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Towards the Differentiation of Initial and Final Retention in Massive Open Online Courses

Al-Shabandar, R and Hussain, A and Laws, A and Keight, R and Lunn, J Towards the Differentiation of Initial and Final Retention in Massive Open Online Courses. Lecture Notes in Computer Science. ISSN 0302-9743 (Accepted)

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Abstract

Following an accelerating pace of technological change, Massive Open Online Courses (MOOCs) have emerged as a popular educational delivery platform, leveraging ubiqui-tous connectivity and computing power to overcome longstanding geographical and financial barriers to education. Consequently, the demographic reach of education delivery is extended towards a global online audience, facilitating learning and development for a continually ex-panding portion of the world population. However, an extensive literature review indicates that the low completion rate is the major issue related to MOOCs. Due to a lack of in-person inter-action between instructors and learners in such courses, the ability of tutors to monitor learners is impaired, often leading to learner withdrawals. To address this problem, learner drop out patterns across five courses offered by Harvard and MIT universities are investigated in this paper. Learning Analytics is applied to address key factors behind participant dropout events through the comparison of attrition during the first and last weeks of each course. The results show that the number of attired participants during the first week of the course is higher than during the last week, low percentages of attired learners are found prior to course closing dates. It is indicated therefore that assessment fees may not represent a significant reason for learners withdrawal. We introduce supervised machine learning algorithms for the analysis of learner retention and attrition within MOOC platform. Results show that machine learning represents a viable direction for the predictive analysis of MOOCs, with highest performances yielded by Boosted Tree classification for initial attrition and Neural Network based classification for final attrition.

Item Type: Article
Additional Information: The final publication is available at Springer via http://dx.doi.org/[insert DOI]”.
Uncontrolled Keywords: 08 Information And Computing Sciences
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Computer Science
Publisher: Springer Verlag (Germany)
Date Deposited: 05 May 2017 08:25
Last Modified: 07 Sep 2017 18:47
URI: http://researchonline.ljmu.ac.uk/id/eprint/6361

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