Understanding MOOC students: motivations and behaviours indicative of MOOC completion B.K. Pursel,* L. Zhang,† K.W. Jablokow,‡ G.W. Choi§ & D. Velegol¶ *Education Technology Services and Center for Online Innovations in Learning, The Pennsylvania State University, USA †Department of Education Policy Studies, The Pennsylvania State University, USA ‡Penn State Great Valley School of Graduate Professional Studies, The Pennsylvania State University, USA §Department of Learning and Performance Systems, The Pennsylvania State University, USA ¶Department of Chemical Engineering, The Pennsylvania State University, USA Abstract Massive open online courses (MOOCs) continue to appear across the higher education land- scape, 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 deliv- ery model is very different from traditional, fee-based models, such as college courses. This re- search 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. Keywords completion, engagement, learning analytics, MOOC, motivation, persistence. Introduction Higher education is at an interesting crossroads. In the USA, tuition continues to rise, as the average family income remains relatively flat (Lewin, 2008). The tradi- tional model of higher education appears unsustainable from a financial standpoint. While this traditional model seems poised for adaptation, emerging models and initia- tives, often enabled through technology, present interest- ing opportunities for experimentation. One of these emerging models is the massive open online course or MOOC. The MOOC provides a model for delivering courses at an unprecedented scale, both in terms of student numbers and in terms of global reach. These benefits are not with- out their corresponding challenges, however, including student retention and course completion in this non- traditional learning environment (Jordan, 2014). We currently know little about why some MOOC students complete a course (often signified by a statement of ac- complishment), while others never visit the course website after registration. The field of learning analytics (Siemens, 2010) provides methods and strategies that might help us better understand specific characteristics and behaviours that lead to students completing a MOOC, which can then influence course designs and tool development to support that completion. By leverag- ing a learning analytics approach, this research aims to investigate the variables that are indicative of MOOC students completing a MOOC. Research question This study set out to answer one primary research ques- tion: What variables are indicative of student completion in a MOOC? The data included in the study were col- lected from a student survey and then combined with stu- dent interaction data from a specific MOOC. Using logistic regression, we hoped to identify a list of motiva- tions and behaviours that influence MOOC course Accepted: 13 May 2015 Correspondence: Bart K. Pursel, Education Technology Services and Center for Online Innovations in Learning, The Pennsylvania State Uni- versity, 309 Rider Building, University Park, PA 16802, USA. Email: bkp10@psu.edu © 2016 John Wiley & Sons Ltd Journal of Computer Assisted Learning (2016), 32, 202–217 202 doi: 10.1111/jcal.12131 Special issue