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New publication: Data-informed design parameters for adaptive collaborative scripting in across-spaces learning situations, User Modeling and User-Adapted Interaction

New publication: Data-informed design parameters for adaptive collaborative scripting in across-spaces learning situations, User Modeling and User-Adapted Interaction

 
Abstract
This study presents how predictive analytics can be used to inform the formulation
of adaptive collaborative learning groups in the context of Computer Supported Col-
laborative 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, i n which real-time predictions of student’s future collaborative learn-
ing activity participation were used to formulate adaptive collaborative learner groups.
Findings of the case studies showed that the data collected from across-spaces learn-
ing 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.

15.07.2019

 

Amarasinghe, I., Hernández-Leo, D., Jonsson, A. (2019) Data-Informed Design Parameters for Adaptive Collaborative Scripting in Across-Spaces Learning Situations, User Modeling and User-Adapted Interaction, 29(4), 869–892, https://doi.org/10.1007/s11257-019-09233-8

 

Abstract:

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.

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