The effects of a learning analytics dashboard on teachers’ diagnosis and intervention in computer-supported collaborative learning

Author(s):  
Haogang Bao ◽  
Yanyan Li ◽  
You Su ◽  
Shuang Xing ◽  
Nian-Shing Chen ◽  
...  
2016 ◽  
Vol 9 (4) ◽  
pp. 1-15 ◽  
Author(s):  
Ángel Hernández-García ◽  
Miguel Ángel Conde-González

Despite the great potential of social network analysis (SNA) methods and visualizations for learning analytics in computer-supported collaborative learning (CSCL), these approaches have not been fully explored due to two important barriers: the scarcity and limited functionality of built-in tools in Learning Management Systems (LMS), and the difficulty to import educational data from formal virtual learning environments into social network analysis programs. This study aims to cover that gap by introducing GraphFES, an application and web service for extraction of interaction data from Moodle message boards and generation of the corresponding social graphs for later analysis using Gephi, a general purpose SNA software. In addition, this paper briefly illustrates the potential of the combination of the three systems (Moodle, GraphFES and Gephi) for social learning analytics using real data from a computer-supported collaborative learning course with strong focus on teamwork and intensive use of forums.


2020 ◽  
Author(s):  
Bodong Chen

Collaboration is an important competency in the modern society. To harness the intersection of learning, work, and collaboration with analytics, several fundamental challenges need to be addressed. This chapter about collaboration analytics aims to highlight these challenges for the learning analytics community. We first survey the conceptual landscape of collaboration and learning with a focus on the computer-supported collaborative learning (CSCL) literature while attending to perspectives from computer supported cooperative work (CSCW). Grounded in the conceptual exploration, we then distinguish two salient strands of collaboration analytics: (a) /computational analysis of collaboration/ that applies computational methods to examining collaborative processes; and (b) /analytics for collaboration/ which is primarily concerned with designing and deploying data analytics in authentic contexts to facilitate collaboration. Examples and cases representing different contexts for learning and analytical frames are presented, followed by a discussion of key challenges and future directions.


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