Unfolding Students' Online Assignment Submission Behavioral Patterns using Temporal Learning Analytics.
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| Title: | Unfolding Students' Online Assignment Submission Behavioral Patterns using Temporal Learning Analytics. |
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| Authors: | Kokoç, Mehmet1 kokoc@trabzon.edu.tr, Akçapınar, Gökhan2 gokhana@hacettepe.edu.tr, Hasnine, Mohammad Nehal3 nehal.hasnine.79@hosei.ac.jp |
| Source: | Educational Technology & Society. Jan2021, Vol. 24 Issue 1, p223-235. 13p. |
| Subject Terms: | *At-risk students, *School failure, *Student assignments, *Learning analytics, Association rule mining, Markov processes |
| Abstract: | This study analyzed students' online assignment submission behaviors from the perspectives of temporal learning analytics. This study aimed to model the time-dependent changes in the assignment submission behavior of university students by employing various machine learning methods. Precisely, clustering, Markov Chains, and association rule mining analysis were used to analyze students' assignment submission behaviors in an online learning environment. The results revealed that students displayed similar patterns in terms of assignment submission behavior. Moreover, it was observed that students' assignment submission behavior did not change much across the semester. When these results are analyzed together with the students' academic performance at the end of the semester, it was observed that students' end-of-term academic performance can be predicted from their assignment submission behaviors at the beginning of the semester. Our results, within the scope of precision education, can be used to diagnose and predict students who are not going to submit the next assignments as the semester progresses as well as students who are going to fail at the end of the semester. Therefore, learning analytics interventions can be designed based on these results to prevent possible academic failures. Furthermore, the findings of the study are discussed considering the development of early-warning intervention systems for at-risk students and precision education. [ABSTRACT FROM AUTHOR] |
| Copyright of Educational Technology & Society is the property of International Forum of Educational Technology & Society (IFETS) 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 | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: ehh DbLabel: Education Research Complete An: 148500997 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Unfolding Students' Online Assignment Submission Behavioral Patterns using Temporal Learning Analytics. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Kokoç%2C+Mehmet%22">Kokoç, Mehmet</searchLink><relatesTo>1</relatesTo><i> kokoc@trabzon.edu.tr</i><br /><searchLink fieldCode="AR" term="%22Akçapınar%2C+Gökhan%22">Akçapınar, Gökhan</searchLink><relatesTo>2</relatesTo><i> gokhana@hacettepe.edu.tr</i><br /><searchLink fieldCode="AR" term="%22Hasnine%2C+Mohammad+Nehal%22">Hasnine, Mohammad Nehal</searchLink><relatesTo>3</relatesTo><i> nehal.hasnine.79@hosei.ac.jp</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Educational+Technology+%26+Society%22">Educational Technology & Society</searchLink>. Jan2021, Vol. 24 Issue 1, p223-235. 13p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22At-risk+students%22">At-risk students</searchLink><br />*<searchLink fieldCode="DE" term="%22School+failure%22">School failure</searchLink><br />*<searchLink fieldCode="DE" term="%22Student+assignments%22">Student assignments</searchLink><br />*<searchLink fieldCode="DE" term="%22Learning+analytics%22">Learning analytics</searchLink><br /><searchLink fieldCode="DE" term="%22Association+rule+mining%22">Association rule mining</searchLink><br /><searchLink fieldCode="DE" term="%22Markov+processes%22">Markov processes</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: This study analyzed students' online assignment submission behaviors from the perspectives of temporal learning analytics. This study aimed to model the time-dependent changes in the assignment submission behavior of university students by employing various machine learning methods. Precisely, clustering, Markov Chains, and association rule mining analysis were used to analyze students' assignment submission behaviors in an online learning environment. The results revealed that students displayed similar patterns in terms of assignment submission behavior. Moreover, it was observed that students' assignment submission behavior did not change much across the semester. When these results are analyzed together with the students' academic performance at the end of the semester, it was observed that students' end-of-term academic performance can be predicted from their assignment submission behaviors at the beginning of the semester. Our results, within the scope of precision education, can be used to diagnose and predict students who are not going to submit the next assignments as the semester progresses as well as students who are going to fail at the end of the semester. Therefore, learning analytics interventions can be designed based on these results to prevent possible academic failures. Furthermore, the findings of the study are discussed considering the development of early-warning intervention systems for at-risk students and precision education. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Educational Technology & Society is the property of International Forum of Educational Technology & Society (IFETS) 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=148500997 |
| RecordInfo | BibRecord: BibEntity: Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 13 StartPage: 223 Subjects: – SubjectFull: At-risk students Type: general – SubjectFull: School failure Type: general – SubjectFull: Student assignments Type: general – SubjectFull: Learning analytics Type: general – SubjectFull: Association rule mining Type: general – SubjectFull: Markov processes Type: general Titles: – TitleFull: Unfolding Students' Online Assignment Submission Behavioral Patterns using Temporal Learning Analytics. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Kokoç, Mehmet – PersonEntity: Name: NameFull: Akçapınar, Gökhan – PersonEntity: Name: NameFull: Hasnine, Mohammad Nehal IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Text: Jan2021 Type: published Y: 2021 Identifiers: – Type: issn-print Value: 11763647 Numbering: – Type: volume Value: 24 – Type: issue Value: 1 Titles: – TitleFull: Educational Technology & Society Type: main |
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