Amortised Design Optimization for Item Response Theory

TL;DR

This paper introduces a novel approach that uses deep reinforcement learning to efficiently select test items and infer student abilities in real-time, reducing computational costs in Item Response Theory applications.

Contribution

It proposes incorporating amortised experimental design into IRT by training a DRL agent to optimize item selection and inference, enabling near real-time adaptive testing.

Findings

DRL agent effectively selects informative test items.

Method reduces computational costs compared to traditional OED.

Achieves near real-time inference in adaptive testing scenarios.

Abstract

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.