Modeling and Detection of Learning in Cognitive Diagnosis

University Of Illinois At Urbana-Champaign, Urbana IL

Investigators

Abstract

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.