Noboru Matsuda
Associate Professor of Cyber STEM Education, Teaching, Learning, and Culture
Office#: 420B Harrington Office Building
Mail Stop: 4232
Office Phone: Not provided
Directory Title: Associate Professor of Cyber STEM Education
Vita: View Document
Web Site:
Short Bio
My primary research focus is on the technology innovation and integration to advance the sciences of learning.

I am interested in the innovation and application of Artificial Intelligence technologies for students to learn, teachers to teach, and researchers to understand how people learn (and, more importantly, fail to learn!). I am therefore an engineer of transformative technologies and a practitioner to improve education.

I am also interested in studying the transformative theory of learning and teaching that brings us with the significant knowledge on how people learn and how people should be taught. I am therefore a learning scientist working on the empirical data collected from field studies conducted with the learning technologies that I invent.

My scholarly expertise thus spans education, learning science, cognitive science, and computer science.
Publications (journal articles, books, book chapters)*

1. Namatame, M., & Matsuda, N. (2016) Development of a peer review system for art education and its evaluation. Transactions of Japan Society of Kansei Engineering.

2. Toyose, K., Asaba, N., Yamaguchi, H., & Nishino, K., Matsuda, N. (2015). Application of Waka-Kansei Database for Learning Japanese Waka in Middle School. Japan Journal of Educational Technology, 38(4), 329-340

3. Blessing, S. B., Aleven, V., Gilbert, S. B., Heffernan, N. T., Matsuda, N., & Mitrovic, A. (2015). Authoring Example-based Tutors for Procedural Tasks. In R. Sottilare, A. Graesser, X. Hu & K. Brawner (Eds.), Design Recommendations for Adaptive Intelligent Tutoring Systems: Authoring Tools (Vol. 3, pp. 71-94)

4. Li, N., Matsuda, N., Cohen, W. W., & Koedinger, K. R. (2015). Integrating representation learning and skill learning in a human-like intelligent agent. Artificial Intelligence, 219, 67-91. doi: [Impact factor: 2.709]

5. Matsuda, N., Cohen, W. W., & Koedinger, K. R. (2015). Teaching the Teacher: Tutoring SimStudent leads to more Effective Cognitive Tutor Authoring. International Journal of Artificial Intelligence in Education, 25, 1-34.

* Publication was joint-authored with students
Diss Publication was from a dissertation
ROS Publication was from a record of study

College of Education and Human Development Grants and Contracts (Current)

Catapult Round 3 - Learning to Write in a Digital Age: Technology-Enhanced Intervention for Young At-Risk Writers (Funded amount $30,000). (Co-PI)
TAMU College of Education & Human Development (State)
2016/12/01 - 2018/05/31
Total Funding: 0.

EXP: Exploratory Study on the Adaptive Online Course and its Implication on Synergetic Competency. (PI)
NSF (Federal)
2016/09/01 - 2018/08/31
Total Funding: 566,000.

Data-Driven Methods to Improve Student Learning from Online Courses. (PI)
NSF (Federal)
2015/09/01 - 2017/07/03
Total Funding: 311,579.

Learning by Teaching a Synthetic Peer: Investigating the effect of tutor scaffolding for tutor learning. (PI)
NSF (Federal)
2015/09/01 - 2017/09/30
Total Funding: 676,638.
Courses Taught
2016 - 2017
EDCI 689
2015 - 2016
EDCI 689, MASC 351
TLAC Faculty   |   TLAC Staff