CAREER: Cognitive Diagnosis in E-Learning: A Nonparametric Approach for Computerized Adaptive Testing

职业：电子学习中的认知诊断：计算机自适应测试的非参数方法

• 批准号: 1552563
• 负责人: Chia-Yi Chiu
• 金额: $ 73.12万
• 依托单位: Rutgers University New Brunswick
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-07-01 至 2022-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1552563&HistoricalAwards=false
• 关键词: CAREER; Cognitive; Diagnosis; Learning; Nonparametric

This is a Faculty Early Career Development Program (CAREER) project. The CAREER program is a National Science Foundation-wide activity that offers the most prestigious awards in support of junior faculty who exemplify the role of teacher-scholars through outstanding research, excellent education and the integration of education and research. The investigator will develop a Cognitive Diagnostic Computerized Adaptive Testing (CD-CAT) system to support the learning of introductory statistics at the university level. The work represents a significant advance in educational testing to provide personalized assessments which in turn generate diagnostic information based on students' proficiency profiles, as opposed to an abstract overall test score. The research deals specifically with difficulties such as the requirement of large samples, underdevelopment in identification of the underlying model, and possible technical complexity limiting the availability of the model-based CD-CAT to educational practitioners; especially teachers and their students in smaller instructional settings. In light of these considerations, the project first proposes an innovative general nonparametric classification (GNPC) alternative, which is a general version of the nonparametric classification (NPC) method. The GNPC method can be used to analyze not only large- but also small-scale data, such as course-based data, conforming to a broad range of cognitive diagnostic models, an advantage that makes cognitive diagnosis analysis accessible to those who are in its highest need. The development of this ready-to-use heuristic method then moves the field further toward the routine use of the nonparametric CD-CAT within educational micro-environments. The proposed algorithms will be applied to online statistics courses, where high-performance formative assessments are employed to provide useful and timely feedback on how well students perform. The proposed line of inquiry integrates research and education through the development of an online CD-CAT system to support the learning of statistics. The education activities include both teaching at the graduate level and professional development activities for teachers and researchers.

这是一个教师早期职业发展计划（CAREER）项目。职业计划是一项全国科学基金会范围内的活动，提供最负盛名的奖项，以支持通过杰出的研究、卓越的教育以及教育与研究的融合体现教师学者角色的初级教师。研究人员将开发认知诊断计算机自适应测试（CD-CAT）系统，以支持大学水平的入门统计学学习。这项工作代表了教育测试领域的重大进步，可提供个性化评估，进而根据学生的能力状况（而不是抽象的总体测试分数）生成诊断信息。该研究专门解决了诸如需要大样本、底层模型识别不发达以及可能的技术复杂性限制了基于模型的 CD-CAT 对教育从业者的可用性等困难；尤其是在较小教学环境中的教师和学生。鉴于这些考虑，该项目首先提出了一种创新的通用非参数分类（GNPC）替代方案，它是非参数分类（NPC）方法的通用版本。 GNPC方法不仅可以分析大规模数据，还可以分析小规模数据，例如基于课程的数据，符合广泛的认知诊断模型，这一优势使得认知诊断分析对于那些处于其领域的人来说是容易的。最高的需要。这种即用型启发式方法的开发进一步推动了该领域在教育微环境中常规使用非参数 CD-CAT。所提出的算法将应用于在线统计课程，其中采用高性能形成性评估来为学生的表现提供有用且及时的反馈。拟议的探究路线通过开发在线 CD-CAT 系统来支持统计学习，从而将研究和教育结合起来。教育活动包括研究生教学以及教师和研究人员的专业发展活动。