A meta-analysis of blended learning and technology use in higher education: From the general to the applied

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Journal of Computing in Higher Education


This paper serves several purposes. First and foremost, it is devoted to developing a better understanding of the effectiveness of blended learning (BL) in higher education. This is achieved through a meta-analysis of a sub-collection of comparative studies of BL and classroom instruction (CI) from a larger systematic review of technology integration (Schmid et al. in Comput Educ 72:271-291, 2014). In addition, the methodology of meta-analysis is described and illustrated by examples from the current study. The paper begins with a summary of the experimental research on distance education (DE) and online learning (OL), encapsulated in meta-analyses that have been conducted since 1990. Then it introduces the Bernard et al. (Rev Educ Res 74(3):379-439, 2009) meta-analysis, which attempted to alter the DE research culture of always comparing DE/OL with CI by examining three forms of interaction treatments (i.e., student-student, student-teacher, student-content) within DE, using the theoretical framework of Moore (Am J Distance Educ 3(2):1-6, 1989) and Anderson (Rev Res Open Distance Learn 4(2):9-14, 2003). The rest of the paper revolves around the general steps and procedures (Cooper in Research synthesis and meta-analysis: a step-by-step approach, 4th edn, SAGE, Los Angeles, CA, 2010) involved in conducting a meta-analysis. This section is included to provide researchers with an overview of precisely how meta-analyses can be used to respond to more nuanced questions that speak to underlying theory and inform practice-in other words, not just answers to the "big questions." In this instance, we know that technology has an overall positive impact on learning (g+ = +0.35, p <.01, Tamim et al. in Rev Educ Res 81(3):4-28, 2011), but the sub-questions addressed here concern BL interacting with technology in higher education. The results indicate that, in terms of achievement outcomes, BL conditions exceed CI conditions by about one-third of a standard deviation (g+ = 0.334, k = 117, p <.001) and that the kind of computer support used (i.e., cognitive support vs. content/presentational support) and the presence of one or more interaction treatments (e.g., student-student/-teacher/-content interaction) serve to enhance student achievement. We examine the empirical studies that yielded these outcomes, work through the methodology that enables evidence-based decision-making, and explore how this line of research can improve pedagogy and student achievement. © 2014 Springer Science+Business Media New York.

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