When Does Scaffolding Provide Too Much Assistance? A Code-Tracing Tutor Investigation

When students are first learning to program, they not only have to learn to write programs, but also how to trace them. Code tracing involves stepping through a program step-by-step, which helps to predict the output of the program and identify bugs. Students routinely struggle with this activity, a...

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Vydané v:International journal of artificial intelligence in education Ročník 31; číslo 4; s. 784 - 819
Hlavní autori: Jennings, Jay, Muldner, Kasia
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York Springer New York 01.12.2021
Springer
Springer Nature B.V
Predmet:
A festschrift in honour of Jim Greer
Artificial Intelligence
Coding
Cognitive Psychology
Community Relations
Computer Science
Computer Science Education
Computers
Computers and Education
Data collection
Educational Environment
Educational Technology
Graduate Students
Individual Differences
Individualized Instruction
Learning
Memory
Multilayers
Novices
Problem Solving
Programming
Programming Languages
Python
Scaffolding
Scaffolding (Teaching Technique)
Short Term Memory
Skills
Students
Subgroups
Syntax
Teaching
Teaching Methods
Thinking Skills
Tracing
Troubleshooting
Tutoring
Tutors
Unsupervised learning
User Interfaces and Human Computer Interaction
Writing Instruction
Code tracing
Worked examples
Programming instruction
Tutoring system
Assistance
ISSN:1560-4292, 1560-4306
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Shrnutí:When students are first learning to program, they not only have to learn to write programs, but also how to trace them. Code tracing involves stepping through a program step-by-step, which helps to predict the output of the program and identify bugs. Students routinely struggle with this activity, as evidenced by prior work and our own experiences in the classroom. To address this, we designed a Code Tracing  (CT)-Tutor. We varied the level of assistance provided in the tutor, based on (1) the interface scaffolding available during code tracing, and (2) instructional order, operationalized by when examples were provided, either before or after the corresponding problem was solved. We collected data by having participants use the tutor to solve code tracing problems ( N =  97) and analyzed both learning outcomes and process data obtained by extracting features of interest from the log files. We used a multi-layered approach for the analysis, including standard inferential statistics and unsupervised learning to cluster students by their behaviors in the tutor. The results show that the optimal level of assistance for code tracing falls in the middle of the assistance spectrum included in the tutor, but also that there are individual differences in terms of optimal assistance for subgroups of individuals. Based on these results, we outline opportunities for future work around personalizing instruction for code tracing.
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ISSN:1560-4292
1560-4306
DOI:10.1007/s40593-020-00217-z