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A Sequential Response Model for Analyzing Process Data on Technology-Based Problem-Solving Tasks
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DOI：10.1080/00273171.2021.1932403.png)
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Abstract
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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.
Keywords:
Sequential response model
technology-based assessment
process data
response sequence
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