ASTRA is an AI-based tool that helps discover math learning strategies by learning them from large-scale datasets. ASTRA will provide learning researchers with a scalable open-source experimentation and analysis platform for math learning. In particular, ASTRA will utilize state-of-the-art advances in AI to learn Large Strategy Models that encode patterns in math learning strategies.
Deepak Venugopal
Associate Professor, University of Memphis
Deepak Venugopal
Associate Professor, University of Memphis
Dr. Deepak Venugopal is an associate professor in computer science at the University of Memphis. His research interests lie in the fields of Machine Learning and Artificial Intelligence. Particularly, he is interested in Neuro-Symbolic and explainable AI and works in applications related to educational technologies.
Vasile Rus
Professor, University of Memphis
Vasile Rus
Professor, University of Memphis
Dr. Rus is the William Dunavant Professor of Computer Science and Director of the Data Science Research Initiative at The University of Memphis. He is also affiliated with the Institute for Intelligent Systems (IIS) where he has been interacting for more than a decade with cognitive scientists developing advanced educational technologies.
Steve Ritter
Founder and Chief Scientist, Carnegie Learning
Steve Ritter
Founder and Chief Scientist, Carnegie Learning
Dr. Steve Ritter is the Founder and Chief Scientist at Carnegie Learning. He has been developing, analyzing and evaluating educational technology for over 20 years. He is well known for making pioneering contributions to ITSs and leading the development of commercial ITSs such as MATHia.
Steve Fancsali
Director of Advanced Analytics, Carnegie Learning
Steve Fancsali
Director of Advanced Analytics, Carnegie Learning
Dr. Steve Fancsali is the director of advanced analytics at Carnegie Learning. His expertise is in statistical and causal modeling of data produced by learners (and teachers) as they interact with artificial intelligence (AI)-driven instructional software. He contributes to and oversees work including data-driven improvements to (and evaluation of) skill and mastery learning models underlying MATHia, developing student-progress monitoring metrics, deploying real-time alerts and analytics for teachers while their students use programs like MATHia, and learning causal models of learner behavior and affect in AI-driven tutors.