Dropout is not always a failure! Exploration on the prior knowledge and learning behaviors of MOOC learners

Massive open online courses (MOOCs) are highly beneficial to the public. However, much research demonstrated low completion numbers of MOOC learners. Several factors have been identified as influential factors in the success of MOOC learners. However, a few studies examined the extent to which prior...

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Published in:Journal of Computers in Education
Main Author: Matcha W.; Natthaphatwirata R.; Uzir N.A.; Gašević D.
Format: Article
Language:English
Published: Springer Science and Business Media Deutschland GmbH 2024
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85208812809&doi=10.1007%2fs40692-024-00340-z&partnerID=40&md5=93993496e43e7ad91f73e5f0ee52293d
id 2-s2.0-85208812809
spelling 2-s2.0-85208812809
Matcha W.; Natthaphatwirata R.; Uzir N.A.; Gašević D.
Dropout is not always a failure! Exploration on the prior knowledge and learning behaviors of MOOC learners
2024
Journal of Computers in Education


10.1007/s40692-024-00340-z
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85208812809&doi=10.1007%2fs40692-024-00340-z&partnerID=40&md5=93993496e43e7ad91f73e5f0ee52293d
Massive open online courses (MOOCs) are highly beneficial to the public. However, much research demonstrated low completion numbers of MOOC learners. Several factors have been identified as influential factors in the success of MOOC learners. However, a few studies examined the extent to which prior knowledge is associated with completion rates, learning outcomes, and patterns of student engagement. Hence, this paper aims to examine the relationships between prior knowledge, knowledge gains, and engagement patterns of MOOC learners. Specifically, this study used data mining techniques based on the patterns of learning performance. The Kruskal–Wallis test was used to examine the differences in terms of performance among the detected clusters. Process mining was then used to explore the learning process. The results demonstrated that five groups of learners were identified based on the patterns of their performance calculated from the score obtained from pre-tests (prior knowledge) and post-tests (learning gains) for each of the five topics, namely, Dropout, Stable, Progress, Late dropout, and Post attempt groups. Among the five groups, two of them exhibited different dropout behaviours, namely a) those who dropped out after scoring highly on the pre-test in the first topic in the MOOC and b) those who received relatively low scores in both pre- and post-tests on each topic they studied. This offers a novel insight into MOOC research while indicating that dropout may not be always associated with a lack of success. The study also demonstrated differences in the learning strategies that were adopted by different groups of learners. The key implication of this research is that the introduction of pre-tests as a priming strategy in MOOC design can have strong implications for decision-making related to learning and teaching. © Beijing Normal University 2024.
Springer Science and Business Media Deutschland GmbH
21979987
English
Article

author Matcha W.; Natthaphatwirata R.; Uzir N.A.; Gašević D.
spellingShingle Matcha W.; Natthaphatwirata R.; Uzir N.A.; Gašević D.
Dropout is not always a failure! Exploration on the prior knowledge and learning behaviors of MOOC learners
author_facet Matcha W.; Natthaphatwirata R.; Uzir N.A.; Gašević D.
author_sort Matcha W.; Natthaphatwirata R.; Uzir N.A.; Gašević D.
title Dropout is not always a failure! Exploration on the prior knowledge and learning behaviors of MOOC learners
title_short Dropout is not always a failure! Exploration on the prior knowledge and learning behaviors of MOOC learners
title_full Dropout is not always a failure! Exploration on the prior knowledge and learning behaviors of MOOC learners
title_fullStr Dropout is not always a failure! Exploration on the prior knowledge and learning behaviors of MOOC learners
title_full_unstemmed Dropout is not always a failure! Exploration on the prior knowledge and learning behaviors of MOOC learners
title_sort Dropout is not always a failure! Exploration on the prior knowledge and learning behaviors of MOOC learners
publishDate 2024
container_title Journal of Computers in Education
container_volume
container_issue
doi_str_mv 10.1007/s40692-024-00340-z
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85208812809&doi=10.1007%2fs40692-024-00340-z&partnerID=40&md5=93993496e43e7ad91f73e5f0ee52293d
description Massive open online courses (MOOCs) are highly beneficial to the public. However, much research demonstrated low completion numbers of MOOC learners. Several factors have been identified as influential factors in the success of MOOC learners. However, a few studies examined the extent to which prior knowledge is associated with completion rates, learning outcomes, and patterns of student engagement. Hence, this paper aims to examine the relationships between prior knowledge, knowledge gains, and engagement patterns of MOOC learners. Specifically, this study used data mining techniques based on the patterns of learning performance. The Kruskal–Wallis test was used to examine the differences in terms of performance among the detected clusters. Process mining was then used to explore the learning process. The results demonstrated that five groups of learners were identified based on the patterns of their performance calculated from the score obtained from pre-tests (prior knowledge) and post-tests (learning gains) for each of the five topics, namely, Dropout, Stable, Progress, Late dropout, and Post attempt groups. Among the five groups, two of them exhibited different dropout behaviours, namely a) those who dropped out after scoring highly on the pre-test in the first topic in the MOOC and b) those who received relatively low scores in both pre- and post-tests on each topic they studied. This offers a novel insight into MOOC research while indicating that dropout may not be always associated with a lack of success. The study also demonstrated differences in the learning strategies that were adopted by different groups of learners. The key implication of this research is that the introduction of pre-tests as a priming strategy in MOOC design can have strong implications for decision-making related to learning and teaching. © Beijing Normal University 2024.
publisher Springer Science and Business Media Deutschland GmbH
issn 21979987
language English
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