PI: Andrew Olney
University of Memphis
This project will develop and evaluate the potential of a new human-computer system that bridges the roles of virtual student and virtual teacher to allow humans and computers to take turns teaching and learning from each other. The key insight is that reading comprehension activities (e.g., vocabulary building, summarizing, question generation, concept mapping) closely parallel the knowledge engineering required to create virtual teachers for intelligent tutoring systems (ITSs). The system links these activities so that when students read online, they engage a virtual student in educational tasks that both improve their reading comprehension and simultaneously contribute to the creation of ITSs for future students. An important aspect of the proposed research is to find the optimum balance between student learning (which benefits the individual) and the creation of ITS knowledge representations (which benefits many). Specific research objectives are: (1) to develop a baseline platform (called BrainTrust) such that students can create ITS knowledge representations by teaching a virtual student; (2) to study the relationship between the student’s ability, the virtual student’s ability, the student’s learning outcomes, and the quality of knowledge representations produced. A distinctive characteristic of the proposed research is the study of these questions in ecologically valid conditions, as students engage in authentic study, while also participating in randomized experiments.
The research may lead to the development of systems that improve reading comprehension, which may have broad benefits given the centrality of reading comprehension to all learning. In particular, problems with reading comprehension have been linked to first-year college student dropout that disproportionately affects African-American students. The research will also enhance infrastructure for research and education through the development and dissemination of the BrainTrust platform, a next-generation computing infrastructure to rapidly create and deploy ITSs tailored to specific needs. If this exploratory project demonstrates that the dual outcomes of human learning and high-quality knowledge representations can be achieved, it will open a new area of research that brings teaching these virtual students full circle with learning from their derived intelligent tutoring systems.