A Multi-Method Approach for Exploring Programming Trajectories Through Log Data: Insights from Data Visualization Tasks.
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| Title: | A Multi-Method Approach for Exploring Programming Trajectories Through Log Data: Insights from Data Visualization Tasks. |
|---|---|
| Authors: | Fernandez, Cassia1,2 (AUTHOR) cassia.ofernandez@gmail.com, Blikstein, Paulo1 (AUTHOR) paulob@tc.columbia.edu, de Deus Lopes, Roseli2 (AUTHOR) roseli.lopes@usp.br |
| Source: | Journal of Science Education & Technology. Oct2025, Vol. 34 Issue 5, p994-1019. 26p. |
| Subject Terms: | *Student engagement, *High school students, *Individual development, Data visualization, Visual programming languages (Computer science), Data mining |
| Abstract: | Interest in data science education is growing as data becomes more prevalent in our daily lives and plays a central role in making informed decisions and understanding the world. Due to the interdisciplinary nature and broad scope of the field, further research is essential to unravel how K-12 students can effectively interact with data through productive learning experiences. This is particularly true in data visualization activities, in which students must employ a variety of skills to effectively extract and communicate data insights. In this study, we describe key actions involved in creating data visualizations using a block-based programming environment (PlayData). Based on qualitative video analysis, we identified six core data visualization programming moves: program creation, selection of parameters, output inspection, data inspection, program rearrangement, and visual design. Then, using learning analytics techniques and Epistemic Network Analysis, we developed a method for automatically categorizing and characterizing those moves based on fine-grained log data collected from the environment, which allowed the identification of patterns in students' trajectories. We found that students' work is distributed across several micro-tasks, each involving distinct types of interaction with the environment and holding a unique value in the process of engaging in programming, data analysis, and visual design. As students progress, there is a transition among these moves, suggesting the need for activities that ensure comprehensive exposure to all of them. Our study presents two main contributions: a novel approach to automatically categorize and describe learning trajectories in open-ended programming tasks and insights into how K-12 students engage with those tasks in a data-related context, laying a foundation for better supporting learning and research in this emergent area. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Science Education & Technology 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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| Header | DbId: ehh DbLabel: Education Research Complete An: 189590675 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: A Multi-Method Approach for Exploring Programming Trajectories Through Log Data: Insights from Data Visualization Tasks. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Fernandez%2C+Cassia%22">Fernandez, Cassia</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> cassia.ofernandez@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Blikstein%2C+Paulo%22">Blikstein, Paulo</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> paulob@tc.columbia.edu</i><br /><searchLink fieldCode="AR" term="%22de+Deus+Lopes%2C+Roseli%22">de Deus Lopes, Roseli</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> roseli.lopes@usp.br</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+Science+Education+%26+Technology%22">Journal of Science Education & Technology</searchLink>. Oct2025, Vol. 34 Issue 5, p994-1019. 26p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Student+engagement%22">Student engagement</searchLink><br />*<searchLink fieldCode="DE" term="%22High+school+students%22">High school students</searchLink><br />*<searchLink fieldCode="DE" term="%22Individual+development%22">Individual development</searchLink><br /><searchLink fieldCode="DE" term="%22Data+visualization%22">Data visualization</searchLink><br /><searchLink fieldCode="DE" term="%22Visual+programming+languages+%28Computer+science%29%22">Visual programming languages (Computer science)</searchLink><br /><searchLink fieldCode="DE" term="%22Data+mining%22">Data mining</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Interest in data science education is growing as data becomes more prevalent in our daily lives and plays a central role in making informed decisions and understanding the world. Due to the interdisciplinary nature and broad scope of the field, further research is essential to unravel how K-12 students can effectively interact with data through productive learning experiences. This is particularly true in data visualization activities, in which students must employ a variety of skills to effectively extract and communicate data insights. In this study, we describe key actions involved in creating data visualizations using a block-based programming environment (PlayData). Based on qualitative video analysis, we identified six core data visualization programming moves: program creation, selection of parameters, output inspection, data inspection, program rearrangement, and visual design. Then, using learning analytics techniques and Epistemic Network Analysis, we developed a method for automatically categorizing and characterizing those moves based on fine-grained log data collected from the environment, which allowed the identification of patterns in students' trajectories. We found that students' work is distributed across several micro-tasks, each involving distinct types of interaction with the environment and holding a unique value in the process of engaging in programming, data analysis, and visual design. As students progress, there is a transition among these moves, suggesting the need for activities that ensure comprehensive exposure to all of them. Our study presents two main contributions: a novel approach to automatically categorize and describe learning trajectories in open-ended programming tasks and insights into how K-12 students engage with those tasks in a data-related context, laying a foundation for better supporting learning and research in this emergent area. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Journal of Science Education & Technology 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.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s10956-025-10210-7 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 26 StartPage: 994 Subjects: – SubjectFull: Student engagement Type: general – SubjectFull: High school students Type: general – SubjectFull: Individual development Type: general – SubjectFull: Data visualization Type: general – SubjectFull: Visual programming languages (Computer science) Type: general – SubjectFull: Data mining Type: general Titles: – TitleFull: A Multi-Method Approach for Exploring Programming Trajectories Through Log Data: Insights from Data Visualization Tasks. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Fernandez, Cassia – PersonEntity: Name: NameFull: Blikstein, Paulo – PersonEntity: Name: NameFull: de Deus Lopes, Roseli IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 10 Text: Oct2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 10590145 Numbering: – Type: volume Value: 34 – Type: issue Value: 5 Titles: – TitleFull: Journal of Science Education & Technology Type: main |
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