Reinforcement Learning Measurement Model
Interactive assessments generate sequential process data that are not well handled by conventional item response models. Existing MDP-based measurement approaches, such as the Markov decision process measurement model (MDP-MM, LaMar, 2018), link action choices to state-action values, but their reliance on person-specific tabular value functions makes them difficult to scale beyond small, fully enumerated tasks. We propose the Reinforcement Learning Measurement Model (RLMM), a measurement framework that decouples person-level choice sensitivity from task-level value representation through a shared parametric action-value function, making estimation more computationally efficient for larger...
| Authors | Wenqian Xu, Feng Ji |
|---|---|
| Primary category | stat.ME |
| Published | 10 May 2026 |
| Last updated at source | 10 May 2026 |
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