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Incorporating student population differences for effective online education: A content-based review and integrative model
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Computers & Education Volume 138, Number 1, ISSN 0360-1315 Publisher: Elsevier Ltd

Abstract

The present study reviews and analyzes recent literature relevant to learning online in higher education. This study applied a content-based method for collecting and evaluating recently published studies relating to populations of students engaged in formal online programs and courses. Based on our findings, we identified key descriptors and processes that prove essential for defining online student populations and understanding their differences.This study offers a new conceptual model that consolidates certain prior typologies and frameworks of online learning. The model integrates key components and major processes derived from our study's findings. Our model thus synthesizes and classifies the major categories of antecedents and processes, and suggests relationships that directly or indirectly influence outcomes. The model also provides an integrated framework by which educators and institutions may develop better strategies and approaches for designing, developing, and delivering online education. This study and its model thus advance theory and contribute to educational practice and research in online learning. The study encourages more research specifically on student populations engaged in online learning. Our framework orients future studies that can generate more consistent, coherent data on antecedents of student populations and how population differences interrelate with processes to impact online learning outcomes.

Citation

Money, W.H. & Dean, B.P. (2019). Incorporating student population differences for effective online education: A content-based review and integrative model. Computers & Education, 138(1), 57-82. Elsevier Ltd. Retrieved June 6, 2020 from .

This record was imported from Computers & Education on June 3, 2019. Computers & Education is a publication of Elsevier.

Full text is availabe on Science Direct: http://dx.doi.org/10.1016/j.compedu.2019.03.013

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