RT Conference Proceedings
T1 Machine and Human Observable Differences in Groups’ Collaborative Problem-Solving Behaviours
A1 Cukurova, Mutlu
A1 Luckin, Rose
A1 Mavrikis, Manolis
A1 Millán-Valldeperas, Eva
K1 Aprendizaje
AB This paper contributes to our understanding of how to designlearning analytics to capture and analyse collaborative problem-solving(CPS) in practice-based learning activities. Most research in learning analytics focuses on student interaction in digital learning environments, yet still most learning and teaching in schools occurs in physical environments. Investigation of student interaction in physical environments can be used to generate observable differences among students, which can then be used in the design and implementation of Learning Analytics.Here, we present several original methods for identifying such differences in groups CPS behaviours. Our data set is based on human observation, hand position ( fiducial marker) and heads direction (face recognition)data from eighteen students working in six groups of three. The results show that the high competent CPS groups spend an equal distribution of time on their problem-solving and collaboration stages. Whereas, the low competent CPS groups spend most of their time in identifying knowledge and skill defi ciencies only. Moreover, as machine observable data shows, high competent CPS groups present symmetrical contributions to the physical tasks and present high synchrony and individual accountability values. The  findings have signifi cant implications on the design and implementation of future learning analytics systems.
YR 2019
FD 2019-10-18
LK https://hdl.handle.net/10630/18599
UL https://hdl.handle.net/10630/18599
LA eng
NO Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech.Agencia Estatal de Investigación (AEI) y el Fondo Europeo de Desarrollo Regional (FEDER),TIN2016-80774-R.
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 13 abr 2026