Noboru Matsuda

Matsuda, N., Weng, W., & Wall, N. (2020). The Effect of Metacognitive Scaffolding for Learning by Teaching a Teachable Agent. International Journal of Artificial Intelligence in Education, 30(1), 1–37. https://doi.org/10.1007/s40593-019-00190-2

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.

Shimmei, M., & Matsuda, N. (2019). Evidence-Based Recommendation for Content Improvement Using Reinforcement Learning. ARTIFICIAL INTELLIGENCE IN EDUCATION, AIED 2019, PT II, pp. 369–373. https://doi.org/10.1007/978-3-030-23207-8_68

Matsuda, N., & Shimmei, M. (2019). PASTEL: Evidence-based learning engineering method to create intelligent online textbook at scale. CEUR Workshop Proceedings, 2384, 70–80.

Matsuda, N., Sekar, V. P. C., & Wall, N. (2018). Metacognitive scaffolding amplifies the effect of learning by teaching a teachable agent. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 311–323). https://doi.org/10.1007/978-3-319-93843-1_23

Matsuda, N. (2018). The State-of-the-Art Pedagogical Agent Technology in the Field of Learning Science. Transactions of Japanese Society for Information and Systems in Education, 35(1), 13–20. Retrieved from https://doi.org/10.14926/jsise.35.13

Inventado, P. S., Li, Y., Heffernan, N., Inventado, S. G. F., Scupelli, P., Tu, S., … McGuire, P. (2018). Using design patterns for math preservice teacher education. ACM International Conference Proceeding Series. https://doi.org/10.1145/3282308.3282340

Matsuda, N. (2017). Instructional Strategy. In H. Matsubara (Ed.), Encyclopedia of Artificial Intelligence (pp. 1157–1159). Tokyo, Japan: Japan Society of Artificial Intelligence.

Matsuda, N. (2017). Intelligent Pedagogical Agents. In H. Matsubara (Ed.), Encyclopedia of Artificial Intelligence (pp. 1152–1153). Tokyo, Japan: Japan Society of Artificial Intelligence.

Dumdumaya, C., Banawan, M., Rodrigo, M. M., Ogan, A., Yarzebinski, E., & Matsuda, N. (2017). Investigating the effects of cognitive and metacognitive scaffolding on learners using a learning by teaching environment. Proceedings of the 25th International Conference on Computers in Education, ICCE 2017 - Main Conference Proceedings, 1–10.

Matsuda, N. (2017). Natural language processing in educational systems. In H. Matsubara (Ed.), Encyclopedia of Artificial Intelligence (p. 1101). Tokyo, Japan: Society of Artificial Intelligence.

Yarzebinski, E., Dumdumaya, C., Rodrigo, M. M. T., Matsuda, N., & Ogan, A. (2017). Regional cultural differences in how students customize their avatars in technology-enhanced learning. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 598–601). https://doi.org/10.1007/978-3-319-61425-0_73

Matsuda, N., Velsen, M., Barbalios, N., Lin, S., Vasa, H., Hosseini, R., … Bier, N. (2016). Cognitive tutors produce adaptive online course: Inaugural field trial. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9684, pp. 327–333). https://doi.org/10.1007/978-3-319-39583-8_37

Namatame, M., & Matsuda, N. (2016). Development of a Peer Review System for Art Education and its Evaluation. Transactions of Japan Society of Kansei Engineering, 15(4), 425–430. https://doi.org/10.5057/jjske.tjske-d-15-00091

Matsuda, N., Chandrasekaran, S., & Stamper, J. (2016). How quickly can wheel spinning be detected? Proceedings of the 9th International Conference on Educational Data Mining, EDM 2016, 607–608.

Matsuda, N., Barbalios, N., Zhao, Z., Ramamurthy, A., Stylianides, G. J., & Koedinger, K. R. (2016). Tell me how to teach, I’ll learn how to solve problems. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9684, pp. 111–121). https://doi.org/10.1007/978-3-319-39583-8_11

Maclellan, C. J., Harpstead, E., Wiese, E. S., Zou, M., Matsuda, N., Aleven, V., & Koedinger, K. R. (2015). Authoring tutors with complex solutions: A comparative analysis of Example Tracing and SimStudent. CEUR Workshop Proceedings, 1432, 35–44.

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. https://doi.org/10.1016/j.artint.2014.11.002

Koedinger, K. R., Matsuda, N., Maclellan, C. J., & McLaughlin, E. A. (2015). Methods for evaluating simulated learners: Examples from SimStudent. CEUR Workshop Proceedings, 1432, 45–54.

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), 1–34. https://doi.org/10.1007/s40593-014-0020-1

Yarzebinski, E., Ogan, A., Rodrigo, M. M. T., & Matsuda, N. (2015). Understanding students’ use of code-switching in a learning by teaching technology. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9112, pp. 504–513). https://doi.org/10.1007/978-3-319-19773-9_50

MacLellan, C. J., Koedinger, K. R., & Matsuda, N. (2014). Authoring tutors with simstudent: An evaluation of efficiency and model quality. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 551–560). https://doi.org/10.1007/978-3-319-07221-0_70

Matsuda, N., Griger, C. L., Barbalios, N., Stylianides, G. J., Cohen, W. W., & Koedinger, K. R. (2014). Investigating the effect of meta-cognitive scaffolding for learning by teaching. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 104–113). https://doi.org/10.1007/978-3-319-07221-0_13

Matsuda, N., Yarzebinski, E., Keiser, V., Raizada, R., Cohen, W. W., Stylianides, G. J., & Koedinger, K. R. (2013). Cognitive anatomy of tutor learning: Lessons learned with SimStudent. Journal of Educational Psychology, 105(4), 1152–1163. https://doi.org/10.1037/a0031955

Rodrigo, M. M. T., Ong, A., Bringula, R., Basa, R. S., Dela Cruz, C., & Matsuda, N. (2013). Impact of prior knowledge and teaching strategies on learning by teaching. CEUR Workshop Proceedings, 1009, 71–80.

Matsuda, N., Yarzebinski, E., Keiser, V., Raizada, R., Stylianides, G. J., & Koedinger, K. R. (2013). Studying the effect of a competitive game show in a learning by teaching environment. International Journal of Artificial Intelligence in Education, 23(1-4), 1–21. https://doi.org/10.1007/s40593-013-0009-1

MacLellan, C. J., Matsuda, N., & Koedinger, K. R. (2013). Toward a reflective SimStudent: Using experience to avoid generalization errors. CEUR Workshop Proceedings, 1009, 51–60.

Ogan, A., Finkelstein, S., Mayfield, E., D’Adamo, C., Matsuda, N., & Cassell, J. (2012). "Oh, dear Stacy!" Social interaction, elaboration, and learning with teachable agents. Conference on Human Factors in Computing Systems - Proceedings, 39–48. https://doi.org/10.1145/2207676.2207684

Namatame, M., & Matsuda, N. (2012). An application of peer review for art education: A tablet PC becomes a language for students who are hard of hearing. Proceedings 2012 17th IEEE International Conference on Wireless, Mobile and Ubiquitous Technology in Education, WMUTE 2012, 190–192. https://doi.org/10.1109/WMUTE.2012.43

Carlson, R., Keiser, V., Matsuda, N., Koedinger, K. R., & Penstein Rosé, C. (2012). Building a conversational simstudent. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 563–569). https://doi.org/10.1007/978-3-642-30950-2_73

Matsuda, N., Yarzebinski, E., Keiser, V., Raizada, R., Stylianides, G., & Koedinger, K. R. (2012). Motivational factors for learning by teaching: The effect of a competitive game show in a virtual peer-Learning Environment. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 101–111). https://doi.org/10.1007/978-3-642-30950-2_14

Matsuda, N., Cohen, W. W., Koedinger, K. R., Keiser, V., Raizada, R., Yarzebinski, E., … Stylianides, G. (2012). Studying the effect of tutor learning using a teachable agent that asks the student tutor for explanations. Proceedings 2012 4th IEEE International Conference on Digital Game and Intelligent Toy Enhanced Learning, DIGITEL 2012, 25–32. https://doi.org/10.1109/DIGITEL.2012.12

Li, N., Matsuda, N., Cohen, W. W., & Koedinger, K. R. (2011). A machine learning approach for automatic student model discovery. EDM 2011 - Proceedings of the 4th International Conference on Educational Data Mining, 31–40.

Matsuda, N., Yarzebinski, E., Keiser, V., Raizada, R., Stylianides, G. J., Cohen, W. W., & Koedinger, K. R. (2011). Learning by teaching simstudent - An initial classroom baseline study comparing with cognitive tutor. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 213–221). https://doi.org/10.1007/978-3-642-21869-9_29

Matsuda, N., Keiser, V., Raizada, R., Stylianides, G., Cohen, W. W., & Koedinger, K. R. (2011). Learning by teaching simstudent - Interactive event. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (p. 623). https://doi.org/10.1007/978-3-642-21869-9_124

Matsuda, N., Keiser, V., Raizada, R., Stylianides, G., Cohen, W. W., & Koedinger, K. (2010). Learning by teaching SimStudent. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (p. 449). https://doi.org/10.1007/978-3-642-13437-1_106

Matsuda, N., Keiser, V., Raizada, R., Tu, A., Stylianides, G., Cohen, W. W., & Koedinger, K. R. (2010). Learning by teaching SimStudent: Technical accomplishments and an initial use with students. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 317–326). https://doi.org/10.1007/978-3-642-13388-6_36

Li, N., Matsuda, N., Cohen, W. W., & Koedinger, K. R. (2010). Towards a computational model of why some students learn faster than others. AAAI Fall Symposium - Technical Report, FS-10-01, 40–46.

Matsuda, N., Cohen, W. W., Koedinger, K. R., Stylianides, G., Keiser, V., & Raizada, R. (2010). Tuning cognitive tutors into a platform for learning-by-teaching with SimStudent technology. CEUR Workshop Proceedings, 587, 20–25.

Matsuda, N., Cohen, W. W., Sewall, J., Lacerda, G., & Koedinger, K. R. (2008). Why tutored problem solving may be better than example study: Theoretical implications from a simulated-student study. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 111–121). https://doi.org/10.1007/978-3-540-69132-7-16

Matsuda, N., Cohen, W. W., Sewall, J., Lacerda, G., & Koedinger, K. R. (2007). Evaluating a simulated student using real students data for training and testing. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 107–116).

Matsuda, N., Cohen, W. W., & Koedinger, K. R. (2005). Applying programming by demonstration in an intelligent authoring tool for cognitive tutors. AAAI Workshop - Technical Report, WS-05-04, 1–8.

Matsuda, N., & Vanlehn, K. (2004). GRAMY: A geometry theorem prover capable of construction. Journal of Automated Reasoning, 32(1), 3–33. https://doi.org/10.1023/B:JARS.0000021960.39761.b7

Matsuda, N., & VanLehn, K. (2003). Modeling hinting strategies for geometry theorem proving. Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science), 2702, 373–377.

Matsuda, N., & VanLehn, K. (2000). A reification of a strategy for geometry theorem proving. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 1839, p. 660).

Matsuda, N., & Okamoto, T. (1998). Diagrammatic reasoning for geometry ITS to teach auxiliary line construction problems. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 1452, pp. 244–253).

Okamoto, T., Morihiro, K., Matsuda, N., & Takuma, S. (1994). Application of analogical reasoning and extraction of tutoring rules for concept-formation learning. Electronics and Communications in Japan (Part III: Fundamental Electronic Science), 77(3), 75–86. https://doi.org/10.1002/ecjc.4430770307

Matsuda, N., & Okamoto, T. (1992). Student model and its recognition by hypothesis-based reasoning in ITS. Electronics and Communications in Japan (Part III: Fundamental Electronic Science), 75(8), 85–95. https://doi.org/10.1002/ecjc.4430750807