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Predicting Engagement on Collaborative Learning Systems: Perceptions of Postgraduate Students

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dc.contributor.author Gyamfi S.A.
dc.contributor.author Koranteng F.N.
dc.contributor.author Apau R.
dc.contributor.author Ansong-Gyimah K.
dc.date.accessioned 2022-10-31T15:05:22Z
dc.date.available 2022-10-31T15:05:22Z
dc.date.issued 2020
dc.identifier.other 10.1145/3383923.3383959
dc.identifier.uri http://41.74.91.244:8080/handle/123456789/391
dc.description Gyamfi, S.A., Department of Information Technology, University of Education, Kumasi Campus, Winneba, Ghana; Koranteng, F.N., Department of Information Technology, University of Education, Kumasi Campus, Winneba, Ghana; Apau, R., Department of Computer Science, Kwame Nkrumah University of Science and Technology, Kumasi-Ghana, Ghana; Ansong-Gyimah, K., Department of Information Technology, University of Education, Kumasi Campus, Winneba, Ghana en_US
dc.description.abstract One of the main prerequisites for the implementation of collaborative learning systems in higher education is academic engagement by students. At the same time, user satisfaction on collaborative learning systems is a precondition for acceptance of such software by students. This presupposes that, for academic institutions to enhance students' participation and engagement on collaborative systems, they must be satisfied with the software. The main aim of this paper is to predict postgraduate students' academic engagement on collaborative learning systems. The paper proposes a model that integrates the Confirmation Expectation Model and Information System (IS) Success Model in order to uncover the factors that influence students' satisfaction while they are using collaborative learning systems. Using a questionnaire, the model is validated with responses from tertiary students in two public universities in Ghana. A PLS-SEM analysis of 146 valid responses was conducted. The hypothesized model explained 25.7% of the variance on Engagement. The results also confirmed all predicted relationships. Specifically, System Quality and Confirmation predicted Perceived Usefulness. Information Quality, Service Quality and Perceived Usefulness also influenced User Satisfaction and User Satisfaction impacted Engagement. The findings provide insightful perspective for institutions and developers of collaborative learning systems that could help to provide information that is relevant to students' academic activities. � 2020 ACM. en_US
dc.publisher Association for Computing Machinery en_US
dc.subject Collaborative learning systems en_US
dc.subject Engagement en_US
dc.subject IS Success Model en_US
dc.subject Postgraduate students en_US
dc.subject User Satisfaction en_US
dc.title Predicting Engagement on Collaborative Learning Systems: Perceptions of Postgraduate Students en_US
dc.type Conference Paper en_US


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