Antecedents of student MOOC revisit intention: Moderation effect of course difficulty

The relationship between course content vividness and intention to revisit are positively moderated by the course difficulty.The relationship between teacher subject knowledge and intention to revisit are negatively moderated by the course difficulty.The relationship between MOOC interactivity and intention to revisit is not significantly moderated by the course difficulty. In response to the research gap in the current literature regarding the low student retention of Massive Open Online Courses (MOOCs), this study uses task-technology fit theory to understand how MOOCs' technological factors in three dimensions (i.e., course vividness, teacher subject knowledge, and interactivity) influence students' revisiting of MOOCs. Going deeper, this study also takes course difficulty into consideration and investigates the interactive effects of course difficulty on the main factors identified above. The empirical results show that the vividness of course content, teacher subject knowledge, and MOOC interactivity can positively affect students' intention to revisit MOOCs. However, the relationships between the three dimensional factors and student intention to revisit are affected in different ways by course difficulty. Specifically, the findings show that course difficulty negatively moderates the relationship between course content vividness and students' intention to revisit, and positively moderates the relationship between teacher subject knowledge and students' intention to revisit. In addition, course difficulty typically does not have a significant influence on the relationship between technology interactivity and students' intention to revisit. Theoretical and practical implications are discussed.

[1]  A. St-Hilaire,et al.  Prenatal Factors in Schizophrenia , 2010 .

[2]  Mayur S. Desai,et al.  E-Learning: Paradigm Shift in Education , 2008 .

[3]  A. Bryman.  Social Research Methods , 2001 .

[4]  Jay F. Nunamaker,et al.  Can e-learning replace classroom learning? , 2004, CACM.

[5]  E. Higgins,et al.  Value From Regulatory Fit , 2005 .

[6]  Anastasios A. Economides,et al.  Continuance acceptance of computer based assessment through the integration of user's expectations and perceptions , 2013, Comput. Educ..

[7]  E. Higgins.  Promotion and Prevention: Regulatory Focus as A Motivational Principle , 1998 .

[8]  Kan-Min Lin,et al.  e-Learning continuance intention: Moderating effects of user e-learning experience , 2011, Comput. Educ..

[9]  Dale Goodhue,et al.  Task-Technology Fit and Individual Performance , 1995, MIS Q..

[10]  Robab Saadatdoost,et al.  Exploring MOOC from education and Information Systems perspectives: a short literature review , 2015 .

[11]  Bin Wang,et al.  What makes them happy and curious online? An empirical study on high school students' Internet use from a self-determination theory perspective , 2011, Comput. Educ..

[12]  Brian L. Massey,et al.  Interactivity, Online Journalism, and English-Language Web Newspapers in Asia , 1999 .

[13]  Weiguo Fan,et al.  Determinants of users' continuance of social networking sites: A self-regulation perspective , 2014, Inf. Manag..

[14]  Vasilis Gialamas,et al.  Student teachers' perceptions about the impact of internet usage on their learning and jobs , 2013, Comput. Educ..

[15]  D. Fortin,et al.  Interactivity and vividness effects on social presence and involvement with a web-based advertisement , 2005 .

[16]  J. Christopher Zimmer,et al.  Podcasting acceptance on campus: The differing perspectives of teachers and students , 2013, Comput. Educ..

[17]  Hangjung Zo,et al.  Understanding the MOOCs continuance: The role of openness and reputation , 2015, Comput. Educ..

[18]  James J. Kellaris,et al.  Cognitive Determinants of Consumers' Time Perceptions: The Impact of Resources Required and Available , 2003 .

[19]  Iris Vessey,et al.  The Role of Cognitive Fit in the Relationship Between Software Comprehension and Modification , 2006, MIS Q..

[20]  Timothy Teo,et al.  Can structured representation enhance students' thinking skills for better understanding of E-learning content? , 2013, Comput. Educ..

[21]  Saniye Tugba Bulu,et al.  Place presence, social presence, co-presence, and satisfaction in virtual worlds , 2012, Comput. Educ..

[22]  Xin Chen,et al.  Temporal predication of dropouts in MOOCs: Reaching the low hanging fruit through stacking generalization , 2016, Comput. Hum. Behav..

[23]  Angela Lin,et al.  The acceptance and use of a business-to-business information system , 2006, Int. J. Inf. Manag..

[24]  E. Thorson,et al.  The Effects of Progressive Levels of Interactivity and Vividness in Web Marketing Sites , 2001 .

[25]  Ming-Chi Lee,et al.  Explaining and predicting users' continuance intention toward e-learning: An extension of the expectation-confirmation model , 2010, Comput. Educ..

[26]  Jie Zhang,et al.  Can MOOCs be interesting to students? An experimental investigation from regulatory focus perspective , 2016, Comput. Educ..

[27]  Scott B. MacKenzie,et al.  Common method biases in behavioral research: a critical review of the literature and recommended remedies. , 2003, The Journal of applied psychology.

[28]  Rex Perez Bringula,et al.  Influence of faculty- and web portal design-related factors on web portal usability: A hierarchical regression analysis , 2013, Comput. Educ..

[29]  Eric T. G. Wang,et al.  Understanding Web-based learning continuance intention: The role of subjective task value , 2008, Inf. Manag..

[30]  Ana Ortiz de Guinea,et al.  Why break the habit of a lifetime? rethinking the roles of intention, habit, and emotion in continuing information technology use , 2009 .

[31]  Kai H. Lim,et al.  Understanding sustained participation in transactional virtual communities , 2012, Decis. Support Syst..

[32]  Allison Littlejohn,et al.  Learning in MOOCs: Motivations and self-regulated learning in MOOCs , 2016, Internet High. Educ..

[33]  R. Abbott,et al.  Beginning Literacy , 2002, Journal of learning disabilities.

[34]  Szu-Yuan Sun,et al.  An empirical analysis of the antecedents of web-based learning continuance , 2007, Comput. Educ..

[35]  Jane E. Klobas,et al.  A task-technology fit view of learning management system impact , 2009, Comput. Educ..

[36]  Hossam Haick,et al.  Motivation to learn in massive open online courses: Examining aspects of language and social engagement , 2016, Comput. Educ..

[37]  C. Fornell,et al.  Evaluating structural equation models with unobservable variables and measurement error. , 1981 .

[38]  Tharindu Rekha Liyanagunawardena,et al.  Massive Open Online Courses , 2015 .

[39]  Izak Benbasat,et al.  Assessing Screening and Evaluation Decision Support Systems: A Resource-Matching Approach , 2010, Inf. Syst. Res..

[40]  N. Selwyn,et al.  Massive Open Online Change? Exploring the Discursive Construction of the "MOOC" in Newspapers. , 2015 .

[41]  Hsueh-Hua Chuang,et al.  The development and validation of an instrument for assessing college students' perceptions of faculty knowledge in technology-supported class environments , 2013, Comput. Educ..

[42]  Eugene F. Stone.  Research methods in organizational behavior , 1978 .

[43]  Diane M. Strong,et al.  Extending the technology acceptance model with task-technology fit constructs , 1999, Inf. Manag..

[44]  Michael S. Roth.  My Modern Experience Teaching a MOOC. , 2013 .

[45]  Howard B. Lee,et al.  A first course in factor analysis , 1973 .

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

[47]  Douglas R. Vogel,et al.  How to satisfy citizens? Using mobile government to reengineer fair government processes , 2016, Decis. Support Syst..

[48]  Lorena Blasco-Arcas,et al.  Using clickers in class. The role of interactivity, active collaborative learning and engagement in learning performance , 2013, Comput. Educ..

[49]  Samar Zutshi,et al.  Experiences in MOOCs: The Perspective of Students , 2013 .

[50]  Fred G. Martin,et al.  Will massive open online courses change how we teach? , 2012, CACM.

[51]  Jinhee Kim,et al.  Interactivity and Persuasion , 2005 .

[52]  SHYAM SUNDAR,et al.  Explicating Web Site Interactivity , 2003, Commun. Res..

[53]  Hui-Min Lai,et al.  Factors influencing secondary school teachers' adoption of teaching blogs , 2011, Comput. Educ..

[54]  Victor Chang,et al.  Review and discussion: E-learning for academia and industry , 2016, Int. J. Inf. Manag..

[55]  Tung-Ching Lin,et al.  Understanding knowledge management system usage antecedents: An integration of social cognitive theory and task technology fit , 2008, Inf. Manag..

[56]  LeeDoo Young,et al.  User acceptance of YouTube for procedural learning , 2013 .

[57]  Jaeho Choi,et al.  A structural equation model of predictors of online learning retention , 2013, Internet High. Educ..

[58]  Mark R. Lehto,et al.  User acceptance of YouTube for procedural learning: An extension of the Technology Acceptance Model , 2013, Comput. Educ..

[59]  Sylvain Senecal,et al.  Is more always better? Investigating the task-technology fit theory in an online user context , 2014, Inf. Manag..

[60]  Izak Benbasat,et al.  Investigating the Influence of the Functional Mechanisms of Online Product Presentations , 2007 .

[61]  Fan-Chuan Tseng,et al.  Critical success factors for motivating and sustaining women's ICT learning , 2013, Comput. Educ..