Dropout prediction in a massive open online course using learning analytics

Anat Cohen, Udi Shimony

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Analysis of the data collected in Massive Open Online Courses (MOOCs) allows for the assessment of massive learning processes and behavior. Many criticize MOOCs for their high rate of dropout. In this study, a model was developed for early identification of learners at risk of dropping out. Due to various motivations for MOOC registration, dropout is defined as termination of participation before achieving the learner aims and purposes. This model is based on learning behavior variables and monthly alerts, which indicate patterns of activity and behavior that may lead to dropout. Five types of learners with similar behavior were identified; non-active learners, video-based learners, video and assignment-based learners, assignment-oriented learners, and active learners. A statistically significant model resulting from a linear regression analysis, explains 45% of the learner achievement variance. Early recognition of dropouts may assist in identifying those who require support.
Original languageEnglish
Title of host publicationProceedings of E-Learn
Subtitle of host publicationWorld Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education 2016
Place of PublicationSan Diego
PublisherAssociation for the Advancement of Computing in Education (AACE)
Pages616-625
Number of pages10
ISBN (Print)978-1-939797-25-4
StatePublished - 1 Nov 2016
EventE-Learn: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education - Washington, United States
Duration: 14 Nov 201616 Nov 2016

Conference

ConferenceE-Learn
Country/TerritoryUnited States
CityWashington
Period14/11/1616/11/16

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