Noboru Matsuda

Shimmei, M., Bier, N., & Matsuda, N. (2023). Machine-Generated Questions Attract Instructors When Acquainted with Learning Objectives. https://doi.org/10.1007/978-3-031-36272-9_1

Shahriar, T., & Matsuda, N. (2023). What and How You Explain Matters: Inquisitive Teachable Agent Scaffolds Knowledge-Building for Tutor Learning. https://doi.org/10.1007/978-3-031-36272-9_11

Rodrigo, M. M., Matsuda, N., Cristea, A. I., & Dimitrova, V. (Eds.). (2022). Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners’ and Doctoral Consortium. https://doi.org/10.1007/978-3-031-11647-6

Matsuda, N., Lv, D., & Zheng, G. (2022, August 31). Teaching How to Teach Promotes Learning by Teaching. INTERNATIONAL JOURNAL OF ARTIFICIAL INTELLIGENCE IN EDUCATION, Vol. 8. https://doi.org/10.1007/s40593-022-00306-1

Shahriar, T., & Matsuda, N. (2021). "Can You Clarify What You Said?": Studying the Impact of Tutee Agents' Follow-Up Questions on Tutors' Learning. ARTIFICIAL INTELLIGENCE IN EDUCATION (AIED 2021), PT I, Vol. 12748, pp. 395–407. https://doi.org/10.1007/978-3-030-78292-4_32

Zimmer, W. K., McTigue, E. M., & Matsuda, N. (2021). Development and validation of the teachers' digital learning identity survey. INTERNATIONAL JOURNAL OF EDUCATIONAL RESEARCH, 105. https://doi.org/10.1016/j.ijer.2020.101717

Shimmei, M., & Matsuda, N. (2021). Learning Association Between Learning Objectives and Key Concepts to Generate Pedagogically Valuable Questions. ARTIFICIAL INTELLIGENCE IN EDUCATION (AIED 2021), PT II, Vol. 12749, pp. 320–324. https://doi.org/10.1007/978-3-030-78270-2_57

Matsuda, N. (2021, July 12). Teachable Agent as an Interactive Tool for Cognitive Task Analysis: A Case Study for Authoring an Expert Model. INTERNATIONAL JOURNAL OF ARTIFICIAL INTELLIGENCE IN EDUCATION, Vol. 32. https://doi.org/10.1007/s40593-021-00265-z

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, Vol. 11626, 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. Retrieved from http://www.scopus.com/inward/record.url?eid=2-s2.0-85067813785&partnerID=MN8TOARS

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. Retrieved from http://www.scopus.com/inward/record.url?eid=2-s2.0-85051544348&partnerID=MN8TOARS

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. Retrieved from http://www.scopus.com/inward/record.url?eid=2-s2.0-85067333848&partnerID=MN8TOARS

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

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.

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 Intelligent Tutoring Systems: Authoring Tools & Expert Modeling Techniques (Vol. 3, pp. 71–94). Orlando, FL: U.S. Army Research Laboratory.

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. Retrieved from http://www.scopus.com/inward/record.url?eid=2-s2.0-84944328396&partnerID=MN8TOARS

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. Retrieved from http://www.scopus.com/inward/record.url?eid=2-s2.0-84944315929&partnerID=MN8TOARS

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., Geli, R. I. A. M., Ong, A., Vitug, G. J. G., Bringula, R., Basa, R. S., … Matsuda, N. (2013). Exploring the Implications of Tutor Negativity Towards a Synthetic Agent in a Learning-by-Teaching Environment. Philippine Computing Journal, 8(1), 15–20.

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. Retrieved from http://www.scopus.com/inward/record.url?eid=2-s2.0-84924982067&partnerID=MN8TOARS

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. Retrieved from http://www.scopus.com/inward/record.url?eid=2-s2.0-84924980730&partnerID=MN8TOARS

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

Toyose, H., Nishino, N., Asaba, N., & Matsuda, N. (2012). An empirical study on the effect of Kansei-database for middle school students to learn Waka-reading comprehension. Japan Journal of Educational Technology, 36(2), 125–134.

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. Retrieved from http://www.scopus.com/inward/record.url?eid=2-s2.0-84863408562&partnerID=MN8TOARS

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. Retrieved from http://www.scopus.com/inward/record.url?eid=2-s2.0-79960149222&partnerID=MN8TOARS

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. Retrieved from http://www.scopus.com/inward/record.url?eid=2-s2.0-84888214372&partnerID=MN8TOARS

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). Retrieved from http://www.scopus.com/inward/record.url?eid=2-s2.0-37249054311&partnerID=MN8TOARS

Matsuda, N. (2006). How to get a Ph.D in America. In A. Arimoto & I. Kitagaki (Eds.), University Authority (pp. 132–137). Tokyo, Japan: Minervashobo Publishers Inc.

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. Retrieved from http://www.scopus.com/inward/record.url?eid=2-s2.0-33646048713&partnerID=MN8TOARS

Matsuda, N. (2005). Instructional strategies. In H. Tanaka (Ed.), Encyclopedia of Artificial Intelligence. Tokyo, Japan: Japan Society of Artificial Intelligence.

Matsuda, N. (2005). Natural language processing in educational systems. In H. Tanaka (Ed.), Encyclopedia of Artificial Intelligence. Tokyo, Japan: Japan Society of Artificial Intelligence.

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. Retrieved from http://www.scopus.com/inward/record.url?eid=2-s2.0-8344255630&partnerID=MN8TOARS

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). Retrieved from http://www.scopus.com/inward/record.url?eid=2-s2.0-84944321228&partnerID=MN8TOARS

Matsuda, N. (1999). Cognitive model of geometry theorem proving with construction and its application to intelligent tutoring systems. In Y. Sugiyama (Ed.), Towards new practical theories in mathematics education. Tokyo, Japan: Toyokan Publishers Inc.

Ochi, T., Matsuda, N., & Okamoto, T. (1998). An object oriented distributed working environment to integrate cooperative work and personal work. Transactions of Information Processing Society of Japan, 39(1), 123–130.

Yoshida, T., Matsuda, N., & Okamoto, T. (1997). The system for supporting to learn/diagnose Z notation. Transaction of Japan Society for Information and Systems in Education, 14(1), 3–12.

Okamoto, T., Matsuda, N., & Sasaki, H. (1996). Intelligent CAI for geometric theorem proving with dynamic manipulative interface. Transactions of Information Processing Society of Japan, 37(9), 1679–1687.

Okamoto, T., Matsuda, N., & Furiya, T. (1995). A study of the relationship between programming abilities and academic achievement in junior high school mathematics. Japan Journal of Educational Technology, 19(2), 85–100.

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

Okamoto, T., Matsuda, N., & Yasuda, K. (1994). Study of CAI with algorithm diagnosis system for novice C programmers. Journal of Japan Society for CAI, 11(2), 63–74.

Matsuda, N. (1993). Computer networking. In T. Okamoto (Ed.), Introduction to Information Education for Teachers: Cases in High-School Education (pp. 180–197). Tokyo: Personal Media.

Matsuda, N., Nagashima, S., Okamoto, T., & Takuma, S. (1993). On the system of learning and diagnosis for fostering space concept. Journal of Japan Society for CAI, 10(3), 114–121.

Matsuda, N., & Okamoto, T. (1993). Student modeling for procedural problem solving. IEICE Transactions on Information and Systems, E77-D(1), 49–56.

Matsuda, N. (1992). Foundations of Computers. In T. Okamoto (Ed.), Introduction to Information Education for Teachers: Cases in Middle-School Education (pp. 88–119). Tokyo, Japan: Personal Media.

Matsuda, N., & Okamoto, T. (1992). Mental model of the process of composing geometric proofs using an intelligent tutoring system. Japan Journal of Educational Technology, 15(4), 167–182.

Okamoto, T., & Matsuda, N. (1992). Overview on the studies of intelligent CAIs/ITSs in Japan. Educational Technology Research, 15(1-2), 1–8.

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

Okamoto, T., Matsuda, N., & Takuma, S. (1991). A knowledge based CAD to support students’ learning elementary geometric concepts and diagnosing their misconceptions. Japan Journal of Educational Technology, 14(4), 147–157.

Matsuda, N., & Okamoto, T. (1990). An automatic generation of knowledge-base for an intelligent CAI on geometry theorem proving and a GUI to draw geometric figures. Transactions of the Institution of Electronics, Information, and Communication Engineering, J73-D-II(1), 88–99.

Matsuda, N., & Hatano, K. (1990). Knowledge communication. In T. Okamoto & R. Mizoguchi (Eds.), Artificial Intelligence and Tutoring Systems (pp. 447–456). Tokyo, Japan: Ohmu Inc.

Matsuda, N. (1990). What is CAI? In T. Okamoto (Ed.), Introduction to C Programming (pp. 201–236). Tokyo, Japan: Personal Media.

Okamoto, T., & Matsuda, N. (1989). Learning to recognize students’ plan in geometry proof using intelligent CAI. Transactions of Information Processing Society of Japan, 30(8), 1046–1057.

Okamoto, T., & Matsuda, N. (1988). An intelligent CAI for geometry proof. Transactions of Information Processing Society of Japan, 29(3), 311–324.

Matsuda, N. (1988). Drill, Practice, and Machine Learning. In T. Okamoto, K. Akahori, & S. Yokoyama (Eds.), Computer environments for children (pp. 21–40). Tokyo, Japan: Personal Media.