I am an associate professor of computer science and a director of the Innovative Educational Computing Laboratory at the Department of Computer Science, North Carolina State University. I am also an affiliate of the Center for Educational Informatics and a member of the Digital Transformation of Education cluster at the NCSU Chancellor's Faculty Excellence Program. I earned a PhD in intelligent systems from the Intelligent Systems Program at University of Pitsburgh in 2004.
My primary research focus is on the technology innovation and integration to advance the sciences of computing and human/machine learning.
To make a breakthrough innovation, I value mathematics principles behind the machine learning technologies and AI in general. I am interested in understanding the underlying computation to realize desired technologies to improve the future of education. On that account, I am an AI tech-engineer to build transformative educational technologies.
I am interested in innovating cutting-edge Artificial Intelligence (AI) technologies for students to learn, teachers to teach, and researchers to understand how people learn (and, more importantly, fail to learn!). I study the transformative theory of learning and teaching to understand how people learn and how people should be taught. I am therefore a computer scientist acting as a learning scientist working on the empirical data collected from field studies conducted with the learning technologies that I invent. I also recognize myself as a lifelong practitioner to improve education.
My scholarly expertise thus spans education, learning science, cognitive science, and computer science. Follow this link for an extended research statement (PDF)
See our lab page for full descriptions of current projects: The Innovative Educational Computing Lab
| SimStudent: Intelligent Teachable Pedagogical Agent | |
2005-present [Web] |
SimStudent is a computational model of learning cognitive skills from examples. It is a model of inductive rule learning through tutored-problem solving. Using the SimStudent technology, we study domain-general and domain-specific mechanisms of skill acquisition, which in turn provides us insights into theories of machine- and human-learning. Applications of SimStudent include intelligent authoring (to facilitate authoring of cognitive tutors), teachable agent (to advance the theory of learning by teaching), and learning simulation (to understand how students learn). |
| PASTEL: Evidence-based Methods for Efficient and Practical Learning Engineering | |
2015-present [Web] |
The goal of the PASTEL (Pragmatic methods to develop Adaptive and Scalable Technologies for next generation E-Learning) project is to develop evidence-based methods for efficient and practical learning engineering. In particular, we are interested in developing advanced technologies to build adaptive online courseware. |
| GRAMY & AGT: Advanced Geometry Intelligent Tutoring System | |
1999-2004 [Web] |
How to teach geometry theorem proving with construction? The "construction" here is to add segments into a problem figure using compasses and a straightedge as part of a proof. Two major findings from this project include: (1) Construction can be done algorithmically (c.f., heuristic knowledge for construction a la Polya). (2) Students who were taught proofs with construction as a forward chaining procedure showed better proof-writing performance than those who were taught the backward chaining procedure. |