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

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).

Published paper reprinted by permission. The final version of record is published by IATED as: Dafoulas, Georgios, Cristiano Cardoso Maia, Almaas Ali, and Juan Augusto. “COLLECTING SENSOR-GENERATED DATA FOR ASSESSING TEAMWORK AND INDIVIDUAL CONTRIBUTIONS IN COMPUTING STUDENT TEAMS.” EDULEARN18 Proceedings (July 2018). doi:10.21125/edulearn.2018.2759.