An Analysis of Course Characteristics, Learner Characteristics, and Certification Rates in MITx MOOCs

Abstract

Massive Open Online Courses (MOOCs), capable of providing free (or low cost) courses for millions of learners anytime and anywhere, have gained the attention of researchers, educational institutions, and learners worldwide. Even though they provide several benefits, there are still some criticisms of MOOCs. For instance, MOOCs’ high dropout rates or predominantly elite participation are considered to be important problems. In order to develop solutions for these problems, a deeper understanding of MOOCs is required. Today, despite the availability of several research studies about MOOCs, there is a shortage of in-depth research on course characteristics, learner characteristics, and predictors of certification rates. This study examined MOOC and learner characteristics in detail and explored the predictors of course certification rates based on data from 122 Massachusetts Institute of Technology MOOCs (MITx) on edX platform as well as data about the 2.8 million participants registered in these MOOCs. The results indicated that as the number of courses offered and the number of learners enrolled increased in years, there was a decrease in the certification rates among enrolled learners. According to our results, the number of average chapters completed, total forum messages, and mean age predicted course certification rates positively. On the other hand, the total number of chapters in a course predicted the course certification rates negatively. Based on these results, shorter and more interactive MOOCs are recommended by considering the needs of the learners, course content design, and strategies encouraging the enrolled students to enter the courses.

A Study on the Influencing Factors of Continued Intention to Use MOOCs: UTAUT Model and CCC Moderating Effect

“…One of the most important characteristics of MOOCs is flexibility of self-arrangement of learning schedule by making use of the contents and resources of MOOCs (Bruff et al, 2013). Since most MOOCs support autonomous learning and in essence, it can realize sub-synchronous learning, which makes MOOCs learners often fail to receive direct feedback from teachers and have certain barriers to interaction with others (Kop et al, 2011).…”

“…Although MOOCs were held up as an educational innovation (Joo et al, 2018) and had experienced rapid development in recent years (Konstan et al, 2015), there were still some criticisms of MOOCs (Cagiltay et al, 2020). For instance, MOOCs are facing a serious problem of low completion rates (Macleod et al, 2015;Li et al, 2018) or high dropout rates (Cagiltay et al, 2020). According to some estimates, only 15% of registrants were able to complete the course (Arpaci et al, 2020).…”

Measuring the effects of course modularizations in online courses for life-long learners

“…She highlights that the modularization of MOOCs has been suggested previously (Bol cited in Harvard Magazine Online, 2013;Challen and Seltzer, 2014), but notes that further research is required to identify the effects of course modules (Jordan, 2015a). In another study with 122 MITx courses offered between 2012 and 2016, no direct correlation between the completion rate and the course length but rather the modules was reported, as well as a negative correlation between the number of modules in a course and the certification rate (Celik et al, 2020). Shorter course modules are also expected to introduce enhanced flexibility for learners as well as for instructors (Bol cited in Harvard Magazine Online, 2013).…”

A Systematic Review of Questionnaire-Based Quantitative Research on MOOCs

“…Data mining and deep learning emphasize correlation judgments between samples and infer the population from the standard data set (Peral et al, 2017). What is more, researchers can apply data mining and deep learning to analyze objective behaviors and subjective perceptions of MOOC learners and instructors, make feature profiles of users, and propose personalized optimization schemes (Geng et al, 2020;Cagiltay et al, 2020…”