This project aims to better support student learning by adapting computer-based tutoring to individual learning phases and real-time capabilities. The specific research goal is to explore a method for automated sensor-based learner/learning assessment in intelligent tutoring systems.
VizWiz answers questions that people who are blind or visually impaired have about the things around them. Users take a photo, ask a question, and receive answers back quickly from people on the web (the crowd). VizWiz leverages color identification, text recognition, and recognition of objects seen before, but automatic methods are limited to a small subset of questions in practice. The bulk of remaining questions are answered by humans, e.g.
Cell phone applications that enable children in the villages and slums in the developing world to acquire language and literacy in immersive, game-like environments. These applications target localized language learning needs and aim to make literacy resources more accessible to underprivileged children, at times and places that are more convenient than schools.
SimStudent is a synthetic student that learns cognitive skills from worked-out examples (i.e., demonstrated solutions) and through tutored problem-solving. Technically, SimStudent is an application of programing by demonstration to generate a cognitive model.
Computational Modeling and Data Mining is one of four thrusts within the Pittsburgh Science of Learning Center. This thrust is pursuing the scientific goal of using large-scale data to advance precise, computational theories of how students learn academic content.
Metacognition and Motivation is one of four thrusts of the Pittsburgh Science of Learning Center. It has the goals of developing a better understanding of how metacognitive processes and motivation interact with learner factors to influence robust student learning outcomes. Researchers are testing whether and how student learning environments can leverage improved metacognition and motivation to increase the robustness of student learning.
Social and Communicative Factors is one of four thrusts within the Pittsburgh Science of Learning Center. The research investigates how human linguistic interaction works in instruction and learning, and how participants in learning exchanges (both teachers and students) can best be taught productive forms of interaction.
Cognitive Factors is one of four thrusts of the Pittsburgh Science of Learning Center. It is aimed at understanding cognitive learning—changes in knowledge—that result from instructional events.
The ACT-R project focuses on developing a robust and general cognitive architecture and applying it to the modeling of human interaction with complex dynamic simulated environments and the creation of synthetic human-like agents.
Cognitive Tutors have been successful in raising students’ math test scores in high-school and middle-school classrooms, but their development requires considerable time and expertise. We are developing a set of authoring tools to make modeling both easier for experts and possible for novices in cognitive science. The tools draw on ideas of programming by demonstration, structured editing, and others. Careful application of HCI methods is key.
