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.

这是一项教师早期职业发展计划（职业）项目。该职业计划是一项全国科学基金会范围的活动，为支持初级教师提供了最负盛名的奖项，他们通过杰出的研究，出色的教育以及教育和研究的融合来体现教师 - scholars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，以支持大学级别的入门统计数据。这项工作代表了教育测试的重大进步，以提供个性化的评估，这反过来又基于学生的能力概况而不是抽象的总体测试评分来产生诊断信息。该研究特别面临着诸如大型样本的需求，识别基础模型的欠发达以及可能限制基于模型的CD-CAT对教育从业者的可用性的困难；尤其是在较小的教学环境中的老师及其学生。鉴于这些考虑因素，该项目首先提出了一种创新的一般非参数分类（GNPC）替代方案，这是非参数分类（NPC）方法的一般版本。 GNPC方法不仅可用于分析大型数据，例如基于课程的数据，符合广泛的认知诊断模型，这使得能够访问其最高需求的人可以访问认知诊断分析的优势。然后，这种现成的启发式方法的开发将领域进一步移动到教育微环境中非参数CD-CAT的常规使用。拟议的算法将应用于在线统计课程中，在该课程中，采用高性能形成性评估来提供有关学生表现效果的有用和及时的反馈。拟议的询问线通过开发在线CD-CAT系统来支持统计数据，将研究和教育整合在一起。教育活动包括在研究生层面的教学和教师和研究人员的专业发展活动。