Using Machine Learning Techniques to Support Group Formation in an Online Collaborative Learning Environment

<p>In this paper we explore the impact of an intelligent grouping algorithm based on learners’ collaborative competency when compared with (a) instructor based Grade Point Average (GPA) method level and (b) random method, on group outcomes and group collaboration problems in an online collaborative learning environment. An intelligent grouping algorithm has been added in a Learning Management System (LMS) which is capable of forming heterogeneous groups based on learners’ collaborative competency level. True experiment design methodology was deployed to examine whether there is any association between group formation method and group scores, learning experiences and group problems. From the findings, all groups had almost similar mean scores in all group tests, and shared many similar group collaboration problems and learning experiences. However, with the understanding that GPA group formation method involves the instructor, may not be dynamic, and the random method does not guarantee heterogeneity based on learner’s collaboration competence level, instructors are more likely to adopt our intelligent grouping method as the findings show that it has similar results. Furthermore, it provides an added advantage in supporting group formation due to its guarantee on heterogeneity, dynamism, and less instructor involvement.</p>

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A Model for Improving Online Collaborative Learning through Machine Learning

Models for Improving and Optimizing Online and Blended Learning in Higher Education - Advances in Higher Education and Professional Development ◽

10.4018/978-1-4666-6280-3.ch011 ◽

2015 ◽

pp. 204-219 ◽

Cited By ~ 1

Author(s):

E. Muuro Maina ◽

Peter W. Wagacha ◽

Robert O. Oboko

Keyword(s):

Machine Learning ◽

Online Learning ◽

Collaborative Learning ◽

Learning Environment ◽

Online Learning Environment ◽

Online Collaborative Learning ◽

Collaborative Software ◽

Online Tools ◽

E Learning ◽

Collaborative Learning Environment

Online collaborative learning provides new opportunities for student collaboration in an online learning environment and at the same time spawns new challenges for teachers supporting group work. With the current Course Management Systems (CMS) such as Moodle, technology has provided online tools that include discussions forums, chat rooms, e-mails, newsgroups, workshops, etc. These tools provide a collaborative learning environment. To include constructivist learning in an online learning environment is a good collaborative strategy that is necessary since it engages learners in learning activities through interaction with their peers and teacher. A good collaborative strategy in an e-learning environment must primarily ensure that the expected interaction occurs in line with the learning mechanism being employed. This cannot merely be met by offering a set of collaborative software tools alone. It also requires the instructors' support. As the number of students studying online continues to increase, there is need to develop models that can improve online collaborative learning with minimal involvement of the instructor because the instructor might not be able to cope with increased number of students. To address this need, this chapter discusses a novel model for improving online collaborative learning that uses Machine Learning (ML) techniques.

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