A Sequential Response Model for Analyzing Process Data on Technology-Based Problem-Solving Tasks. (2nd November 2022)

• Record Type: Journal Article
• Authors: Han, Yuting; Liu, Hongyun; Ji, Feng
• Is Part Of: Multivariate behavioral research. Volume 57:Number 6(2022)
• Page Start: 960
• Page End: 977
• Publication Date: 2022-11-02
• Subjects: Sequential response model -- technology-based assessment -- process data -- response sequence; Psychometrics -- Periodicals; Psychology, Experimental -- Periodicals; Psychology, Experimental; Psychometrics; Periodicals; 150.15195
• DOI: 10.1080/00273171.2021.1932403
• Languages: English
• ISSNs: 0027-3171

Abstract

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.