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

这是一个教师早期职业发展计划(Career)项目。职业生涯计划是国家科学基金会范围内的一项活动，为那些通过杰出的研究、出色的教育以及教育和研究的整合来体现教师学者作用的初级教师提供最有声望的奖项。调查员将开发一个认知诊断计算机化自适应测试(CD-CAT)系统，以支持在大学一级学习介绍性统计。这项工作代表着教育测试在提供个性化评估方面的重大进步，这些评估反过来又根据学生的熟练程度概况生成诊断信息，而不是抽象的总体测试分数。这项研究特别涉及一些困难，例如需要大样本、在确定基本模式方面不发达，以及可能的技术复杂性限制了教育从业者，特别是较小教学环境中的教师和学生获得基于模型的CD-CAT的机会。基于这些考虑，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方法不仅可用于分析符合广泛认知诊断模型的大型数据，还可用于分析小规模数据，例如基于课程的数据，这一优势使认知诊断分析适用于最有需求的人。这种现成的启发式方法的发展使这一领域进一步走向非参数CD-CAT在教育微环境中的常规使用。拟议的算法将应用于在线统计课程，在这些课程中，使用高性能形成性评估来提供关于学生表现的有用和及时的反馈。拟议的调查路线通过开发一个支持统计学习的在线CD-CAT系统，将研究和教育结合起来。教育活动既包括研究生阶段的教学，也包括教师和研究人员的专业发展活动。

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.