2026-04-13T07:23:33Zhttps://recercat.cat/oai/request
oai:recercat.cat:10230/372772025-12-24T08:51:49Zcom_2072_6col_2072_452952
RECERCAT author Amarasinghe, Ishari author Hernández Leo, Davinia author Jonsson, Anders, 1973- 2019-05-23T11:44:06Z2019 This study presents how predictive analytics can be used to inform the formulation of adaptive collaborative learning groups in the context of Computer Supported Collaborative Learning considering across-spaces learning situations. During the study we have collected data from different learning spaces which depicted both individual and collaborative learning activity engagement of students in two different learning contexts (namely the classroom learning and distance learning context) and attempted to predict individual student’s future collaborative learning activity participation in a pyramid-based collaborative learning activity using supervised machine learning techniques. We conducted experimental case studies in the classroom and in distance learning settings, in which real-time predictions of student’s future collaborative learning activity participation were used to formulate adaptive collaborative learner groups. Findings of the case studies showed that the data collected from across-spaces learning scenarios is informative when predicting future collaborative learning activity participation of students hence facilitating the formulation of adaptive collaborative group configurations that adapt to the activity participation differences of students in real-time. Limitations of the proposed approach and future research direction are illustrated.This work has been partially funded by FEDER, the National Research Agency of the Spanish Ministry of Science, Innovations and Universities MDM-2015-0502, TIN2014-53199-C3-3-R, TIN2017-85179-C3-3-R and “la Caixa Foundation” (CoT project, 100010434). DHL is a Serra Húnter Fellow. © Springer The final publication is available at Springer via http://dx.doi.org/10.1007/s11257-019-09233-8 info:eu-repo/semantics/openAccess Computer supported collaborative learning (CSCL) Adaptive collaborative scripting Collaborative learning flow patterns (CLFP) Supervised machine learning Prediction algorithms Data-informed design parameters for adaptive collaborative scripting in across-spaces learning situations info:eu-repo/semantics/article info:eu-repo/semantics/acceptedVersion