Clusters of Solvers' Behavior Patterns among Beginners and Non-Beginners and Their Changes during an Introductory Programming Course

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Bibliographic Details
Title: Clusters of Solvers' Behavior Patterns among Beginners and Non-Beginners and Their Changes during an Introductory Programming Course
Language: English
Authors: Heidi Taveter, Marina Lepp
Source: Informatics in Education. 2025 24(1):199-221.
Availability: Vilnius University Institute of Mathematics and Informatics, Lithuanian Academy of Sciences. Akademjos str. 4, Vilnius LT 08663 Lithuania. Tel: +37-5-21-09300; Fax: +37-5-27-29209; e-mail: info@mii.vu.lt; Web site: https://infedu.vu.lt/journal/INFEDU
Peer Reviewed: Y
Page Count: 23
Publication Date: 2025
Document Type: Journal Articles
Reports - Research
Descriptors: Behavior Patterns, Novices, Expertise, Programming, Introductory Courses, Problem Solving, Student Characteristics, Multivariate Analysis, Tests, Scores, Troubleshooting
ISSN: 1648-5831
2335-8971
Abstract: 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.
Abstractor: As Provided
Entry Date: 2025
Accession Number: EJ1468074
Database: ERIC
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