Toward evidence-based learning analytics: Using proxy variables to improve asynchronous online discussion environments.
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| Title: | Toward evidence-based learning analytics: Using proxy variables to improve asynchronous online discussion environments. |
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| Authors: | Kim, Dongho1 dongho@uga.edu, Park, Yeonjeong2,3 ypark@honam.ac.kr, Yoon, Meehyun1 meehyun@uga.edu, Jo, Il-Hyun2,3 ijo@ewha.ac.kr |
| Source: | Internet & Higher Education. Jul2016, Vol. 30, p30-43. 14p. |
| Subject Terms: | *Blended learning, *Discussion, Empirical research, Data mining, Web analytics |
| Abstract: | Although asynchronous online discussion (AOD) is increasingly used as a main activity for blended learning, many students find it difficult to engage in discussions and report low achievement. Early prediction and timely intervention can help potential low achievers get back on track as early as possible. This study presented a data mining process to construct proxy variables that reflect theoretical and empirical evidence and measured the accuracy of a prediction model that incorporated all of the variables for validation. For the empirical study, data were obtained from 105 university students who were enrolled in two blended learning courses that used AOD as their main activity. The results indicated the high accuracy of the prediction model as well as the possibility of early detection and timely interventions. In addition, we examined participants' learning behaviors in the two courses using the proxy variables and provided suggestions for practice. The implications of this study for education data mining and learning analytics are discussed. [ABSTRACT FROM AUTHOR] |
| Copyright of Internet & Higher Education is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Education Research Complete |
| FullText | Text: Availability: 0 |
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| Header | DbId: ehh DbLabel: Education Research Complete An: 115824012 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Toward evidence-based learning analytics: Using proxy variables to improve asynchronous online discussion environments. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Kim%2C+Dongho%22">Kim, Dongho</searchLink><relatesTo>1</relatesTo><i> dongho@uga.edu</i><br /><searchLink fieldCode="AR" term="%22Park%2C+Yeonjeong%22">Park, Yeonjeong</searchLink><relatesTo>2,3</relatesTo><i> ypark@honam.ac.kr</i><br /><searchLink fieldCode="AR" term="%22Yoon%2C+Meehyun%22">Yoon, Meehyun</searchLink><relatesTo>1</relatesTo><i> meehyun@uga.edu</i><br /><searchLink fieldCode="AR" term="%22Jo%2C+Il-Hyun%22">Jo, Il-Hyun</searchLink><relatesTo>2,3</relatesTo><i> ijo@ewha.ac.kr</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Internet+%26+Higher+Education%22">Internet & Higher Education</searchLink>. Jul2016, Vol. 30, p30-43. 14p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Blended+learning%22">Blended learning</searchLink><br />*<searchLink fieldCode="DE" term="%22Discussion%22">Discussion</searchLink><br /><searchLink fieldCode="DE" term="%22Empirical+research%22">Empirical research</searchLink><br /><searchLink fieldCode="DE" term="%22Data+mining%22">Data mining</searchLink><br /><searchLink fieldCode="DE" term="%22Web+analytics%22">Web analytics</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Although asynchronous online discussion (AOD) is increasingly used as a main activity for blended learning, many students find it difficult to engage in discussions and report low achievement. Early prediction and timely intervention can help potential low achievers get back on track as early as possible. This study presented a data mining process to construct proxy variables that reflect theoretical and empirical evidence and measured the accuracy of a prediction model that incorporated all of the variables for validation. For the empirical study, data were obtained from 105 university students who were enrolled in two blended learning courses that used AOD as their main activity. The results indicated the high accuracy of the prediction model as well as the possibility of early detection and timely interventions. In addition, we examined participants' learning behaviors in the two courses using the proxy variables and provided suggestions for practice. The implications of this study for education data mining and learning analytics are discussed. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Internet & Higher Education is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=ehh&AN=115824012 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1016/j.iheduc.2016.03.002 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 14 StartPage: 30 Subjects: – SubjectFull: Blended learning Type: general – SubjectFull: Discussion Type: general – SubjectFull: Empirical research Type: general – SubjectFull: Data mining Type: general – SubjectFull: Web analytics Type: general Titles: – TitleFull: Toward evidence-based learning analytics: Using proxy variables to improve asynchronous online discussion environments. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Kim, Dongho – PersonEntity: Name: NameFull: Park, Yeonjeong – PersonEntity: Name: NameFull: Yoon, Meehyun – PersonEntity: Name: NameFull: Jo, Il-Hyun IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 07 Text: Jul2016 Type: published Y: 2016 Identifiers: – Type: issn-print Value: 10967516 Numbering: – Type: volume Value: 30 Titles: – TitleFull: Internet & Higher Education Type: main |
| ResultId | 1 |
