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: 2423762
• 负责人: Chia-Yi Chiu
• 金额: $ 73.12万
• 依托单位: Teachers College, Columbia University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-04-01 至 2025-01-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2423762&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。职业生涯计划是一个国家科学基金会范围内的活动，提供最负盛名的奖项，以支持初级教师谁通过杰出的研究，优秀的教育和教育与研究的整合，发挥教师学者的作用。研究人员将开发一个认知诊断计算机化自适应测试（CD-CAT）系统，以支持在大学一级的入门统计学的学习。这项工作代表了教育测试的一个重大进步，提供个性化的评估，从而根据学生的能力概况生成诊断信息，而不是抽象的整体测试分数。该研究专门涉及的困难，如大样本的要求，欠发达的基础模型的识别，可能的技术复杂性限制了基于模型的CD-CAT的教育从业者的可用性，特别是教师和他们的学生在较小的教学设置。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，这是非参数分类（NPC）方法的通用版本。GNPC方法不仅可用于分析大规模数据，还可用于分析小规模数据，例如基于课程的数据，符合广泛的认知诊断模型，这一优势使认知诊断分析能够为最需要的人所用。这种现成的启发式方法的发展，然后进一步走向非参数CD-CAT在教育微观环境中的常规使用领域。所提出的算法将应用于在线统计课程，在那里高性能的形成性评估是用来提供有用的和及时的反馈，以及学生的表现。拟议的调查路线通过开发在线CD-CAT系统支持统计学习，将研究和教育结合起来。教育活动包括研究生一级的教学和教师和研究人员的专业发展活动。