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Article: Use of Learning Analytics Data in Health Care-Related Educational Disciplines: Systematic Review

TitleUse of Learning Analytics Data in Health Care-Related Educational Disciplines: Systematic Review
Authors
Keywordse-learning
education
learning analytics
learning management systems
online learning
Issue Date2019
PublisherJournal of Medical Internet Research. The Journal's web site is located at http://www.jmir.org/
Citation
Journal of Medical Internet Research, 2019, v. 21 n. 2, p. article no. e11241 How to Cite?
AbstractBACKGROUND: While the application of learning analytics in tertiary education has received increasing attention in recent years, a much smaller number have explored its use in health care-related educational studies. OBJECTIVE: This systematic review aims to examine the use of e-learning analytics data in health care studies with regards to how the analytics is reported and if there is a relationship between e-learning analytics and learning outcomes. METHODS: We performed comprehensive searches of papers from 4 electronic databases (MEDLINE, EBSCOhost, Web of Science, and ERIC) to identify relevant papers. Qualitative studies were excluded from this review. Papers were screened by 2 independent reviewers. We selected qualified studies for further investigation. RESULTS: A total of 537 papers were screened, and 19 papers were identified. With regards to analytics undertaken, 11 studies reported the number of connections and time spent on e-learning. Learning outcome measures were defined by summative final assessment marks or grades. In addition, significant statistical results of the relationships between e-learning usage and learning outcomes were reported in 12 of the identified papers. In general, students who engaged more in e-learning resources would get better academic attainments. However, 2 papers reported otherwise with better performing students consuming less e-learning videos. A total of 14 papers utilized satisfaction questionnaires for students, and all were positive in their attitude toward e-learning. Furthermore, 6 of 19 papers reported descriptive statistics only, with no statistical analysis. CONCLUSIONS: The nature of e-learning activities reported in this review was varied and not detailed well. In addition, there appeared to be inadequate reporting of learning analytics data observed in over half of the selected papers with regards to definitions and lack of detailed information of what the analytic was recording. Although learning analytics data capture is popular, a lack of detail is apparent with regards to the capturing of meaningful and comparable data. In particular, most analytics record access to a management system or particular e-learning materials, which may not necessarily detail meaningful learning time or interaction. Hence, learning analytics data should be designed to record the time spent on learning and focus on key learning activities. Finally, recommendations are made for future studies. ©Albert KM Chan, Michael G Botelho, Otto LT Lam. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 13.02.2019.
Persistent Identifierhttp://hdl.handle.net/10722/277967
ISSN
2020 Impact Factor: 5.428
2020 SCImago Journal Rankings: 1.446
PubMed Central ID
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorChan, AKM-
dc.contributor.authorBotelho, MG-
dc.date.accessioned2019-10-04T08:04:50Z-
dc.date.available2019-10-04T08:04:50Z-
dc.date.issued2019-
dc.identifier.citationJournal of Medical Internet Research, 2019, v. 21 n. 2, p. article no. e11241-
dc.identifier.issn1438-8871-
dc.identifier.urihttp://hdl.handle.net/10722/277967-
dc.description.abstractBACKGROUND: While the application of learning analytics in tertiary education has received increasing attention in recent years, a much smaller number have explored its use in health care-related educational studies. OBJECTIVE: This systematic review aims to examine the use of e-learning analytics data in health care studies with regards to how the analytics is reported and if there is a relationship between e-learning analytics and learning outcomes. METHODS: We performed comprehensive searches of papers from 4 electronic databases (MEDLINE, EBSCOhost, Web of Science, and ERIC) to identify relevant papers. Qualitative studies were excluded from this review. Papers were screened by 2 independent reviewers. We selected qualified studies for further investigation. RESULTS: A total of 537 papers were screened, and 19 papers were identified. With regards to analytics undertaken, 11 studies reported the number of connections and time spent on e-learning. Learning outcome measures were defined by summative final assessment marks or grades. In addition, significant statistical results of the relationships between e-learning usage and learning outcomes were reported in 12 of the identified papers. In general, students who engaged more in e-learning resources would get better academic attainments. However, 2 papers reported otherwise with better performing students consuming less e-learning videos. A total of 14 papers utilized satisfaction questionnaires for students, and all were positive in their attitude toward e-learning. Furthermore, 6 of 19 papers reported descriptive statistics only, with no statistical analysis. CONCLUSIONS: The nature of e-learning activities reported in this review was varied and not detailed well. In addition, there appeared to be inadequate reporting of learning analytics data observed in over half of the selected papers with regards to definitions and lack of detailed information of what the analytic was recording. Although learning analytics data capture is popular, a lack of detail is apparent with regards to the capturing of meaningful and comparable data. In particular, most analytics record access to a management system or particular e-learning materials, which may not necessarily detail meaningful learning time or interaction. Hence, learning analytics data should be designed to record the time spent on learning and focus on key learning activities. Finally, recommendations are made for future studies. ©Albert KM Chan, Michael G Botelho, Otto LT Lam. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 13.02.2019.-
dc.languageeng-
dc.publisherJournal of Medical Internet Research. The Journal's web site is located at http://www.jmir.org/-
dc.relation.ispartofJournal of Medical Internet Research-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.subjecte-learning-
dc.subjecteducation-
dc.subjectlearning analytics-
dc.subjectlearning management systems-
dc.subjectonline learning-
dc.titleUse of Learning Analytics Data in Health Care-Related Educational Disciplines: Systematic Review-
dc.typeArticle-
dc.identifier.emailBotelho, MG: botelho@hkucc.hku.hk-
dc.identifier.authorityBotelho, MG=rp00033-
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.2196/11241-
dc.identifier.pmid30758291-
dc.identifier.pmcidPMC6391646-
dc.identifier.scopuseid_2-s2.0-85061480056-
dc.identifier.hkuros306945-
dc.identifier.volume21-
dc.identifier.issue2-
dc.identifier.spagearticle no. e11241-
dc.identifier.epagearticle no. e11241-
dc.identifier.isiWOS:000458873400001-
dc.publisher.placeCanada-
dc.identifier.issnl1438-8871-

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