面向序列诊断的强化计算机自适应测验方法  

Computerized Adaptive Testing Method Based on Reinforcement Learning for Series Diagnosis

作　　者：刘子瑞 吴金泽 姚方舟 刘淇[1] 陈恩红[1] 沙晶 王士进 苏喻 LIU Zirui;WU Jinze;YAO Fangzhou;LIU Qi;CHEN Enhong;SHA Jing;WANG Shijin;SU Yu(School of Computer Science and Technology,University of Sci-ence and Technology of China,Hefei 230027;iFLYTEK Co.,Ltd,Hefei 230088;School of Computer and Artificial Intelligence,Hefei Normal University,Hefei 230061)

机构地区：[1]中国科学技术大学计算机科学与技术学院,合肥230027 [2]科大讯飞股份有限公司,合肥230088 [3]合肥师范学院计算机与人工智能学院,合肥230061

出　　处：《模式识别与人工智能》2024年第1期13-26,共14页Pattern Recognition and Artificial Intelligence

基　　金：国家重点研发计划项目(No.2022YFC3303504)资助。

摘　　要：计算机自适应测验旨在根据学生历史表现为学生选择合适的题目,快速有效地测量学生的真实能力.然而在智能教育场景下,现有自适应测验策略仍存在目标复杂和知识稀疏等问题.为此,文中构建用于智能场景的可精准测评学生知识能力的面向序列诊断的强化计算机自适应测验方法,包括基于序列诊断的学生模拟器和学生画像模型,并针对真实场景中自适应测验的目标复杂性,设计薄弱点准确率、预测表现耦合、自适应测验时长、测验异常率和测验的难度结构这5个评价指标.进一步地,提出基于强化学习的计算机自适应测验选题策略,利用双通道自注意力学习及矛盾学习模块缓解知识稀疏的问题.真实数据上的实验表明,文中选题策略不仅可高效测量学生真实能力,还可优化学生的作答体验,同时选择的题目也具有一定的可解释性,从而为智能教育场景下的计算机自适应测验提供一个可行方案.Computerized adaptive testing is designed to select appropriate questions for students based on their historical performance,thereby measuring their actual ability quickly and effectively.However,in intelligent education scenarios,the existing selection strategy of traditional computerized adaptive testing is still faced with some problems such as target complexity and knowledge sparseness.To solve these problems,a computerized adaptive testing method based on reinforcement learning for series diagnosis is proposed in this paper to accurately assess students′knowledge proficiency for intelligent scenarios.A student simulator and a student portrait model based on series diagnosis model are adopted.To address the complexity of computerized adaptive testing goals in real-world scenarios,five evaluation indicators are designed,including accuracy of weak points,coupling of prediction performance,adaptive testing duration,testing anomaly rate and testing difficulty structure.Furthermore,a selection strategy for reinforcement learning based computerized adaptive testing is proposed.The dual-channel self-attention learning module and the contradiction learning module are utilized to ameliorate knowledge sparseness problem.Experiments on real datasets show that the proposed selection strategy not only efficiently measures students′actual abilities,but also optimizes their answering experience.The selected questions exhibit a certain level of interpretability,and the method provides a feasible solution for computerized adaptive testing in intelligent education scenarios.

关 键 词：智能教育 个性化教育 计算机自适应测验 强化学习 Q 学习 

分 类 号：G434[文化科学—教育学]