Peer-Reviewed Conferences:
Shimmei, M., & Matsuda, N. (to appear). Machine-Generated Questions Attract Instructors when Acquainted with Learning Objectives. In N. Wang, G. Rebolledo-Mendez, O. C. Santos, V. Dimitrova & N. Matsuda (Eds.), Proceedings of the International Conference on Artificial Intelligence in Education: Springer. [0.21 acceptance rate out of 251 submissions]
Shimmei, M., and Matsuda, N. (to appear) Can’t Inflate Data? Let the Models Unite and Vote: Data- agnostic Method to Avoid Overfit with Small Data. In R. Agrawal Y. Narahari, M. Pechenizkiy, M. Feng, T. Käser & P. Talukdar (Eds.), Proceedings of the International Educational Data Mining Society.: Springer.
Shimmei, M., & Matsuda, N. (2021). Learning Association between Learning Objectives and Key Concepts to Generate Pedagogically Valuable Questions. In I. Roll & D. McNamara (Eds.), Proceedings of the International Conference on Artificial Intelligence in Education (pp. 320-324, short paper).
Shimmei, M., & Matsuda, N. (2020). Learning a Policy Primes Quality Control: Towards Evidence-Based Automation of Learning Engineering. In A. Rafferty & J. Whitehill (Eds.), Proceedings of the International Conference on Educational Data Mining (pp. 224-232). [Acceptance rate: 30.6% out of 98 submissions]
Shimmei, M., & Matsuda, N.(2019) Evidence-Based Recommendation for Content Improvement using Reinforcement Learning. In S. Isotani, A. Ogan, B. McLaren, E. Millán, P. Hastings & R. Luckin (Eds.), Proceedings of the International Conference on Artificial Intelligence in Education (pp. 369-373). Cham, Switzerland: Springer.
Journal Papers:
Matsuda, N., Wood, J., Shrivastava, R., Shimmei, M., & Bier, N. (2022). Latent Skill Mining and Labeling from Courseware Content. Journal of Educational Data Mining, 14(2), 1-31.
Peer-Reviewed Workshops:
Matsuda, N., & Shimmei, M. (2019) Application of Reinforcement Learning for Automated Contents Validation towards Self-Improving Online Courseware. In B. Goldberg (Ed.), Proceedings of the Annual GIFT User Symposium (pp. 57-65). Orlando, FL: U.S. Army Combat Capabilities Development Command Soldier Center.
Shimmei, M. (2018). An Evidence-based Method for Online Courseware Contents Validation using Reinforcement. Annual Graduate Student Conference on Learning Sciences. Nashville, TN. Vanderbilt University.
Book Chapters:
Matsuda, N., Shimmei, M., Chaudhuri, P., Makam, D., Shrivastava, R., Wood, J., & Taneja, P. (in press). PASTEL: Evidence-based learning engineering methods to facilitate creation of adaptive online courseware. In F. Ouyang, P. Jiao, B. M. McLaren & A. H. Alavi (Eds.), Artificial Intelligence in STEM Education: The Paradigmatic Shifts in Research, Education, and Technology. New York, NY: CSC Press.
Shimmei, M., & Matsuda, N. (2021). Interactive Online Course Engineering Using Reinforcement Learning with Students’ Performance Profile. In H. Jiao & R. Lissitz (Eds.), Enhancing Effective Instruction and Learning Using Assessment Data (pp. 47-59). Charlotte, NC: Information Age Publishing.
Shen, S., Shimmei, M., Chi, M., & Matsuda, N. (2019). Applications of Reinforcement Learning to Self-Improving Educational Systems. In A. M. Sinatra, A. C. Graesser, X. Hu, K. Brawner & V. Rus (Eds.), Design Recommendations for Intelligent Tutoring Systems (Vol. 7: Self-Improving Systems, pp. 77-96). Orlando, FL: US Army Research Lab.
Press:
Pop Quiz: AI Matches Human Performance at Developing Good Test Questions (2023). NC State University News.