Modeling and Detection of Learning in Cognitive Diagnosis

这项研究项目将开发描述学生学习方式的统计模型，并将设计有效的培训方法来帮助他们有效地学习。该项目将通过开发动态认知诊断模型来推动心理测量学理论的发展，该模型可以捕捉学生在培训过程中掌握的技能。它将通过改进电子学习环境和智能辅导系统的设计来影响心理测量学和教育方法，在这些环境中，学生可以接受大量技能的培训。这项研究的最终产品之一将是公开提供的软件，其中将纳入这项工作中将要开发的方法。将在这个项目中获得的理论知识将被纳入研究生水平课程的材料中，这些课程涵盖项目反应理论和顺序分析。两名研究生将在这项研究中发挥关键作用，本科生将参与该项目的某些方面。调查人员将尽一切努力将代表不足群体的合格学生纳入这些研究活动。这项研究将解决两个基本问题。首先，学生如何掌握掌握一系列任务的技能？第二，如何实时选择这些任务，以帮助学生高效学习？第一个问题将随着以认知诊断理论为基础的复杂统计模型的发展而得到解答。第二个问题将需要开发在线算法，以便快速检测学生是否掌握了一项技能，并选择可能的最佳任务，以促进学习。这些算法将通过融合序列变化检测和实验设计领域的统计技术来开发。为解决这两个不同的研究问题而开发的方法将合并，使开发的学习模型为检测和任务选择算法提供信息。总体而言，这个项目将考虑教育和教学实践中核心的统计问题，并将突出认知诊断、潜在课堂建模、最快变化检测和适应性设计等领域之间的相互作用。

This research project will develop statistical models that describe the way students learn and will design efficient training methods to help them learn efficiently. The project will advance psychometric theory by developing dynamic cognitive diagnosis models that capture the skills a student has mastered in the course of his training. It will impact psychometric and educational methodology by improving the design of e-learning environments and intelligent tutoring systems, where students are trained in a large number of skills. One of the final products of this research will be publicly available software that will incorporate the methodologies to be developed in this work. The theoretical knowledge that will be gained in this project will be incorporated into the material of graduate-level courses that cover item response theory and sequential analysis. Two graduate students will play key roles in conducting this research, and undergraduate students will be involved in certain aspects of the project. The investigators will make every effort to include qualified students of underrepresented groups in these research activities. This research will address two fundamental questions. First, how do students acquire the skills to master a series of tasks? Second, how should these tasks be selected in real time in order to help students learn efficiently? The first question will be answered with the development of complex statistical models that are grounded in the theory of cognitive diagnosis. The second question will require the development of on-line algorithms for detecting quickly that a student has mastered a skill and for selecting the best possible tasks in order to facilitate learning. These algorithms will be developed through the fusion of statistical techniques from the fields of sequential change detection and experimental design. The methodologies developed to address these two distinct research questions will be merged by having the developed learning models inform the detection and task-selection algorithms. Overall, this project will consider statistical problems at the heart of educational and instructional practice, and it will highlight the interplay among the fields of cognitive diagnosis, latent class modeling, quickest change detection, and adaptive design.