A Classification of Barriers that Influence Intention Achievement in MOOCs

MOOC-learning can be challenging as barriers which prevent or hinder acting out MOOC-takers’ individual learning intentions may be encountered. The aim of this research was to elicit and to empirically classify barriers that influence this intention achievement in MOOCs. The best fit model of our factor-analytical approach resulted in 4 distinctive components; 1. Technical and online-learning related skills, 2. Social context, 3. Course design/expectations management, 4. Time, support and motivation. The main finding of our study is that the experienced barriers by MOOC-takers are predominantly non-MOOC related. This knowledge can be of value for MOOC-designers and providers. It may guide them in finding suitable re-design solutions or interventions to support MOOC-takers in their learning, even if it concerns non-MOOC related issues. Furthermore, it makes a valuable contribution to the expanding empirical research on MOOCs.

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

[2]  Ghada R. El Said,et al.  Exploring the factors affecting MOOC retention: A survey study , 2016, Comput. Educ..

[3]  Howard B. Lee,et al.  A First Course in Factor Analysis 2nd Ed , 1973 .

[4]  John A. Schibrowsky,et al.  Virtual Communities and the Assessment of Online Marketing Education , 2003 .

[5]  H. Kaiser.  The Application of Electronic Computers to Factor Analysis , 1960 .

[6]  L. Czerniewicz,et al.  Learning through engagement: MOOCs as an emergent form of provision , 2016 .

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

[8]  J. Galusha.  Barriers to Learning in Distance Education , 1997 .

[9]  Martin Ebner,et al.  MOOCs Completion Rates and Possible Methods to Improve Retention - A Literature Review , 2014 .

[10]  H. Kaiser.  A second generation little jiffy , 1970 .

[11]  Ji-Hye Park,et al.  Factors Influencing Adult Learners' Decision to Drop Out or Persist in Online Learning , 2009, J. Educ. Technol. Soc..

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

[13]  Joseph Jay Williams,et al.  HarvardX and MITx: Two Years of Open Online Courses Fall 2012-Summer 2014 , 2015 .

[14]  Jane Sinclair,et al.  Dropout rates of massive open online courses : behavioural patterns , 2014 .

[15]  H. Kaiser.  An index of factorial simplicity , 1974 .

[16]  W F Velicer,et al.  Component Analysis versus Common Factor Analysis: Some issues in Selecting an Appropriate Procedure. , 1990, Multivariate behavioral research.

[17]  Dorian A. Canelas,et al.  Understanding the massive open online course (MOOC) student experience: An examination of attitudes, motivations, and barriers , 2017, Comput. Educ..

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

[19]  Khe Foon Hew,et al.  Promoting engagement in online courses: What strategies can we learn from three highly rated MOOCS , 2016, Br. J. Educ. Technol..

[20]  Marco Kalz,et al.  Refining success and dropout in massive open online courses based on the intention–behavior gap , 2017 .

[21]  Panagiotis Adamopoulos,et al.  What makes a great MOOC? An interdisciplinary analysis of student retention in online courses , 2013, ICIS.

[22]  Marco Kalz,et al.  To Change or Not to Change? That's the Question... On MOOC-Success, Barriers and Their Implications , 2017, EMOOCs.

[23]  A. Espasa,et al.  Setting-up a European cross-provider data collection on open online courses , 2015 .

[24]  Janette R. Hill,et al.  Improving online learning: Student perceptions of useful and challenging characteristics , 2004, Internet High. Educ..

[25]  Steven R. Aragon,et al.  Factors Influencing Completion and Noncompletion of Community College Online Courses , 2008 .

[26]  Sean B. Eom,et al.  The Determinants of Students' Perceived Learning Outcomes and Satisfaction in University Online Education: An Empirical Investigation* , 2006 .