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- Publisher Website: 10.1111/eje.12428
- Scopus: eid_2-s2.0-85061989366
- PMID: 30714273
- WOS: WOS:000475475400005
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Article: Self-Regulated Learning strategies in world's first MOOC in Implant Dentistry
Title | Self-Regulated Learning strategies in world's first MOOC in Implant Dentistry |
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Authors | |
Keywords | elearning implant dentistry massive open online course self regulated learning |
Issue Date | 2019 |
Publisher | Wiley-Blackwell Publishing, Inc. The Journal's web site is located at http://onlinelibrary.wiley.com/journal/10.1111/(ISSN)1600-0579 |
Citation | European Journal of Dental Education, 2019, v. 23 n. 3, p. 278-285 How to Cite? |
Abstract | Background Massive Open Online Courses (MOOCs) have been often described as a major innovation Higher Education, but their application in the teaching of clinical disciplines is still very limited, while there is a lack of scientific evaluations in this domain. The aim of this study was to investigate learners' behaviours and correlate patterns of Self‐Regulated Learning (SRL) with performance and achievement during a MOOC in Implant Dentistry. Methods The clickstream data of learners from the first run of the MOOC Implant dentistry by The University of Hong Kong were modelled and quantified based on Zimmerman's SRL model. The data were quantitatively analysed by means of k‐means clustering for evidence of five SRL behavioural indicators of student activity. The clusters identified were then correlated with student engagement and learning performance. Results A total of 7608 individuals enrolled, 5014 engaged (active learners 65.90%), 1277 of them (25.47%) completed the course and 1232 purchased a certificate. Two major groups of learners emerged: Attentive (n = 1433) who were more likely to follow the prescribed pathway in the MOOC and Auditors (n = 3581) who accessed content selectively. There was significantly higher engagement, achievement and completion rates among Attentive than Auditors. Both groups included subcategories (Browser, Digger, Test‐driven, Sampler, Persistent) which might reflect different SRL strategies. Conclusion A MOOC in a clinical discipline can achieve high enrolment and completion rates as compared to current benchmarks. There appears to be a wide diversity of learning behaviours among learners, with two however dominant patterns. Learners with a linear learning pathway achieved significantly higher grades and completion rates than those who accessed content irregularly and selectively. Such differences however might be influenced by the demographic and professional background of the learner, as well as their motivation to attending the MOOC. Certain learning behaviours, in particular how learners access content in relation to assessments might be closer related to SRL. |
Persistent Identifier | http://hdl.handle.net/10722/278088 |
ISSN | 2023 Impact Factor: 1.7 2023 SCImago Journal Rankings: 0.633 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | LAN, M | - |
dc.contributor.author | Hou, X | - |
dc.contributor.author | Qi, X | - |
dc.contributor.author | Mattheos, N | - |
dc.date.accessioned | 2019-10-04T08:07:16Z | - |
dc.date.available | 2019-10-04T08:07:16Z | - |
dc.date.issued | 2019 | - |
dc.identifier.citation | European Journal of Dental Education, 2019, v. 23 n. 3, p. 278-285 | - |
dc.identifier.issn | 1396-5883 | - |
dc.identifier.uri | http://hdl.handle.net/10722/278088 | - |
dc.description.abstract | Background Massive Open Online Courses (MOOCs) have been often described as a major innovation Higher Education, but their application in the teaching of clinical disciplines is still very limited, while there is a lack of scientific evaluations in this domain. The aim of this study was to investigate learners' behaviours and correlate patterns of Self‐Regulated Learning (SRL) with performance and achievement during a MOOC in Implant Dentistry. Methods The clickstream data of learners from the first run of the MOOC Implant dentistry by The University of Hong Kong were modelled and quantified based on Zimmerman's SRL model. The data were quantitatively analysed by means of k‐means clustering for evidence of five SRL behavioural indicators of student activity. The clusters identified were then correlated with student engagement and learning performance. Results A total of 7608 individuals enrolled, 5014 engaged (active learners 65.90%), 1277 of them (25.47%) completed the course and 1232 purchased a certificate. Two major groups of learners emerged: Attentive (n = 1433) who were more likely to follow the prescribed pathway in the MOOC and Auditors (n = 3581) who accessed content selectively. There was significantly higher engagement, achievement and completion rates among Attentive than Auditors. Both groups included subcategories (Browser, Digger, Test‐driven, Sampler, Persistent) which might reflect different SRL strategies. Conclusion A MOOC in a clinical discipline can achieve high enrolment and completion rates as compared to current benchmarks. There appears to be a wide diversity of learning behaviours among learners, with two however dominant patterns. Learners with a linear learning pathway achieved significantly higher grades and completion rates than those who accessed content irregularly and selectively. Such differences however might be influenced by the demographic and professional background of the learner, as well as their motivation to attending the MOOC. Certain learning behaviours, in particular how learners access content in relation to assessments might be closer related to SRL. | - |
dc.language | eng | - |
dc.publisher | Wiley-Blackwell Publishing, Inc. The Journal's web site is located at http://onlinelibrary.wiley.com/journal/10.1111/(ISSN)1600-0579 | - |
dc.relation.ispartof | European Journal of Dental Education | - |
dc.subject | elearning | - |
dc.subject | implant dentistry | - |
dc.subject | massive open online course | - |
dc.subject | self regulated learning | - |
dc.title | Self-Regulated Learning strategies in world's first MOOC in Implant Dentistry | - |
dc.type | Article | - |
dc.identifier.email | Hou, X: hxiangyu@hku.hk | - |
dc.identifier.email | Qi, X: Andreaq@hku.hk | - |
dc.identifier.email | Mattheos, N: mattheos@hku.hk | - |
dc.identifier.authority | Mattheos, N=rp01662 | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1111/eje.12428 | - |
dc.identifier.pmid | 30714273 | - |
dc.identifier.scopus | eid_2-s2.0-85061989366 | - |
dc.identifier.hkuros | 306059 | - |
dc.identifier.volume | 23 | - |
dc.identifier.issue | 3 | - |
dc.identifier.spage | 278 | - |
dc.identifier.epage | 285 | - |
dc.identifier.isi | WOS:000475475400005 | - |
dc.publisher.place | Denmark | - |
dc.identifier.issnl | 1396-5883 | - |