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Modeling and Detection of Learning in Cognitive Diagnosis

认知诊断中学习的建模和检测

基本信息

批准号：
1632023
负责人：
Georgios Fellouris
金额：
$ 38.96万
依托单位：
University of Illinois at Urbana-Champaign
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2016
资助国家：
美国
起止时间：
2016-08-15 至 2019-07-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1632023&HistoricalAwards=false
关键词：
Modeling Detection Learning Cognitive Diagnosis

项目摘要

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.

该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高效地学习。该项目将通过开发动态认知诊断模型来推进心理测量学理论，这些模型可以捕捉学生在训练过程中掌握的技能。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，在这些系统中，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可用的软件，它将包含在这项工作中开发的方法。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，包括项目反应理论和序列分析。两名研究生将在进行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在这些研究活动中纳入代表性不足群体的合格学生。这项研究将解决两个基本问题。首先，学生如何获得掌握一系列任务的技能？其次，这些任务应该如何实时选择，以帮助学生有效地学习？第一个问题将在基于认知诊断理论的复杂统计模型的发展中得到解答。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通过将开发的学习模型告知检测和任务选择算法而合并。总体而言，本项目将考虑教育和教学实践的核心统计问题，并将强调认知诊断、潜在类建模、最快变化检测和自适应设计等领域之间的相互作用。