Conference Paper: Using Log Data to Evaluate MOOC Engagement and Inform Instructional Design

TitleUsing Log Data to Evaluate MOOC Engagement and Inform Instructional Design
Authors
KeywordsMOOCs
Log Data
Learning Analytics
Learning Design
Issue Date2019
PublisherSociety for Learning Analytics Research (SoLAR) .
Citation
Companion Proceedings 9th International Conference on Learning Analytics & Knowledge (LAK19), Tempe, Arizona, USA, 4-8 March 2019, p. 646-655 How to Cite?
AbstractTraditional educational studies verify the performance of courses through questionnaires, interviews and observations, which can be an arduous task for researchers. It is easier to verify the effectiveness of online courses as all the interactions between students and the courseware are recorded. However, the utilization of these activity data is lack of theoretical framework. In this paper, we propose to utilize learning interaction theory and web analytics knowledge to evaluate MOOC engagement and inform instructional design. This framework is composed of learner-interface, learner-content and learner-community interaction. 15 indicators derived from web analytics are proposed to help teachers better understand the engagement level of their courses in three interaction dimensions. To illustrate how the above analysis can facilitate teaching in practice, we used log data of 10 MOOCs owned by The University of Hong Kong on edX. Results and corresponding insights are offered. 10 experts are invited to evaluate the proposed framework. Most of them have showed positive attitudes. In the future, we will cooperate with MOOC designers and verify whether this framework can help them teaching and improve MOOC engagement.
DescriptionLAK 19 Workshop - Extracting evidence in the context of MOOCs
Persistent Identifierhttp://hdl.handle.net/10722/276290

 

DC FieldValueLanguage
dc.contributor.authorChai, Y-
dc.contributor.authorLei, CU-
dc.contributor.authorKwok, YK-
dc.date.accessioned2019-09-10T02:59:55Z-
dc.date.available2019-09-10T02:59:55Z-
dc.date.issued2019-
dc.identifier.citationCompanion Proceedings 9th International Conference on Learning Analytics & Knowledge (LAK19), Tempe, Arizona, USA, 4-8 March 2019, p. 646-655-
dc.identifier.urihttp://hdl.handle.net/10722/276290-
dc.descriptionLAK 19 Workshop - Extracting evidence in the context of MOOCs-
dc.description.abstractTraditional educational studies verify the performance of courses through questionnaires, interviews and observations, which can be an arduous task for researchers. It is easier to verify the effectiveness of online courses as all the interactions between students and the courseware are recorded. However, the utilization of these activity data is lack of theoretical framework. In this paper, we propose to utilize learning interaction theory and web analytics knowledge to evaluate MOOC engagement and inform instructional design. This framework is composed of learner-interface, learner-content and learner-community interaction. 15 indicators derived from web analytics are proposed to help teachers better understand the engagement level of their courses in three interaction dimensions. To illustrate how the above analysis can facilitate teaching in practice, we used log data of 10 MOOCs owned by The University of Hong Kong on edX. Results and corresponding insights are offered. 10 experts are invited to evaluate the proposed framework. Most of them have showed positive attitudes. In the future, we will cooperate with MOOC designers and verify whether this framework can help them teaching and improve MOOC engagement.-
dc.languageeng-
dc.publisherSociety for Learning Analytics Research (SoLAR) .-
dc.relation.ispartof9th International Conference on Learning Analytics & Knowledge (LAK19)-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.subjectMOOCs-
dc.subjectLog Data-
dc.subjectLearning Analytics-
dc.subjectLearning Design-
dc.titleUsing Log Data to Evaluate MOOC Engagement and Inform Instructional Design-
dc.typeConference_Paper-
dc.identifier.emailChai, Y: yqchai@hku.hk-
dc.identifier.emailLei, CU: culei@hku.hk-
dc.identifier.emailKwok, YK: ykwok@hku.hk-
dc.identifier.authorityLei, CU=rp01908-
dc.identifier.authorityKwok, YK=rp00128-
dc.description.naturepublished_or_final_version-
dc.identifier.hkuros303013-
dc.identifier.spage646-
dc.identifier.epage655-
dc.publisher.placeTempe, Arizona, USA-

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