A meta-analysis of blended learning and technology use in higher education: from the general to the applied - Bernard, Robert M.; Borokhovski, Eugene; Schmid, Richard F.; Tamim, Rana M.; Abrami, Philip C. (Journal of computing in higher education, 2014)
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.