Detecting Bias in Meta-Analyses of Distance Education Research: Big Pictures We Can Rely On
ARTICLE
Robert M. Bernard, Eugene Borokhovski, Rana M. Tamim
Distance Education Volume 35, Number 3, ISSN 0158-7919
Abstract
This article has two interrelated purposes. The first is to explain how various forms of bias, if introduced during any stage of a meta-analysis, can provide the consumer with a misimpression of the state of a research literature. Five of the most important bias-producing aspects of a meta-analysis are presented and discussed. Second, armed with this information, we examine 15 meta-analyses of the literatures of distance education (DE), online learning (OL), and blended learning (BL), conducted from 2000 to 2014, with the intention of assessing potential sources of bias in each. All of these meta-analyses address the question: "How do students taking courses through DE, OL, and BL compare to students engaged in pure classroom instruction in terms of learning achievement outcomes?" We argue that questions asked by primary researchers must change to reflect issues that will drive improvements in designing and implementing DE, OL, and BL courses.
Citation
Bernard, R.M., Borokhovski, E. & Tamim, R.M. (2014). Detecting Bias in Meta-Analyses of Distance Education Research: Big Pictures We Can Rely On. Distance Education, 35(3), 271-293. Retrieved March 19, 2024 from https://www.learntechlib.org/p/157399/.
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Cited By
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Research Trends in Mobile Learning in Higher Education: A Systematic Review of Articles (2011 – 2015)
Greig Krull & Josep Duart, Universitat Oberta de Catalunya
The International Review of Research in Open and Distributed Learning Vol. 18, No. 7 (Nov 29, 2017)
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