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.