Intelligent support for group work in collaborative learning environments
The delivery of intelligent support for group work is a complex issue in\ud collaborative learning environments. This particularly pertains to the construction\ud of effective groups and assessment of collaboration problems. This is because the\ud composition of groups can be affected by several variables, and various methods\ud are desirable for ascertaining the existence of different collaboration problems.\ud Literature has shown that current collaborative learning environments provide\ud limited or no support for teachers to cope with these tasks. Considering this and the\ud increasing use of online collaboration, this research aims to explore solutions for\ud improving the delivery of support for group work in collaborative learning\ud environments, and thus to simplify how teachers manage collaborative group work.\ud In this thesis, three aspects were investigated to achieve this goal. The first\ud aspect emphasises on proposing a novel approach for group formation based on\ud students‘ learning styles. The novelty and importance of this approach is the\ud provision of an automatic grouping method that can tailor to individual students‘\ud characteristics and fit well into the existing collaborative learning environments.\ud The evaluation activities comprise the development of an add-on tool and an\ud undergraduate student experiment, which indicate the feasibility and strength of the\ud proposed approach — being capable of forming diverse groups that tend to perform\ud more effectively and efficiently than similar groups for conducting group\ud discussion tasks.\ud The second focus of this research relates to the identification of major\ud group collaboration problems and their causes. A nationwide survey was conducted\ud that reveals a student perspective on the issue, which current literature fails to\ud adequately address. Based on the findings from the survey, an XML-based\ud representation was created that provides a unique perspective on the linkages\ud between the problems and causes identified.\ud Finally, the focus was then shifted to the proposal of a novel approach for\ud diagnosing the major collaboration problems identified. The originality and\ud significance of this approach lies in the provision of various methods for ascertaining the existence of different collaboration problems identified, based on\ud student interaction data that result from the group work examined. The evaluation\ud procedure focused on the development of a supporting tool and several\ud experiments with a test dataset. The results of the evaluation show that the\ud feasibility and effectiveness are sustained, to a great extent, for the diagnostic\ud methods addressed.\ud Besides these main proposals, this research has explored a multi-agent\ud architecture to unify all the components derived for intelligently managing online\ud collaborative learning, which suggests an overarching framework providing\ud context for other parts of this thesis.
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