Using learning analytics to develop early-warning system for at-risk students.
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| Title: | Using learning analytics to develop early-warning system for at-risk students. |
|---|---|
| Authors: | Akçapınar, Gökhan1 (AUTHOR) gokhana@hacettepe.edu.tr, Altun, Arif1 (AUTHOR) altunar@hacettepe.edu.tr, Aşkar, Petek1 (AUTHOR) paskar@hacettepe.edu.tr |
| Source: | International Journal of Educational Technology in Higher Education. 10/31/2019, Vol. 16 Issue 1, pN.PAG-N.PAG. 1p. 1 Color Photograph, 1 Diagram, 14 Charts, 2 Graphs. |
| Subject Terms: | *At-risk students, *Computer input-output equipment, *Online education, *Virtual classrooms, K-nearest neighbor classification, Classification algorithms, Feature selection |
| Abstract: | In the current study interaction data of students in an online learning setting was used to research whether the academic performance of students at the end of term could be predicted in the earlier weeks. The study was carried out with 76 second-year university students registered in a Computer Hardware course. The study aimed to answer two principle questions: which algorithms and features best predict the end of term academic performance of students by comparing different classification algorithms and pre-processing techniques and whether or not academic performance can be predicted in the earlier weeks using these features and the selected algorithm. The results of the study indicated that the kNN algorithm accurately predicted unsuccessful students at the end of term with a rate of 89%. When findings were examined regarding the analysis of data obtained in weeks 3, 6, 9, 12, and 14 to predict whether the end-of-term academic performance of students could be predicted in the earlier weeks, it was observed that students who were unsuccessful at the end of term could be predicted with a rate of 74% in as short as 3 weeks' time. The findings obtained from this study are important for the determination of features for early warning systems that can be developed for online learning systems and as indicators of student success. At the same time, it will aid researchers in the selection of algorithms and pre-processing techniques in the analysis of educational data. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Educational Technology in Higher Education is the property of Springer Nature 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 |
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| FullText | Links: – Type: pdflink Text: Availability: 1 |
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| Header | DbId: ehh DbLabel: Education Research Complete An: 139391744 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Using learning analytics to develop early-warning system for at-risk students. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Akçapınar%2C+Gökhan%22">Akçapınar, Gökhan</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> gokhana@hacettepe.edu.tr</i><br /><searchLink fieldCode="AR" term="%22Altun%2C+Arif%22">Altun, Arif</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> altunar@hacettepe.edu.tr</i><br /><searchLink fieldCode="AR" term="%22Aşkar%2C+Petek%22">Aşkar, Petek</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> paskar@hacettepe.edu.tr</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Educational+Technology+in+Higher+Education%22">International Journal of Educational Technology in Higher Education</searchLink>. 10/31/2019, Vol. 16 Issue 1, pN.PAG-N.PAG. 1p. 1 Color Photograph, 1 Diagram, 14 Charts, 2 Graphs. – 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="%22Computer+input-output+equipment%22">Computer input-output equipment</searchLink><br />*<searchLink fieldCode="DE" term="%22Online+education%22">Online education</searchLink><br />*<searchLink fieldCode="DE" term="%22Virtual+classrooms%22">Virtual classrooms</searchLink><br /><searchLink fieldCode="DE" term="%22K-nearest+neighbor+classification%22">K-nearest neighbor classification</searchLink><br /><searchLink fieldCode="DE" term="%22Classification+algorithms%22">Classification algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Feature+selection%22">Feature selection</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: In the current study interaction data of students in an online learning setting was used to research whether the academic performance of students at the end of term could be predicted in the earlier weeks. The study was carried out with 76 second-year university students registered in a Computer Hardware course. The study aimed to answer two principle questions: which algorithms and features best predict the end of term academic performance of students by comparing different classification algorithms and pre-processing techniques and whether or not academic performance can be predicted in the earlier weeks using these features and the selected algorithm. The results of the study indicated that the kNN algorithm accurately predicted unsuccessful students at the end of term with a rate of 89%. When findings were examined regarding the analysis of data obtained in weeks 3, 6, 9, 12, and 14 to predict whether the end-of-term academic performance of students could be predicted in the earlier weeks, it was observed that students who were unsuccessful at the end of term could be predicted with a rate of 74% in as short as 3 weeks' time. The findings obtained from this study are important for the determination of features for early warning systems that can be developed for online learning systems and as indicators of student success. At the same time, it will aid researchers in the selection of algorithms and pre-processing techniques in the analysis of educational data. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of International Journal of Educational Technology in Higher Education is the property of Springer Nature 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=139391744 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1186/s41239-019-0172-z Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 1 StartPage: N.PAG Subjects: – SubjectFull: At-risk students Type: general – SubjectFull: Computer input-output equipment Type: general – SubjectFull: Online education Type: general – SubjectFull: Virtual classrooms Type: general – SubjectFull: K-nearest neighbor classification Type: general – SubjectFull: Classification algorithms Type: general – SubjectFull: Feature selection Type: general Titles: – TitleFull: Using learning analytics to develop early-warning system for at-risk students. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Akçapınar, Gökhan – PersonEntity: Name: NameFull: Altun, Arif – PersonEntity: Name: NameFull: Aşkar, Petek IsPartOfRelationships: – BibEntity: Dates: – D: 31 M: 10 Text: 10/31/2019 Type: published Y: 2019 Identifiers: – Type: issn-print Value: 23659440 Numbering: – Type: volume Value: 16 – Type: issue Value: 1 Titles: – TitleFull: International Journal of Educational Technology in Higher Education Type: main |
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