Analyzing Student Performance in Programming Education Using Classification Techniques.
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| Title: | Analyzing Student Performance in Programming Education Using Classification Techniques. |
|---|---|
| Authors: | Sunday, Kissinger1 kissinger.sunday@udusok.edu.ng, Patrick Ocheja2, Hussain, Sadiq3, Oyelere, Solomon Sunday4, Balogun, Oluwafemi Samson4, Agbo, Friday Joseph4 |
| Source: | International Journal of Emerging Technologies in Learning. 2020, Vol. 15 Issue 2, p127-144. 18p. |
| Subject Terms: | *Computer programming, *Classification, Decision trees, Data mining, Classification algorithms, Data logging, Automatic extracting (Information science) |
| Geographic Terms: | Sokoto (Nigeria) |
| Abstract: | In this research, we aggregated students log data such as Class Test Score (CTS), Assignment Completed (ASC), Class Lab Work (CLW) and Class Attendance (CATT) from the Department of Mathematics, Computer Science Unit, Usmanu Danfodiyo University, Sokoto, Nigeria. Similarly, we employed data mining techniques such as ID3 & J48 Decision tree algorithms to analyze the data. We compared these algorithms on 239 classification instances. The experimental results show that the J48 algorithm has higher accuracy in the classification task compared to the ID3 algorithm. The important feature attributes such as Information Gain and Gain Ratio feature evaluators were also compared. Both the methods applied were able to rank search methods. The experimental results confirmed that the two methods derived the same set of attributes with a slight deviation in the ranking. From the results analyzed, we discovered that 67.36 percent failed the course titled "Introduction to Computer Programming", while 32.64 percent passed the course. Since the CATT has the highest gain value from our analysis; we concluded that it is largely responsible for the success or failure of the students. Recommendations were given on how to improve the failure rates in the future. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Emerging Technologies in Learning is the property of International Association of Online Engineering (IAOE) 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: 141467355 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Analyzing Student Performance in Programming Education Using Classification Techniques. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Sunday%2C+Kissinger%22">Sunday, Kissinger</searchLink><relatesTo>1</relatesTo><i> kissinger.sunday@udusok.edu.ng</i><br /><searchLink fieldCode="AR" term="%22Patrick+Ocheja%22">Patrick Ocheja</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Hussain%2C+Sadiq%22">Hussain, Sadiq</searchLink><relatesTo>3</relatesTo><br /><searchLink fieldCode="AR" term="%22Oyelere%2C+Solomon+Sunday%22">Oyelere, Solomon Sunday</searchLink><relatesTo>4</relatesTo><br /><searchLink fieldCode="AR" term="%22Balogun%2C+Oluwafemi+Samson%22">Balogun, Oluwafemi Samson</searchLink><relatesTo>4</relatesTo><br /><searchLink fieldCode="AR" term="%22Agbo%2C+Friday+Joseph%22">Agbo, Friday Joseph</searchLink><relatesTo>4</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Emerging+Technologies+in+Learning%22">International Journal of Emerging Technologies in Learning</searchLink>. 2020, Vol. 15 Issue 2, p127-144. 18p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Computer+programming%22">Computer programming</searchLink><br />*<searchLink fieldCode="DE" term="%22Classification%22">Classification</searchLink><br /><searchLink fieldCode="DE" term="%22Decision+trees%22">Decision trees</searchLink><br /><searchLink fieldCode="DE" term="%22Data+mining%22">Data mining</searchLink><br /><searchLink fieldCode="DE" term="%22Classification+algorithms%22">Classification algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Data+logging%22">Data logging</searchLink><br /><searchLink fieldCode="DE" term="%22Automatic+extracting+%28Information+science%29%22">Automatic extracting (Information science)</searchLink> – Name: SubjectGeographic Label: Geographic Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Sokoto+%28Nigeria%29%22">Sokoto (Nigeria)</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: In this research, we aggregated students log data such as Class Test Score (CTS), Assignment Completed (ASC), Class Lab Work (CLW) and Class Attendance (CATT) from the Department of Mathematics, Computer Science Unit, Usmanu Danfodiyo University, Sokoto, Nigeria. Similarly, we employed data mining techniques such as ID3 & J48 Decision tree algorithms to analyze the data. We compared these algorithms on 239 classification instances. The experimental results show that the J48 algorithm has higher accuracy in the classification task compared to the ID3 algorithm. The important feature attributes such as Information Gain and Gain Ratio feature evaluators were also compared. Both the methods applied were able to rank search methods. The experimental results confirmed that the two methods derived the same set of attributes with a slight deviation in the ranking. From the results analyzed, we discovered that 67.36 percent failed the course titled "Introduction to Computer Programming", while 32.64 percent passed the course. Since the CATT has the highest gain value from our analysis; we concluded that it is largely responsible for the success or failure of the students. Recommendations were given on how to improve the failure rates in the future. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of International Journal of Emerging Technologies in Learning is the property of International Association of Online Engineering (IAOE) 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.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3991/ijet.v15i02.11527 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 18 StartPage: 127 Subjects: – SubjectFull: Computer programming Type: general – SubjectFull: Classification Type: general – SubjectFull: Decision trees Type: general – SubjectFull: Data mining Type: general – SubjectFull: Classification algorithms Type: general – SubjectFull: Data logging Type: general – SubjectFull: Automatic extracting (Information science) Type: general – SubjectFull: Sokoto (Nigeria) Type: general Titles: – TitleFull: Analyzing Student Performance in Programming Education Using Classification Techniques. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Sunday, Kissinger – PersonEntity: Name: NameFull: Patrick Ocheja – PersonEntity: Name: NameFull: Hussain, Sadiq – PersonEntity: Name: NameFull: Oyelere, Solomon Sunday – PersonEntity: Name: NameFull: Balogun, Oluwafemi Samson – PersonEntity: Name: NameFull: Agbo, Friday Joseph IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 02 Text: 2020 Type: published Y: 2020 Identifiers: – Type: issn-print Value: 18630383 Numbering: – Type: volume Value: 15 – Type: issue Value: 2 Titles: – TitleFull: International Journal of Emerging Technologies in Learning Type: main |
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