Adaptive Support For Group Formation In Computer Supported Collaborative Learning

Author(s):  
Adeniran Adetunji
Author(s):  
Changhao Liang ◽  
Rwitajit Majumdar ◽  
Hiroaki Ogata

AbstractCollaborative learning in the form of group work is becoming increasingly significant in education since interpersonal skills count in modern society. However, teachers often get overwhelmed by the logistics involved in conducting any group work. Valid support for executing and managing such activities in a timely and informed manner becomes imperative. This research introduces an intelligent system focusing on group formation which consists of a parameter setting module and the group member visualization panel where the results of the created group are shown to the user and can be graded. The system supports teachers by applying algorithms to actual learning log data thereby simplifying the group formation process and saving time for them. A pilot study in a primary school mathematics class proved to have a positive effect on students’ engagement and affections while participating in group activities based on the system-generated groups, thus providing empirical evidence to the practice of Computer-Supported Collaborative Learning (CSCL) systems.


2019 ◽  
Vol 121 (1/2) ◽  
pp. 1-18
Author(s):  
Yuxin Chen ◽  
Christopher D. Andrews ◽  
Cindy E. Hmelo-Silver ◽  
Cynthia D'Angelo

Purpose Computer-supported collaborative learning (CSCL) is widely used in different levels of education across disciplines and domains. Researchers in the field have proposed various conceptual frameworks toward a comprehensive understanding of CSCL. However, as the definition of CSCL is varied and contextualized, it is critical to develop a shared understanding of collaboration and common definitions for the metrics that are used. The purpose of this research is to present a synthesis that focuses explicitly on the types and features of coding schemes that are used as analytic tools for CSCL. Design/methodology/approach This research collected coding schemes from researchers with diverse backgrounds who participated in a series of workshops on collaborative learning and adaptive support in CSCL, as well as coding schemes from recent volumes of the International Journal of Computer-Supported Collaborative learning (ijCSCL). Each original coding scheme was reviewed to generate an empirically grounded framework that reflects collaborative learning models. Findings The analysis generated 13 categories, which were further classified into three domains: cognitive, social and integrated. Most coding schemes contained categories in the cognitive and integrated domains. Practical implications This synthesized coding scheme could be used as a toolkit for researchers to pay attention to the multiple and complex dimensions of collaborative learning and for developing a shared language of collaborative learning. Originality/value By analyzing a set of coding schemes, the authors highlight what CSCL researchers find important by making these implicit understandings of collaborative learning visible and by proposing a common language for researchers across disciplines to communicate by referencing a synthesized framework.


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