDF - Published version (with publisher's formatting) Download (369kB) | Preview
	PDF - Final accepted version (with author's formatting) Restricted to Repository staff and depositor only Download (2MB)

Abstract

The aim of this paper is twofold. First, the authors describe a series of experiments that have been conducted in a dedicated smart-spaces laboratory, aiming to combine the use of several sensors in collecting student data. Second, the paper shares key findings from the use of sensor-generated data as an instrument for assessing individual contributions as well as team performance. The early sections of the paper describe the setting of a smart-space laboratory and how it was used for two scenarios; on one hand student teams were monitored during a coordination meeting involving decision making, while on the other hand students were observed during a team presentation. The discussion explains how sensors were used to monitor emotions (using facial image processing), stress (using galvanic skin response) and participation (based on the use of Kinnect). The key contribution is in the form of the experiment setting that can be replicated with students from different educational backgrounds but also in scenarios involving practitioners from different disciplines. The authors discuss the drivers for organizing this type of experiment and explain the reasoning behind the use of certain sensors and the value of collecting specific data sets. The later part of the paper describes how the analysis of collected data has produced visualizations of patterns that can be used in education for assessing student contribution, emotions and stress levels. Similar approaches could be used for project management where student teams are replaced by software engineering teams in agile development scenarios (e.g. scrum stand-up meetings).

Actions (login required)

View Item

Statistics

DownloadsShow export options

Export as CSVXMLJSON

Activity Overview

6 month trend

356Downloads

6 month trend

887Hits

Additional statistics are available via IRStats2.