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Temporal dynamics of MOOC learning trajectories

Published: 01 October 2018 Publication History

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Abstract

Massive Open Online Courses (MOOCs) are a relatively new online learning phenomenon, whereby in 2017 more than 81 million learners have followed around 9,400 courses offered by more than 800 universities. Learners' retention has been one of the most vital issues associated with MOOC learning. A large body of literature can be found addressing various aspects of retention. However, few studies have examined the temporal aspects of learning processes, and why some learners complete only a few learning activities before dropping out, while others persist over time. Little is known about the nature and level of participation, or learners' progression in ordered learning activities in MOOCs, i.e., learners' learning pathways. This study aims to fill this gap in knowledge by analyzing an Open University MOOC offered via FutureLearn platform. Using exploratory methods associated with Educational Process Mining (EPM) on system logs, the study explored self-allocated time that 2,086 learners assigned to a variety of learning activities. Learners' activities were mapped to identify common and distinct learning pathways. Analyses were performed on two distinct groups of learners: Completers and Non-Completers. Using the measure of relative frequencies, the study compared participatory behaviors of both groups with expected learning behavior for all types of learning activities. Also, we explored typical weekly performance, identified and mapped most significant temporal learning pathways of subgroup of learners. The results indicated that at least one main and dominating pathway existed, but paths of dominant subgroups of Completers and Non-Completers remained noticeably distinct. We concluded the paper with practical implications and limitations of using process mining methods for temporal behavioral modeling in educational domains. Future research directions and potential benefits of such temporal modeling are also discussed.

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  • (2024)Learner Engagement and Demographic Influences in Brazilian Massive Open Online Courses: Aprenda Mais Platform Case StudyAnalytics10.3390/analytics30200103:2(178-193)Online publication date: 3-Apr-2024
  • (2024)Analysis and discovery of procrastination patterns in a language learning MOOCComputers & Education10.1016/j.compedu.2024.105154(105154)Online publication date: Sep-2024
  • (2022)Unfolding the learning behaviour patterns of MOOC learners with different levels of achievementInternational Journal of Educational Technology in Higher Education10.1186/s41239-022-00328-819:1Online publication date: 3-May-2022
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cover image ACM Other conferences
DATA '18: Proceedings of the First International Conference on Data Science, E-learning and Information Systems
October 2018
274 pages
ISBN:9781450365369
DOI:10.1145/3279996
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 October 2018

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Author Tags

  1. MOOCs
  2. behavioral analysis
  3. educational process mining
  4. temporal modelling

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  • Research-article

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  • everhulme Trust, Open World Learning

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DATA '18

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Cited By

View all
  • (2024)Learner Engagement and Demographic Influences in Brazilian Massive Open Online Courses: Aprenda Mais Platform Case StudyAnalytics10.3390/analytics30200103:2(178-193)Online publication date: 3-Apr-2024
  • (2024)Analysis and discovery of procrastination patterns in a language learning MOOCComputers & Education10.1016/j.compedu.2024.105154(105154)Online publication date: Sep-2024
  • (2022)Unfolding the learning behaviour patterns of MOOC learners with different levels of achievementInternational Journal of Educational Technology in Higher Education10.1186/s41239-022-00328-819:1Online publication date: 3-May-2022
  • (2022)Adaptive Empathy Learning Support in Peer Review ScenariosProceedings of the 2022 CHI Conference on Human Factors in Computing Systems10.1145/3491102.3517740(1-17)Online publication date: 29-Apr-2022
  • (2022)Raising student engagement using digital nudges tailored to students' motivation and perceived ability levelsBritish Journal of Educational Technology10.1111/bjet.1326154:2(554-580)Online publication date: 30-Jul-2022
  • (2022)Systematic Literature Review on Process Mining in Learning Management System2022 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT)10.1109/IAICT55358.2022.9887428(160-166)Online publication date: 28-Jul-2022
  • (2022)Profiling temporal learning interests with time-aware transformers and knowledge graph for online course recommendationElectronic Commerce Research10.1007/s10660-022-09541-z23:4(2357-2377)Online publication date: 3-Mar-2022
  • (2021)Temporal flexibility, gender, and online learning completionDistance Education10.1080/01587919.2020.186952342:1(22-36)Online publication date: 8-Feb-2021
  • (2019)Investigating variation in learning processes in a FutureLearn MOOCJournal of Computing in Higher Education10.1007/s12528-019-09231-032:1(162-181)Online publication date: 14-Jun-2019

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