Clusters of Solvers’ Behavior Patterns Among Beginners and Non-beginners and Their Changes During an Introductory Programming Course

Learning programming has become increasingly popular, with learners from diverse backgrounds and experiences requiring different support. Programming-process analysis helps to identify solver types and needs for assistance. The study examined students’ behavior patterns in programming among beginner...

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Veröffentlicht in:Informatics in education Jg. 24; H. 1; S. 199 - 221
Hauptverfasser: Taveter, Heidi, Lepp, Marina
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Vilnius Vilniaus Universiteto Leidykla 01.01.2025
Vilnius University Press
Institute of Mathematics and Informatics
Vilnius University
Schlagworte:
At Risk Students
behavior features in programming
Behavior Patterns
behavior patterns in programming
Behaviorism
Cluster analysis
clustering
Debugging
Education
Errors
ICT Information and Communications Technologies
Introductory Courses
introductory programming
Literature Reviews
Low Achievement
Multivariate Analysis
Novices
Observation
Outcomes of Education
Process analysis
Programming
programming-process analysis
Psychological Patterns
School education
Skills
Social psychology and group interaction
Solvers
Student Characteristics
Students
Syntax
Teaching
Tests
Time
programmingprocess analysis
behavior features in programming
behavior patterns in programming
clustering
introductory programming
ISSN:1648-5831, 2335-8971
Online-Zugang:Volltext
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Beschreibung
Zusammenfassung:Learning programming has become increasingly popular, with learners from diverse backgrounds and experiences requiring different support. Programming-process analysis helps to identify solver types and needs for assistance. The study examined students’ behavior patterns in programming among beginners and non-beginners to identify solver types, assess midterm exam scores’ differences, and evaluate the types’ persistence. Data from Thonny logs were collected during introductory programming exams in 2022, with sample sizes of 301 and 275. Cluster analysis revealed four solver types: many runs and errors, a large proportion of syntax errors, balance in all features, and a late start with executions. Significant score differences were found in the second midterm exam. The late start of executions characterizes one group with lower performance, and types are impersistent during the first programming course. The findings underscore the importance of teaching debugging early and the need to teach how to program using regular executions.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
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ISSN:1648-5831
2335-8971
DOI:10.15388/infedu.2025.07