Examining student characteristics, goals, and engagement in Massive Open Online Courses

Abstract Massive Open Online Courses (MOOCs) have emerged with much popularity in the last five years, yet many questions remain about whom MOOCs best serve and what constitutes learner success. Completion rates, a common metric of student success, remain low, averaging less than 8%, and may be a misleading measure of success unless learner intentions are considered. This research addresses the relationships among learner characteristics and goals for enrolling in MOOCs, and the impacts on student persistence and completion in varying disciplines. We examined learner self-reported goals for taking a MOOC, characteristics, and rate of completion of 15,655 participants in eight MOOC courses. Results revealed that while age was positively associated with MOOC participation, motivation differed across course disciplines. The relationship between learner goals and engagement differed between those enrolled in Humanities/Liberal Arts (HLA) and STEM courses. Most notably, while taking the course due to personal interest or usefulness to a participant's career held a positive relationship with engagement in HLA courses, the endorsement of these same goals was predictive of less engagement in STEM courses. Our findings indicate that learner goals impact engagement and success, and that there are differences in engagement and goals between course disciplines. Suggestions for future MOOC research and potential course improvement to better align with learner goals are also provided.

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

[2]  Judith Ramsay,et al.  Massive open online courses (MOOCs): Insights and challenges from a psychological perspective , 2015, Br. J. Educ. Technol..

[3]  Yoav Bergner,et al.  Who does what in a massive open online course? , 2014, Commun. ACM.

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

[5]  Li Yuan,et al.  MOOCs and open education: Implications for higher education , 2013 .

[6]  Alan Agresti,et al.  Categorical Data Analysis , 1991, International Encyclopedia of Statistical Science.

[7]  A. Elliot,et al.  A HIERARCHICAL MODEL OF APPROACH AND AVOIDANCE ACHIEVEMENT MOTIVATION , 1997 .

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

[9]  K. Hew,et al.  Students’ and instructors’ use of massive open online courses (MOOCs): Motivations and challenges , 2014 .

[10]  Anne Trumbore,et al.  Rules of Engagement: Strategies to Increase Online Engagement at Scale , 2014 .

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

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

[13]  René F. Kizilcec,et al.  Motivation as a Lens to Understand Online Learners , 2015, ACM Trans. Comput. Hum. Interact..

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

[15]  Linda Corrin,et al.  Visualizing patterns of student engagement and performance in MOOCs , 2014, LAK.

[16]  Katy Mahraj Using Information Expertise to Enhance Massive Open Online Courses , 2012 .

[17]  M. Prensky Digital Natives, Digital Immigrants Part 1 , 2001 .

[18]  E. Deci,et al.  Intrinsic and Extrinsic Motivations: Classic Definitions and New Directions. , 2000, Contemporary educational psychology.