A Sequential Response Model for Analyzing Process Data on Technology-Based Problem-Solving Tasks.
Multivariate Behav Res
; 57(6): 960-977, 2022.
Article
en En
| MEDLINE
| ID: mdl-34224276
ABSTRACT
Students' response sequences to a technology-based problem-solving task can be treated as a discrete time stochastic process with a conditional Markov property-after conditioning on the students' abilities of problem solving, the next state only depends on the current state. This article proposes a sequential response model (SRM) with a Bayesian approach for parameter estimation that incorporates comprehensive information from the response process to infer problem-solving ability more effectively. A Monte Carlo simulation study showed that parameters were well-recovered. An illustrated example is provided to showcase additional gains using our model for understanding the response process with a real-world interactive assessment item "Tickets" in the programme for international student assessment (PISA) 2012.
Palabras clave
Texto completo:
1
Banco de datos:
MEDLINE
Asunto principal:
Solución de Problemas
/
Tecnología
Tipo de estudio:
Health_economic_evaluation
/
Prognostic_studies
Límite:
Humans
Idioma:
En
Revista:
Multivariate Behav Res
Año:
2022
Tipo del documento:
Article