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

Guardado en:

Detalles Bibliográficos
Publicado en:	Informatics in Education vol. 24, no. 1 (2025), p. 199
Autor principal:	Taveter, Heidi
Otros Autores:	Lepp, Marina
Publicado:	Institute of Mathematics and Informatics
Materias:	Programming Solvers Cluster analysis Errors Teaching Students Debugging Education Skills Process analysis Syntax Tests Multivariate Analysis Novices Psychological Patterns Literature Reviews Student Characteristics Outcomes of Education Observation Behavior Patterns Introductory Courses Time Low Achievement At Risk Students
Acceso en línea:	Citation/Abstract Full Text - PDF Full text outside of ProQuest
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Descripción
Resumen:	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.
ISSN:	1648-5831 2335-8971
DOI:	10.15388/infedu.2025.07
Fuente:	Library Science Database