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Identifying nonconformities in contributions to programming projects: from an engagement perspective in improving code quality.

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Title: Identifying nonconformities in contributions to programming projects: from an engagement perspective in improving code quality.
Authors: Nguyen, Bao-An, Chen, Hsi-Min, Dow, Chyi-Ren
Source: Behaviour & Information Technology. Jan2023, Vol. 42 Issue 1, p141-157. 17p. 6 Diagrams, 5 Charts, 5 Graphs.
Subjects: Learning assessment, Computer software, Teams in the workplace, Structural equation modeling, Kruskal-Wallis Test, Computers, Patient participation, Multiple regression analysis, Rating of students, Workflow, Experience, Software architecture, Pearson correlation (Statistics), Interprofessional relations, Quality assurance, Students, Automation, Descriptive statistics, Factor analysis, Research funding, Student attitudes, Cluster analysis (Statistics)
Geographic Terms: Taiwan
Abstract: Project-based learning is among the most common learning approaches aimed at conveying professional standards and best practices to students in programming courses. However, team projects commonly impose problems related to responsibility sharing, such as low effort or inequality in contributions. This paper presents a collaborative programming assessment system featuring a code quality assessment function with specific metrics to measure individual contributions. Student engagement data is used to detect nonconformities in collaboration using a learning analytical approach. Latent profile analysis was used to detect four theoretical team profiles differentiated by team effort (2 levels) and within-team collaboration (2 levels). We demonstrated the efficacy of assessing code to evaluate team dynamics and student behaviour, wherein efforts to resolve coding style failures can be used as a proxy by which to estimate the taskwork awareness of team members. Submission data from 146 students in 41 web-programming projects revealed four behavioural patterns that could potentially hinder the effective functioning of programming teams: free-riding, social loafing, the bystander effect, and lone wolves. We also demonstrated the applicability of automated programming assessment systems to the monitoring of learning progress, thereby facilitating timely interventions to correct difficulties at the team level. [ABSTRACT FROM AUTHOR]
Copyright of Behaviour & Information Technology is the property of Taylor & Francis Ltd 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: Psychology and Behavioral Sciences Collection
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  Data: Identifying nonconformities in contributions to programming projects: from an engagement perspective in improving code quality.
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  Data: <searchLink fieldCode="AR" term="%22Nguyen%2C+Bao-An%22">Nguyen, Bao-An</searchLink><br /><searchLink fieldCode="AR" term="%22Chen%2C+Hsi-Min%22">Chen, Hsi-Min</searchLink><br /><searchLink fieldCode="AR" term="%22Dow%2C+Chyi-Ren%22">Dow, Chyi-Ren</searchLink>
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  Data: <searchLink fieldCode="JN" term="%22Behaviour+%26+Information+Technology%22">Behaviour & Information Technology</searchLink>. Jan2023, Vol. 42 Issue 1, p141-157. 17p. 6 Diagrams, 5 Charts, 5 Graphs.
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  Data: Project-based learning is among the most common learning approaches aimed at conveying professional standards and best practices to students in programming courses. However, team projects commonly impose problems related to responsibility sharing, such as low effort or inequality in contributions. This paper presents a collaborative programming assessment system featuring a code quality assessment function with specific metrics to measure individual contributions. Student engagement data is used to detect nonconformities in collaboration using a learning analytical approach. Latent profile analysis was used to detect four theoretical team profiles differentiated by team effort (2 levels) and within-team collaboration (2 levels). We demonstrated the efficacy of assessing code to evaluate team dynamics and student behaviour, wherein efforts to resolve coding style failures can be used as a proxy by which to estimate the taskwork awareness of team members. Submission data from 146 students in 41 web-programming projects revealed four behavioural patterns that could potentially hinder the effective functioning of programming teams: free-riding, social loafing, the bystander effect, and lone wolves. We also demonstrated the applicability of automated programming assessment systems to the monitoring of learning progress, thereby facilitating timely interventions to correct difficulties at the team level. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Behaviour & Information Technology is the property of Taylor & Francis Ltd 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=pbh&AN=161394345
RecordInfo BibRecord:
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      – Type: doi
        Value: 10.1080/0144929X.2021.2017483
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      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 17
        StartPage: 141
    Subjects:
      – SubjectFull: Learning assessment
        Type: general
      – SubjectFull: Computer software
        Type: general
      – SubjectFull: Teams in the workplace
        Type: general
      – SubjectFull: Structural equation modeling
        Type: general
      – SubjectFull: Kruskal-Wallis Test
        Type: general
      – SubjectFull: Computers
        Type: general
      – SubjectFull: Patient participation
        Type: general
      – SubjectFull: Multiple regression analysis
        Type: general
      – SubjectFull: Rating of students
        Type: general
      – SubjectFull: Workflow
        Type: general
      – SubjectFull: Experience
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      – SubjectFull: Software architecture
        Type: general
      – SubjectFull: Pearson correlation (Statistics)
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      – SubjectFull: Interprofessional relations
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      – SubjectFull: Quality assurance
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      – SubjectFull: Students
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      – SubjectFull: Automation
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      – SubjectFull: Descriptive statistics
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      – SubjectFull: Factor analysis
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      – SubjectFull: Research funding
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      – SubjectFull: Student attitudes
        Type: general
      – SubjectFull: Cluster analysis (Statistics)
        Type: general
      – SubjectFull: Taiwan
        Type: general
    Titles:
      – TitleFull: Identifying nonconformities in contributions to programming projects: from an engagement perspective in improving code quality.
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            NameFull: Nguyen, Bao-An
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            NameFull: Chen, Hsi-Min
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            – D: 01
              M: 01
              Text: Jan2023
              Type: published
              Y: 2023
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