Collaborative Research: Enabling Robust Learning with Conceptual Personalization Technologies

University Corporation For Atmospheric Res, Boulder CO

Investigators

Abstract

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. 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 (mainly latent semantic analysis) to automatically process learning resources (primarily texts) in order to create a domain knowledge map. This includes automatically identifying core concepts. 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. For learning assessment, the project uses a 2 (technology or none) by 2 (domain of study) mixed research design using a sample of 40 students. The intent of this project is to make science learning and teaching more effective. The project draws upon a rich set of new tools for analyzing textual materials. The tools serve several functions. They initially allow the analysis of text course materials to automatically develop a description of the core concepts embedded in the texts. The tools then assess essays that the students write about the core materials to determine their individual level of understanding. The tools then provide feedback to the individual students regarding missing or misunderstood concepts. This feedback includes references to the text materials that comprise the course of study and references to additional internet resources that the individual students can use to better understand scientific concepts in biology and earch sciences. This research builds upon 15 years of work on semantic analysis of texts. The research is significant because it works for any subject area and, since it is automated, it can scale nationwide.