Exploring the factors affecting MOOC retention: A survey study

By Kate S. Hone, Ghada Refaat El Said. Comput. Educ., 2016

667 citations recorded by the source corpus.

Massive Open Online Courses (MOOCs) hold the potential to open up educational opportunities to a global audience. However, evidence suggests that only a small proportion of MOOC participants go on to complete their courses and relatively little is understood about the MOOC design and implementation factors that influence retention. This paper reports a survey study of 379 participants enrolled at university in Cairo who were encouraged to take a MOOC of their own choice as part of their development. 122 participants (32.2%) went onto to complete an entire course. There were no significant differences in completion rates by gender, level of study (undergraduate or postgraduate) or MOOC platform. A post-MOOC survey of students' perceptions found that MOOC Course Content was a significant predictor of MOOC retention, with the relationship mediated by the effect of content on the Perceived Effectiveness of the course. Interaction with the instructor of the MOOC was also found to be significant predictor of MOOC retention. Overall these constructs explained 79% of the variance in MOOC retention. We present a model explaining 79% of variance in MOOC learner retention.Course content affects MOOC learner retention via Perceived Effectiveness.Interaction with instructor affects MOOC learner retention directly.Those who pass the mid-point of a MOOC are likely to complete.

Subfields: Computer Science Applications, Education

View Paper: https://www.semanticscholar.org/paper/dbcade6e8f05655e55e0f7b9a58088855814cab0

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Science Applications

1. J. Goopio
2. Catherine Cheung
3. M. Yağcı

In Education

1. J. Goopio
2. Catherine Cheung

Citations in the corpus

• Predicting academic performance of students from VLE big data using deep learning models (2020, background): “In the literature, there has been substantial debate on the subject of student retention in MOOCs.”
• The MOOC dropout phenomenon and retention strategies (2020, background): “…have complained of poor course design, overwhelming course content, high learning workload, poor course structure, long and boring videos, long course duration, and unchallenging activities (Eriksson et al., 2017; Gomez-Zermeno & Aleman de La Garza, 2016; Hone & El Said, 2016; El Said, 2017).”