成对比较
计算机科学
人工智能
公制(单位)
秩(图论)
度量(数据仓库)
计算机化自适应测验
基本事实
机器学习
功能(生物学)
钥匙(锁)
质量(理念)
选择(遗传算法)
数据挖掘
数学
工程类
统计
哲学
运营管理
计算机安全
认识论
组合数学
生物
心理测量学
进化生物学
作者
Haiping Ma,Yi Zeng,Shangshang Yang,Chuan Qin,Xingyi Zhang,Limiao Zhang
标识
DOI:10.1007/s40747-023-01019-1
摘要
Abstract Computerized adaptive testing (CAT) targets to accurately assess the student’s proficiency in the required subject/area. The key issue is how to design a question selector that adaptively selects the best-suited questions for each student based on previous performance step by step. Most existing question selectors execute via greedy metric functions (e.g., question information and uncertainty), which can not effectively capture data characteristics. There also exist learning-based question selectors that redefine the CAT problem as a bilevel optimization problem, where the parameter learning of the question selector and the student proficiency estimation model are coupled, which is not flexible enough. To this end, in this paper, we propose a novel CAT framework with Decoupled Learning selector (DL-CAT). Specifically, we first use the currently estimated student ability and question characteristics as input and design a deep learning-based question selector to predict question selection scores. Then, to address the issue that there is no ground truth to measure the quality of the selected question, an approximate ground-truth and a pairwise rank loss function are specially designed to update the parameters of the question selector independently. Extensive experiments on two real datasets demonstrate that our proposed DL-CAT has certain advantages in effectiveness and significant advantages in efficiency.
科研通智能强力驱动
Strongly Powered by AbleSci AI