Exploring Demographics and Students’ Motivation as Predictors of Completion of a Massive Open Online Course

Abstract

This paper investigates the degree to which different variables affect the completion of a Massive Open Online Course (MOOC). Data on those variables, such as age, gender, English proficiency, education level, and motivation for course enrollment were first collected through a pre-course survey. Next, course completion records were collected via the Coursera database. Finally, multiple binomial logistic regression models were used to identify factors related to MOOC completion. Although students were grouped according to their preferences, working in groups did not affect students’ likelihood for MOOC completion. Also, other variables such as age, the institution hosting the MOOC, academic program alignment with students’ needs, and students’ intention to complete the course all affected their probability of MOOC completion. This study contributes to the literature by indicating the factors that influence the probability of MOOC completion. Results show that older participants (age > 50 years old) have higher probability of completing the MOOC. Students’ MOOC completion also increases when the MOOC provides experiences that add to students’ current academic backgrounds and when they are hosted by institutions with a strong academic reputation. Based on these factors, this study contributes to research methods in MOOCs by proposing a model that is aligned with the most important factors predicting completion as recommended by the current MOOC literature. For the next phase of assigning learners to work in groups, findings from this study also suggest that MOOC instructors should provide assistance for group work and monitor students’ collaborative processes.

MOOCs at the Crossroads: A Literature Review and Reflection Drawing upon Discourse Analysis

“…Moreover, some authors have identified different subgroups among MOOC-takers with different profiles in relation to engagement, completion rates, accomplishment, and learning outcomes. For example, Li and Baker (2018) found heterogeneity in behavioral patterns among learners that is expression of different levels of engagement, and different reasons why participants decide to engage (see also Walji, Deacon, Small, & Czerniewicz, 2016;Williams, Stafford, Corliss, & Reilly, 2018;Zhang, Cesar Bonafini, Lockee, Jablokow, & Hu, 2019). In sum, they conclude that in MOOCs the existence of self-defined learning pathways generates the need to apply different measures to discern the way learners are taking advantage of the content of the courses (see also Petronzi & Hadi, 2016;Reilly, Williams, Stafford, Corliss, Walkow, & Kidwell, 2016;Shapiro, Lee, C. H., Wyman Roth, Li, Çetinkaya-Rundel, & Canelas, 2017;Tseng, Tsao, Yu, Chan, & Lai, 2016).…”

MOOCs and Education Access: Evidence from New Zealand

“…A more recent study of students' motivation and their completions by Zhang et al (2019) used students' demographics and motivation as predictors of their completion in a MOOC.…”

Self-regulated learning with massive open online course (MOOC) for the fundamentals of data structure course: A descriptive analysis

“…However, given the positive impact, there are still some challenges reported in many kinds of literature focused on trying to understand the low completion rates for courses in MOOC platforms (Liu et al, 2019). Analysis of learner behavior in MOOCs mentioned that students who have got the certificate were majority viewed without deep understanding the contents, and some of them were completely skipping (Zhang et al, 2019). This is a pressing issue until recently, mainly for a difficult subject.…”