Theories and Applications of Massive Online Open Courses (MOOCs) : The Case for Hybrid Design

Authors

  • Abram Anders University of Minnesota Duluth

DOI:

https://doi.org/10.19173/irrodl.v16i6.2185

Keywords:

blended learning, connectivism, cMOOCs, emergent learning, heutagogy, hybrid MOOCs, massive open online courses (MOOCs), xMOOCs

Abstract

Initial research on learning in massive open online courses (MOOCs) primarily focused participation patterns and participant experiences. More recently, research has addressed learning theories and offered case studies of different pedagogical designs for MOOCs. Based on a meta-analysis and synthesis of the research literature, this study develops a conceptual model of prominent theories and applications of MOOCs. It proposes a continuum of MOOC learning design that consolidates previous theories into a tripartite scheme corresponding to primary types of MOOCs including content-based, community/tasked-based, and network-based applications. A series of MOOC hybrids are analyzed to demonstrate the value of this model while also clarifying appropriate applications and significant design challenges for MOOCs.

Author Biography

Abram Anders, University of Minnesota Duluth

Abram Anders is an assistant professor of business communication in the Labovitz School of Business and Economics at the University of Minnesota Duluth. He received his PhD in English from Pennsylvania State University. He studies rhetoric and composition with an emphasis in professional communication and technology-enhanced learning. His research interests also include business communication,  cultural studies, literary studies, networked learning, new media, open source, rhetoric, and theory. His research has appeared in Business Communication Quarterly, Configurations, Disability Studies Quarterly, and The KB Journal.

Published

2015-12-03

How to Cite

Anders, A. (2015). Theories and Applications of Massive Online Open Courses (MOOCs) : The Case for Hybrid Design. The International Review of Research in Open and Distributed Learning, 16(6). https://doi.org/10.19173/irrodl.v16i6.2185

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