Massive open online course completion rates revisited: Assessment, length and attrition

Abstract

This analysis is based upon enrolment and completion data collected for a total of 221 Massive Open Online Courses (MOOCs). It extends previously reported work (Jordan, 2014) with an expanded dataset; the original work is extended to include a multiple regression analysis of factors that affect completion rates and analysis of attrition rates during courses. Completion rates (defined as the percentage of enrolled students who completed the course) vary from 0.7% to 52.1%, with a median value of 12.6%. Since their inception, enrolments on MOOCs have fallen while completion rates have increased. Completion rates vary significantly according to course length (longer courses having lower completion rates), start date (more recent courses having higher percentage completion) and assessment type (courses using auto grading only having higher completion rates). For a sub-sample of courses where rates of active use and assessment submission across the course are available, the first and second weeks appear to be critical in achieving student engagement, after which the proportion of active students and those submitting assessments levels out, with less than 3% difference between them.

Refining success and dropout in massive open online courses based on the intention–behavior gap

“…She found that on average 6.5% of the students who enrolled in a MOOC met the certificate-earning criteria of the course. In a later study she found that the success rate reached a mean value of 12.6%, which entailed an average dropout rate of 87.4% (Jordan, 2015).…”

“…However, after a short time, this first excitement was followed by frustration. Despite its popularity, the number of MOOC-takers who actually completed a MOOC after enrollment was reported to be very low with dropout rates between 98 and 90% (Jordan, 2014(Jordan, , 2015Koller, Ng, Do, & Chen, 2013;Liyanagunawardena, Parslow, & Williams, 2014;Reich, 2014). Many researchers agreed that the learning circumstances in MOOCs are exceptional (Huin, Bergheaud, Caron, Codina, & Disson, 2016;Koller et al, 2013;Liyanagunawardena et al, 2014).…”

Learner Behaviour in a MOOC Practice-oriented Course: In Empirical Study Integrating TAM and TPB

“…These issues, as they relate to MOOCs, have been fully discussed and studied by numerous researchers (Lowenthal & Hodges, 2015;Rodriguez, 2012;Margaryan, Bianco, & Littlejohn, 2015;Jordan, 2015). Clearly, we are still early in the research that truly understands learner perception and attitude within the MOOC environment.…”

E-learning and blended learning in textile engineering education: a closed feedback loop approach

“…While the interactive and multimedia features of e-learning have been seen to attract the learners' attention to the learning content of the course, the retention of the initial interest and attention and the lasting engagement of the learners in the educational activities are objectives not fully conquered as yet. The impressively low percentage of learners subscribed to MOOCs, [7], that complete their courses (an average of 12% reported in 2015) is an aspect of the same problem, the MOOCs being typical examples of purely e-learning education. To address this weakness, modern e- learning platforms incorporate learning style recognition in adaptive learning platforms that can personalize both the learning content and the media through which this content is offered, [8], [9], narration, Animated Pedagogical Agents (APA), [10], [11], affective computing, [12], [13], gamification, [14], 3D or Virtual Reality environments, [15], and other such state-of-the-art technologies.…”