Automatically Detecting Previous Programming Knowledge from Novice Programmer Code Compilation History.
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| Title: | Automatically Detecting Previous Programming Knowledge from Novice Programmer Code Compilation History. |
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| Authors: | LOKKILA, Erno1,2 eolokk@utu.fi, CHRISTOPOULOS, Athanasios2 atchri@utu.fi, LAAKSO, Mikko-Jussi2 milaak@utu.fi |
| Source: | Informatics in Education. Jun2023, Vol. 22 Issue 2, p277-294. 18p. |
| Subject Terms: | *Machine learning, *Prior learning, *Higher education, Markov processes, K-means clustering, Likert scale |
| Abstract: | Prior programming knowledge of students has a major impact on introductory programming courses. Those with prior experience often seem to breeze through the course. Those without prior experience see others breeze through the course and disengage from the material or drop out. The purpose of this study is to demonstrate that novice student programming behavior can be modeled as a Markov process. The resulting transition matrix can then be used in machine learning algorithms to create clusters of similarly behaving students. We describe in detail the state machine used in the Markov process and how to compute the transition matrix. We compute the transition matrix for 665 students and cluster them using the k-means clustering algorithm. We choose the number of cluster to be three based on analysis of the dataset. We show that the created clusters have statistically different means for student prior knowledge in programming, when measured on a Likert scale of 1-5. [ABSTRACT FROM AUTHOR] |
| Copyright of Informatics in Education is the property of Informatics in Education 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: 169904508 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Automatically Detecting Previous Programming Knowledge from Novice Programmer Code Compilation History. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22LOKKILA%2C+Erno%22">LOKKILA, Erno</searchLink><relatesTo>1,2</relatesTo><i> eolokk@utu.fi</i><br /><searchLink fieldCode="AR" term="%22CHRISTOPOULOS%2C+Athanasios%22">CHRISTOPOULOS, Athanasios</searchLink><relatesTo>2</relatesTo><i> atchri@utu.fi</i><br /><searchLink fieldCode="AR" term="%22LAAKSO%2C+Mikko-Jussi%22">LAAKSO, Mikko-Jussi</searchLink><relatesTo>2</relatesTo><i> milaak@utu.fi</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Informatics+in+Education%22">Informatics in Education</searchLink>. Jun2023, Vol. 22 Issue 2, p277-294. 18p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br />*<searchLink fieldCode="DE" term="%22Prior+learning%22">Prior learning</searchLink><br />*<searchLink fieldCode="DE" term="%22Higher+education%22">Higher education</searchLink><br /><searchLink fieldCode="DE" term="%22Markov+processes%22">Markov processes</searchLink><br /><searchLink fieldCode="DE" term="%22K-means+clustering%22">K-means clustering</searchLink><br /><searchLink fieldCode="DE" term="%22Likert+scale%22">Likert scale</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Prior programming knowledge of students has a major impact on introductory programming courses. Those with prior experience often seem to breeze through the course. Those without prior experience see others breeze through the course and disengage from the material or drop out. The purpose of this study is to demonstrate that novice student programming behavior can be modeled as a Markov process. The resulting transition matrix can then be used in machine learning algorithms to create clusters of similarly behaving students. We describe in detail the state machine used in the Markov process and how to compute the transition matrix. We compute the transition matrix for 665 students and cluster them using the k-means clustering algorithm. We choose the number of cluster to be three based on analysis of the dataset. We show that the created clusters have statistically different means for student prior knowledge in programming, when measured on a Likert scale of 1-5. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Informatics in Education is the property of Informatics in Education 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=169904508 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.15388/infedu.2023.15 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 18 StartPage: 277 Subjects: – SubjectFull: Machine learning Type: general – SubjectFull: Prior learning Type: general – SubjectFull: Higher education Type: general – SubjectFull: Markov processes Type: general – SubjectFull: K-means clustering Type: general – SubjectFull: Likert scale Type: general Titles: – TitleFull: Automatically Detecting Previous Programming Knowledge from Novice Programmer Code Compilation History. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: LOKKILA, Erno – PersonEntity: Name: NameFull: CHRISTOPOULOS, Athanasios – PersonEntity: Name: NameFull: LAAKSO, Mikko-Jussi IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 06 Text: Jun2023 Type: published Y: 2023 Identifiers: – Type: issn-print Value: 16485831 Numbering: – Type: volume Value: 22 – Type: issue Value: 2 Titles: – TitleFull: Informatics in Education Type: main |
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