Collaborative Research: Enabling Robust Learning with Conceptual Personalization Technologies

点击翻译按钮获取中文摘要

Personalized instruction ? instruction that targets individual students? unique learning needs and builds upon their prior knowledge ? is critical for supporting effective science learning. The primary goal in this project is to support robust learning with personalization strategies using natural language technologies. The project is a three-institution collaboration between the University of Colorado, the University Corporation for Atmospheric Research, and the University of Utah. It has two objectives: the technology objective is to create domain-independent techniques to create personalization algorithms, and the learning science objective is to measure the effect of these algorithms on learning. The project focuses on robust learning, i.e., learning the supports transfer and the promotion of meta-cognitive skills. The subject matter is earth science and biology. The proposed techonology would operate as follows. Firstly, the system uses state-of-the-art statistical natural language processing methods to automatically process learning resources (primarily texts) in order to create a domain knowledge map. This includes automatically identifying core concepts in a treatment of the subject matter. Secondly, during learning sessions, the system would analyze students' essays to dynamically construct a domain knowledge map of the students' responses (and an assessment of student understanding). Using graph matching techniques, the system evaluates the student's response, including determining what concepts were missing or misunderstood. Finally, the system uses recommendation engine methods to suggest web resources that could help the student understand the material.This project, by automating many of the processes to identify knowledge and key concepts, has the potential to transform learning. The system is independent of the domain of learning so it can be used for any area of science. The system also does not depend upon skilled teachers ? so it can be effectively used in under-served schools.