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Reexamining Learning Curve Analysis in Programming Education: The Value of Many Small Problems

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Title: Reexamining Learning Curve Analysis in Programming Education: The Value of Many Small Problems
Language: English
Authors: Mehmet Arif Demirta¸, Max Fowler, Kathryn Cunningham
Source: International Educational Data Mining Society. 2024.
Availability: International Educational Data Mining Society. e-mail: admin@educationaldatamining.org; Web site: https://educationaldatamining.org/conferences/
Peer Reviewed: Y
Page Count: 15
Publication Date: 2024
Document Type: Speeches/Meeting Papers
Reports - Research
Education Level: Higher Education
Postsecondary Education
Descriptors: Programming, Computer Science Education, Skill Development, Knowledge Level, College Students, Learning, Introductory Courses, Programming Languages
Abstract: Analyzing which skills students develop in introductory programming education is an important question for the computer science education community. These key skills and concepts have been formalized as knowledge components, which are units of knowledge that can be measured by performance on a set of tasks. While knowledge components in other domains have been successfully identified using learning curve analysis, such attempts on students' open-ended code-writing assignments have not been very successful. To understand why, we replicated a previously proposed approach, which uses abstract syntax tree (AST) nodes as knowledge components, on data collected across multiple semesters of a large-scale introductory programming course. Findings from our replication show that, given sufficient Kathryn Cunningham University of Illinois Urbana-Champaign Urbana, IL, USA katcun@illinois.edu validate domain models that describe such skills, however, attempts to apply learning curve analysis in the context of programming education have yielded few results so far. While programming education is rich in data collected during code-writing, thanks to numerous learning environments that capture and automatically grade student program submissions, applications of learning curve analysis on such data have produced a limited number of validated knowledge components, or knowledge components that are difficult to interpret. measurement opportunities, a significant subset of AST nodes provide a viable knowledge component model for learning curve analysis to understand student learning, contrary to earlier findings. In addition to providing evidence for the validity of certain AST-based knowledge components, we recommend a set of conditions for programming courses that may enable knowledge components generated using AST nodes to be successfully observed using learning curve analysis. Our findings suggest that learning curve analysis can yield useful insight for instructors on skills related to language elements, and can be integrated into any environment that collects code-writing data using our step generation method. [For the complete proceedings, see ED675485.]
Abstractor: As Provided
Entry Date: 2025
Accession Number: ED675656
Database: ERIC
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  Data: Analyzing which skills students develop in introductory programming education is an important question for the computer science education community. These key skills and concepts have been formalized as knowledge components, which are units of knowledge that can be measured by performance on a set of tasks. While knowledge components in other domains have been successfully identified using learning curve analysis, such attempts on students' open-ended code-writing assignments have not been very successful. To understand why, we replicated a previously proposed approach, which uses abstract syntax tree (AST) nodes as knowledge components, on data collected across multiple semesters of a large-scale introductory programming course. Findings from our replication show that, given sufficient Kathryn Cunningham University of Illinois Urbana-Champaign Urbana, IL, USA katcun@illinois.edu validate domain models that describe such skills, however, attempts to apply learning curve analysis in the context of programming education have yielded few results so far. While programming education is rich in data collected during code-writing, thanks to numerous learning environments that capture and automatically grade student program submissions, applications of learning curve analysis on such data have produced a limited number of validated knowledge components, or knowledge components that are difficult to interpret. measurement opportunities, a significant subset of AST nodes provide a viable knowledge component model for learning curve analysis to understand student learning, contrary to earlier findings. In addition to providing evidence for the validity of certain AST-based knowledge components, we recommend a set of conditions for programming courses that may enable knowledge components generated using AST nodes to be successfully observed using learning curve analysis. Our findings suggest that learning curve analysis can yield useful insight for instructors on skills related to language elements, and can be integrated into any environment that collects code-writing data using our step generation method. [For the complete proceedings, see ED675485.]
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      – SubjectFull: Programming Languages
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      – TitleFull: Reexamining Learning Curve Analysis in Programming Education: The Value of Many Small Problems
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              Y: 2024
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