Amortised Design Optimization for Item Response Theory
This work addresses the problem of slow, costly student ability inference in education by making it interactive and efficient, though it is incremental as it builds on existing IRT and OED methods with a novel computational shift.
The paper tackles the computational inefficiency of Optimal Experimental Design (OED) methods in Item Response Theory (IRT) for interactive applications like education, by proposing an amortised design approach using Deep Reinforcement Learning (DRL) to precompute and enable real-time, informative test item selection.
Item Response Theory (IRT) is a well known method for assessing responses from humans in education and psychology. In education, IRT is used to infer student abilities and characteristics of test items from student responses. Interactions with students are expensive, calling for methods that efficiently gather information for inferring student abilities. Methods based on Optimal Experimental Design (OED) are computationally costly, making them inapplicable for interactive applications. In response, we propose incorporating amortised experimental design into IRT. Here, the computational cost is shifted to a precomputing phase by training a Deep Reinforcement Learning (DRL) agent with synthetic data. The agent is trained to select optimally informative test items for the distribution of students, and to conduct amortised inference conditioned on the experiment outcomes. During deployment the agent estimates parameters from data, and suggests the next test item for the student, in close to real-time, by taking into account the history of experiments and outcomes.