Learner Communications in Massively Open Online Courses

Massively open online courses (MOOCs) bring together hundreds to thousands of people from around the world to learn a variety of topics in short (2 to 15) week courses. Until now, most have not offered formal institutional credit but have been freely available to anyone with an internet connection, regardless of their educational background. MOOCs have become a popular topic in higher education largely because they enable a geographically diverse group of learners to access educational resources from the world’s top universities. They have evolved from previous incarnations of online learning but are distinguished in their global reach and semi-synchronicity. In the past two years, MOOCs have received very polarized media attention. Some believe MOOCs will completely transform traditional models of higher education. Others view them as mechanisms for furthering a commoditization of learning that is best experienced in small groups and in-person. Unfortunately, a great deal of this debate has lacked theoretical grounding and evidence from rigorous research. Sound investigation is needed to move beyond these extreme views and evaluate the true pedagogical potential of MOOCs. This work analyses a key differentiator of MOOCs from previous efforts at open education – communication between a global body of learners via online discussion forums – to discover who tends to interact online and how. Literature on MOOCs has not yet indicated the backgrounds, motivations, or achievement levels of forum participants. It also has not revealed their communication patterns or how groups tend to form and disband around certain topics. As MOOCs enable communication between learners that may have otherwise never interacted, it is essential to gain insights into how they engage in online discussions to better support their learning. This study aims to address this need. Data analysis of nearly 87,000 individuals from a case study of a particular MOOC reveals a number of key trends. Forum participants – like those in the course more broadly – tend to be young adults from the western world. Students in the course favour “real-world” topics that have relevance and significance in their lives beyond the academic setting. Forum participants assemble and disperse quickly as crowds, not communities, of learners. Finally, those that engage explicitly in the discussion forums are often higher-performing than their counterparts in the course, although the vast majority of forum participants receive “failing” marks. These findings have implications for how certain types of MOOCs may encourage and promote online discussions in the future – and how these discussions can help students learn. 1 MSc Candidate, Department of Education, University of Oxford. nabeel.gillani@new.ox.ac.uk.

[1]  Thomas M. Duffy,et al.  Problem Based Learning: An instructional model and its constructivist framework , 1995 .

[2]  Hal Abelson,et al.  The Creation of OpenCourseWare at MIT , 2008 .

[3]  Albert-László Barabási,et al.  Scale-Free Networks: A Decade and Beyond , 2009, Science.

[4]  M E J Newman.  Assortative mixing in networks. , 2002, Physical review letters.

[5]  Marie Bienkowski,et al.  Enhancing Teaching and Learning Through Educational Data Mining and Learning Analytics: An Issue Brief , 2012 .

[6]  K. Punch.  Survey Research: The Basics , 2003 .

[7]  G. Leinhardt,et al.  Going the Distance With Online Education , 2006 .

[8]  Henk G. Schmidt,et al.  Cognitive engagement in the problem-based learning classroom , 2011, Advances in health sciences education : theory and practice.

[9]  Adnan Boyacı.  Authentic Learning Environments in Higher Education , 2006 .

[10]  R. Emerson,et al.  Participant Observation and Fieldnotes , 2001 .

[11]  Rajeev Motwani,et al.  The PageRank Citation Ranking : Bringing Order to the Web , 1999, WWW 1999.

[12]  M. J. W. Thomas,et al.  Learning within incoherent structures: the space of online discussion forums , 2002, J. Comput. Assist. Learn..

[13]  Gueorgi Kossinets,et al.  Empirical Analysis of an Evolving Social Network , 2006, Science.

[14]  Alex Pentland,et al.  Meeting mediator: enhancing group collaboration with sociometric feedback , 2008, CHI Extended Abstracts.

[15]  Theodore Panitz,et al.  Collaborative versus Cooperative Learning: A Comparison of the Two Concepts Which Will Help Us Understand the Underlying Nature of Interactive Learning. , 1999 .

[16]  C. Haythornthwaite.  Building Virtual Communities: Building Social Networks Via Computer Networks: Creating and Sustaining Distributed Learning Communities , 2002 .

[17]  D. Boyd,et al.  CRITICAL QUESTIONS FOR BIG DATA , 2012 .

[18]  E. Guba,et al.  Paradigmatic Controversies, Contradictions, and Emerging Confluences. , 2005 .

[19]  Annette N. Markham,et al.  Ethical Decision-Making and Internet Research: Version 2.0 Recommendations from the AoIR Ethics Working Committee , 2012 .

[20]  Jennifer Preece,et al.  The top five reasons for lurking: improving community experiences for everyone , 2004, Comput. Hum. Behav..

[21]  S. Wasserman,et al.  Social Network Analysis: Computer Programs , 1994 .

[22]  Etienne Wenger,et al.  Situated Learning: Legitimate Peripheral Participation , 1991 .

[23]  G. Hughes.  Identity and belonging in social learning groups: The importance of distinguishing social, operational and knowledge‐related identity congruence , 2010 .

[24]  M. Bassey.  Case study research in educational settings , 1999 .

[25]  H. Niles.  Correlation, Causation and Wright's Theory of "Path Coefficients". , 1922, Genetics.