scholarly journals A Learning Analytics Study of the Effect of Group Size on Social Dynamics and Performance in Online Collaborative Learning

2019 ◽  
pp. 466-479 ◽  
Author(s):  
Mohammed Saqr ◽  
Jalal Nouri ◽  
Ilkka Jormanainen
Keyword(s):  
Collaborative Learning ◽  
Group Size ◽  
Learning Analytics ◽  
Social Dynamics ◽  
Online Collaborative Learning ◽  
And Performance

Web Data Mining in Education

2017 ◽  
pp. 58-77 ◽  
Author(s):  
Wing Shui Ng
Keyword(s):  
Data Mining ◽  
Collaborative Learning ◽  
Learning Analytics ◽  
Web Data ◽  
Web Data Mining ◽  
Collaborative Process ◽  
Web Technologies ◽  
Online Collaborative Learning ◽  
Pedagogical Decisions ◽  
New Research

Web data mining for extracting meaningful information from large amount of web data has been explored over a decade. The concepts and techniques have been borrowed into the education sector and the new research discipline of learning analytics has emerged. With the development of web technologies, it has been a common practice to design online collaborative learning activities to enhance learning. To apply learning analytics techniques to monitor the online collaborative process enables a lecturer to make instant and informed pedagogical decisions. However, it is still a challenge to build strong connection between learning analytics and learning science for understanding cognitive progression in learning. In this connection, this chapter reports a study to apply learning analytics techniques in the aspect of web usage mining and clustering analysis with underpinning Bloom's taxonomy to analyze students' performance in the online collaborative learning process. The impacts of intermediate interventions are also elaborated.

scholarly journals Commentary On “Theory-led design of instruments and representations in learning analytics: Developing a novel tool for orchestration of online collaborative learning”

2015 ◽  
Vol 2 (2) ◽  
pp. 44-46 ◽  
Author(s):  
Chris Teplovs
Keyword(s):  
Discourse Analysis ◽  
Collaborative Learning ◽  
Learning Analytics ◽  
Analysis Tool ◽  
Theoretical Frameworks ◽  
Online Collaborative Learning ◽  
Alternative Approaches ◽  
The Creation

This commentary reflects on the contributions to learning analytics and theory by a paper that describes how multiple theoretical frameworks were woven together to inform the creation of a new automated discourse analysis tool.  The commentary highlights the contributions of the original paper, provides some alternative approaches, and touches on issues of sustainability and scalability of learning analytics innovations.

scholarly journals Insights from Learning Analytics for Hands-On Cloud Computing Labs in AWS

2020 ◽  
Vol 10 (24) ◽  
pp. 9148
Author(s):  
Germán Moltó ◽  
Diana M. Naranjo ◽  
J. Damian Segrelles
Keyword(s):  
Cloud Computing ◽  
Learning Analytics ◽  
Cloud Provider ◽  
Online Students ◽  
Computing Services ◽  
Hands On ◽  
Data Processing Tool ◽  
Amazon Web Services ◽  
Course Length ◽  
And Performance

Cloud computing instruction requires hands-on experience with a myriad of distributed computing services from a public cloud provider. Tracking the progress of the students, especially for online courses, requires one to automatically gather evidence and produce learning analytics in order to further determine the behavior and performance of students. With this aim, this paper describes the experience from an online course in cloud computing with Amazon Web Services on the creation of an open-source data processing tool to systematically obtain learning analytics related to the hands-on activities carried out throughout the course. These data, combined with the data obtained from the learning management system, have allowed the better characterization of the behavior of students in the course. Insights from a population of more than 420 online students through three academic years have been assessed, the dataset has been released for increased reproducibility. The results corroborate that course length has an impact on online students dropout. In addition, a gender analysis pointed out that there are no statistically significant differences in the final marks between genders, but women show an increased degree of commitment with the activities planned in the course.

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