Understanding MOOC students: motivations and behaviours indicative of MOOC completion

Massive open online courses MOOCs continue to appear across the higher education landscape, originating from many institutions in the USA and around the world. MOOCs typically have low completion rates, at least when compared with traditional courses, as this course delivery model is very different from traditional, fee-based models, such as college courses. This research examined MOOC student demographic data, intended behaviours and course interactions to better understand variables that are indicative of MOOC completion. The results lead to ideas regarding how these variables can be used to support MOOC students through the application of learning analytics tools and systems.

[1]  Robert Rueda,et al.  Situational interest, computer self-efficacy and self-regulation: Their impact on student engagement in distance education , 2012, Br. J. Educ. Technol..

[2]  Kristy Elizabeth Boyer,et al.  Unsupervised modeling for understanding MOOC discussion forums: a learning analytics approach , 2015, LAK.

[3]  Karen Swan,et al.  Validating a Measurement Tool of Presen ce in Online Communities of Inquiry , 2008 .

[4]  Erman Yükseltürk,et al.  Predictors for Student Success in an Online Course , 2007, J. Educ. Technol. Soc..

[5]  Z. Berge,et al.  Student barriers to online learning: A factor analytic study , 2005 .

[6]  M. Mitchell Waldrop,et al.  Online learning: Campus 2.0 , 2013, Nature.

[7]  Stephanie D. Teasley,et al.  A time series interaction analysis method for building predictive models of learners using log data , 2015, LAK.

[8]  Shirley Williams,et al.  MOOCs: A systematic study of the published literature 2008-2012 , 2013 .

[9]  Björn Hartmann,et al.  Should your MOOC forum use a reputation system? , 2014, CSCW.

[10]  Jason K. Y. Chan,et al.  Direct and indirect effects of online learning on distance education , 2004, Br. J. Educ. Technol..

[11]  David E. Pritchard,et al.  Studying Learning in the Worldwide Classroom Research into edX's First MOOC. , 2013 .

[12]  John P. Campbell,et al.  Academic Analytics: A New Tool for a New Era. , 2007 .

[13]  Andrew D. Ho,et al.  Changing “Course” , 2014 .

[14]  Robert F. Boruch,et al.  Moving Through MOOCs , 2014 .

[15]  S. Rozendaal,et al.  Campus 2.0 , 2017 .

[16]  J. Krosnick,et al.  Survey research. , 1999, Annual review of psychology.

[17]  C. C. Robinson,et al.  New Benchmarks in Higher Education: Student Engagement in Online Learning , 2008 .

[18]  Kathryn Weed Jablokow,et al.  A Multidisciplinary MOOC on Creativity, Innovation, and Change: Encour- aging Experimentation and Experiential Learning on a Grand Scale , 2014 .

[19]  Yvonne Belanger,et al.  Bioelectricity: A Quantitative Approach Duke University’s First MOOC , 2013 .

[20]  Julian Robinson,et al.  Assessing the value of using an online discussion board for engaging students , 2011 .

[21]  Marcia D. Dixson.  Creating effective student engagement in online courses: What do students find engaging? , 2010 .

[22]  Karen Swan,et al.  Virtual interaction: Design factors affecting student satisfaction and perceived learning in asynchronous online courses , 2001 .

[23]  D. Garrison,et al.  Assessing Social Presence In Asynchronous Text-based Computer Conferencing , 1999 .

[24]  Jennifer C. Richardson,et al.  The Role of Students' Cognitive Engagement in Online Learning , 2006 .

[25]  George D. Kuh,et al.  Being (Dis)Engaged in Educationally Purposeful Activities: The Influences of Student and Institutional Characteristics , 2002 .

[26]  Phyllis Jones,et al.  Virtual Spaces: Employing a Synchronous Online Classroom to Facilitate Student Engagement in Online Learning , 2009 .

[27]  J. Daniel,et al.  Making Sense of MOOCs : Musings in a Maze of Myth , Paradox and Possibility Author : , 2013 .

[28]  Terry Anderson,et al.  Student Perceptions of Teaching Presence, Social Presence, and Cognitive Presence in a Virtual World , 2011 .

[29]  Kayode C. V. Ekwunife-Orakwue,et al.  The impact of transactional distance dialogic interactions on student learning outcomes in online and blended environments , 2014, Comput. Educ..

[30]  Shane Dawson,et al.  Mining LMS data to develop an "early warning system" for educators: A proof of concept , 2010, Comput. Educ..

[31]  A. A. G. Putra,et al.  PIECING TOGETHER THE STUDENT SUCCESS PUZZLE: RESEARCH, PROPOSITIONS, AND RECOMMENDATIONS , 2013 .

[32]  Matthew D. Pistilli,et al.  Course signals at Purdue: using learning analytics to increase student success , 2012, LAK.

[33]  Kerri-Lee Krause,et al.  Students’ engagement in first‐year university , 2008 .

[34]  C. Tu,et al.  The Relationship of Social Presence and Interaction in Online Classes , 2002 .

[35]  Dianne L. Conrad,et al.  Deep in the Hearts of Learners: Insights into the Nature of Online Community , 2002 .

[36]  Chris Piech,et al.  Deconstructing disengagement: analyzing learner subpopulations in massive open online courses , 2013, LAK '13.

[37]  Allison Littlejohn,et al.  Merlot Journal of Online Learning and Teaching Patterns of Engagement in Connectivist Moocs , 2022 .

[38]  Vicki Trowler.  Student engagement literature review , 2010 .

[39]  John H. Bradley,et al.  Computer Self-Efficacy, Anxiety, and Learning in Online Versus Face to Face Medium , 2012, J. Inf. Technol. Educ. Res..

[40]  Katrina A. Meyer,et al.  Student Engagement in Online Learning: What Works and Why , 2014 .

[41]  D. Keegan.  Theoretical Principles of Distance Education. , 1994 .

[42]  Katy Jordan,et al.  Initial trends in enrolment and completion of massive open online courses , 2014 .