Content area

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.

Details

Business indexing term
Title
Clusters of Solvers’ Behavior Patterns Among Beginners and Non-beginners and Their Changes During an Introductory Programming Course
Publication title
Volume
24
Issue
1
Pages
199-221
Publication year
2025
Publication date
2025
Section
Article
Publisher
Institute of Mathematics and Informatics
Place of publication
Vilnius
Country of publication
Lithuania
Publication subject
ISSN
16485831
e-ISSN
23358971
Source type
Scholarly Journal
Language of publication
English
Document type
Journal Article
Publication history
 
 
Online publication date
2025-03-28
Publication history
 
 
   First posting date
28 Mar 2025
ProQuest document ID
3189990295
Document URL
https://www.proquest.com/scholarly-journals/clusters-solvers-behavior-patterns-among/docview/3189990295/se-2?accountid=208611
Copyright
Copyright Institute of Mathematics and Informatics 2025
Last updated
2025-11-14
Database
2 databases
  • Education Research Index
  • ProQuest One Academic