User Modeling and User-Adapted Interaction

, Volume 29, Issue 4, pp 869–892 | Cite as

Data-informed design parameters for adaptive collaborative scripting in across-spaces learning situations

  • Ishari AmarasingheEmail author
  • Davinia Hernández-Leo
  • Anders Jonsson


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.


Computer Supported Collaborative Learning (CSCL) Adaptive collaborative scripting Collaborative learning flow patterns (CLFP) Supervised machine learning Prediction algorithms 



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.


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Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.ICT DepartmentUniversitat Pompeu FabraBarcelonaSpain

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