Quality meets Diversity: A Model-Agnostic Framework for Computerized Adaptive Testing

01/15/2021
by   Haoyang Bi, et al.
10

Computerized Adaptive Testing (CAT) is emerging as a promising testing application in many scenarios, such as education, game and recruitment, which targets at diagnosing the knowledge mastery levels of examinees on required concepts. It shows the advantage of tailoring a personalized testing procedure for each examinee, which selects questions step by step, depending on her performance. While there are many efforts on developing CAT systems, existing solutions generally follow an inflexible model-specific fashion. That is, they need to observe a specific cognitive model which can estimate examinee's knowledge levels and design the selection strategy according to the model estimation. In this paper, we study a novel model-agnostic CAT problem, where we aim to propose a flexible framework that can adapt to different cognitive models. Meanwhile, this work also figures out CAT solution with addressing the problem of how to generate both high-quality and diverse questions simultaneously, which can give a comprehensive knowledge diagnosis for each examinee. Inspired by Active Learning, we propose a novel framework, namely Model-Agnostic Adaptive Testing (MAAT) for CAT solution, where we design three sophisticated modules including Quality Module, Diversity Module and Importance Module. Extensive experimental results on two real-world datasets clearly demonstrate that our MAAT can support CAT with guaranteeing both quality and diversity perspectives.

READ FULL TEXT

Authors

page 1

05/27/2019

Enhancing Item Response Theory for Cognitive Diagnosis

Cognitive diagnosis is a fundamental and crucial task in many educationa...
08/23/2019

Interpretable Cognitive Diagnosis with Neural Network

In intelligent education systems, one key issue is to discover students'...
08/23/2019

Interpretable Cognitive Diagnosis with Neural Network for Intelligent Educational Systems

In intelligent education systems, one key issue is to discover students'...
02/28/2020

fff

sss...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.