CAREER: Learning about Learning

Worcester Polytechnic Institute, Worcester MA

Investigators

Abstract

The proposed project will investigate methods of building web-based intelligent tutoring systems that teach as they assess. The project focuses upon the creation of a system to help 8th grade students learn math for the Massachusetts Comprehensive Assessment Survey (MCAS). The system provides what the PI and his colleagues at CMU call assistments: individual pieces of computer-generated tutoring that combine assessment of learning with scaffolded assistance to the students such that as they receive individualized computer help, teachers can monitor, in real time, precisely the kinds of difficulty students are having and what progress they are making. The project will have 5 main research thrusts: In the first research thrust, designing cognitive models, the PI will use methodologies such as difficulty factors analysis and learning factors analysis in order to create good-fitting models that can predict student learning and transfer. In the second research thrust, inferring what students know and are learning, the PI will study novel model learning and inference mechanisms in order to try to predict when learning and transfer occur. This effort will entail combining psychometric methods with intelligent tutoring systems. In the third thrust, optimizing learning, the PI will focus on discovering what pedagogical strategies used by teachers lead to better learning by students. Through the iterative refining of his models, he will devise mechanisms that select problems that maximize the ratio of expected test-score gain to the expected time needed for completion. In the fourth thrust, informing educators, the PI will study how best to present information from the finer-grained model to teachers. In the final thrust, allowing user adaptation, the PI will attempt to provide a means for allowing K-12 teachers to design the pedagogy used in an intelligent tutoring system. On his website, he will provide the research team's current "best hypothesis" of an ideal 8th grade intelligent tutoring system. Teachers will be able to adapt the system for their own uses, changing existing assistments or creating new problems for their students. The hope is that such systems could eventually be made available to large numbers of teachers.