Innovative Educational Computing Laboratory
As a special focused group at the Center for Educational Informatics, we are interested in building innovative advanced learning technologies. Our technology innovation focuses on artificial intelligence such as new machine learning algorithms and new big data mining methods. We are also interested in studying innovative applications of existing technologies.
Our central mission is to advance a theory of advanced educational computing for practical and theoretical purposes: (1) To provide better learning and instructional opportunities for students and teachers, and (2) to advance theories of how people learn. We conjecture that adaptive learning technologies, that are capable of building a model of individual students' competency and dynamically change their instructions based on students' needs, have a great potential to implement a next-generation learning. We also suppose that data showing students' activity logs, collected while students are using our innovation, provides us great potential to understand how people learn when data mining techniques are applied appropriately (aka Educational Data Mining). We develop our innovation with evidence-based techniques through data analysis and often have the machine learn to perform better through its interaction with students (aka self-improving teaching machine).
We apply human-centered research methods and iterative design-engineering techniques. We start with understanding what students and teachers need. We often conduct contextual inquiry and customer interviews to identify pain points. Lessons learned from such customer research will give us insight into a design for the desired technology. We develop prototypes that will be iteratively improved through multiple user tests. For a summative evaluation, we conduct field studies to test our invention in the authentic learning settings, known as "in-vivo" study. The field study provides us with legitimate knowledge and insights on how students learn with the proposed technology.
We dream to change the world with technology innovation. Everybody should have equal opportunity to receive a high-quality education and a need-based assistance. We spend our entire professional career to make this dream come true.
SimStudent is a computational model of learning that allows us to investigate a cognitive mechanism of human learning, the theory of how students learn by teaching, and the impact of interactive machine learning as a tool for creating a computer tutor.
iCIT ("I see it")
An emerging project to investigate how tangible computing and mixed reality technologies help students learn complex concepts by making otherwise-invisible entities visible and tangible.
We are always looking for motivated and talented students (all levels of grad and underegrad). Interested student should send a current CV along with a one-page research statement to Dr. Noboru Matsuda <Noboru.Matsuda@ncsu.edu>