Publications

Learning by Teaching / Teachable Agent

Journal Papers:

Matsuda, N., Lv, D., & Zheng, G. (2023). Teaching How to Teach Promotes Learning by Teaching. International Journal of Artificial Intelligence in Education, 1-32. doi: 10.1007/s40593-022-00306-1. 

Matsuda, N. (2022). 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, 32, 48-75. doi: 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. 

Matsuda, N., Yarzebinski, E., Keiser, V., Raizada, R., Stylianides, G. J., & Koedinger, K. R. (2013). Studying the Effect of Competitive Game Show in a Learning by Teaching Environment. International Journal of Artificial Intelligence in Education, 23(1-4), 1-21. Invited paper for the special issue on the Best of ITS 2012

Matsuda, N., Yarzebinski, E., Keiser, V., Raizada, R., William, W. C., Stylianides, G. J., & Koedinger, K. R. (2013). Cognitive anatomy of tutor learning: Lessons learned with SimStudent. Journal of Educational Psychology, 105(4), 1152-1163. doi: 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.

Peer Reviewed Conference Papers:

Shahriar, T., & Matsuda, N. (2024). “I am confused! How to differentiate between…?” Adaptive Followup Questions Facilitate Tutor Learning with Effective Time-on-task. In Andrew, Irene & Zitao (Eds.), Proceedings of the International Conference on Artificial Intelligence in Education (pp. 1-14): Springer. [0.15 acceptance rate out of 334 submissions]

Shahriar, T., & Matsuda, N. (2023). What and how you explain matters: Inquisitive Teachable Agent Scaffolds Knowledge-building for Tutor Learning. In N. Wang, G. Rebolledo-Mendez, O. C. Santos, V. Dimitrova & N. Matsuda (Eds.), Proceedings of the International Conference on Artificial Intelligence in Education (pp. 126-138): Springer. [0.21 acceptance rate out of 251 submissions]

Shahriar, T., & Matsuda, N. (2021). Can you clarify what you said?—Studying the impact of tutee agent’s follow-up questions on tutor’s learning. In I. Roll & D. McNamara (Eds.), International Conference on Artificial Intelligence in Education (pp.1-10). [0.24 acceptance rate out of 168 submissions]

Matsuda, N., Sekar, V. P. C., & Wall, N. (2018). Metacognitive scaffolding amplifies the effect of learning by teaching a teachable agent. In B. McLaren & B. du Boulay (Eds.), Proceedings of the International Conference on Artificial Intelligence in Education (pp. 311-323).

Matsuda, N., Barbalios, N., Zhao, J., Ramamurthy, A., Stylianides, G., & Koedinger, K. R. (2016). Tell me how to teach, I’ll learn how to solve problems. In A. Micarelli, J. Stamper & K. Panourgia (Eds.), Proceedings of the International Conference on Intelligent Tutoring Systems (pp. 111-121). Switzerland: Springer.

Yarzebinski, E., Ogan, A., Rodrigo, M. M. T., & Matsuda, N. (2015). Understanding Students’ Use of Code-switching in a Learning by Teaching Technology. In C. Conati & N. Heffernan (Eds.), Proceedings of the international conference on artificial intelligence in education (pp. 504-516). [0.28 acceptance rate out of 170 submissions]

Matsuda, N., Griger, C. L., Barbalios, N., Stylianides, G., Cohen, W. W., & Koedinger, K. R. (2014). Investigating the Effect of Meta-Cognitive Scaffolding for Learning by Teaching. In S. Trausen-Matu & K. Boyer (Eds.), Proceedings of the International Conference on Intelligent Tutoring Systems (pp.104-113). Switzerland: Springer [0.18 acceptance rate out of 177 submissions]

Matsuda, N., Yarzebinski, E., Keiser, V., Raizada, R., William, W. C., Stylianides, G., et al. (2012). Shallow learning as a pathway for successful learning both for tutors and tutees. In N. Miyake, D. Peebles & R. P. Cooper (Eds.), Proceedings of the Annual Conference of the Cognitive Science Society (pp. 731-736). Austin, TX: Cognitive Science Society. [0.38 acceptance rate out of 537 submissions]

Matsuda, N., Yarzebinski, E., Keiser, V., Raizada, R., Stylianides, G., Cohen, W. W., et al. (2012). Motivational factors for learning by teaching: The effect of a competitive game show in a virtual peer-learning environment. In S. Cerri & W. Clancey (Eds.), Proceedings of International Conference on Intelligent Tutoring Systems (pp. 101-111). Heidelberg, Berlin: Springer-Verlag. [0.16 acceptance rate out of 177 submissions]  A finalist for the best paper award.

Carlson, R., Matsuda, N., Koedinger, K. R., & Rose, C. (2012). Building a Conversational SimStudent. In S. Cerri & W. Clancey (Eds.), Proceedings of International Conference on Intelligent Tutoring Systems (pp. 563-569). Heidelberg, Berlin: Springer-Verlag.

Matsuda, N., Cohen, W. W., Koedinger, K. R., Keiser, V., Raizada, R., Yarzebinski, E., et al. (2012). Studying the Effect of Tutor Learning using a Teachable Agent that asks the Student Tutor for Explanations. In M. Sugimoto, V. Aleven, Y. S. Chee & B. F. Manjon (Eds.), Proceedings of the International Conference on Digital Game and Intelligent Toy Enhanced Learning (DIGITEL 2012) (pp. 25-32). Los Alamitos, CA: IEEE Computer Society. [13% acceptance rate out of 56 submissions]   A finalist for the best paper award.

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. Proceedings of CHI2012 (39-48). [0.23 acceptance rate out of 1577 submissions]

Matsuda, N., Yarzebinski, E., Keiser, V., Raizada, R., Stylianides, G., Cohen, W. W., et al. (2011). Learning by Teaching SimStudent – An Initial Classroom Baseline Study comparing with Cognitive Tutor. In G. Biswas & S. Bull (Eds.), Proceedings of the International Conference on Artificial Intelligence in Education (pp. 213-221): Springer.   [0.32 acceptance rate, of 153]

Matsuda, N., Keiser, V., Raizada, R., Tu, A., Stylianides, G., Cohen, W. W., et al. (2010). Learning by Teaching SimStudent: Technical Accomplishments and an Initial Use with Students. In V. Aleven, J. Kay & J. Mostow (Eds.), Proceedings of the International Conference on Intelligent Tutoring Systems (pp. 317-326). Heidelberg, Berlin: Springer.

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. In Proceedings of the International Workshop on Adaptation and Personalization in E-B/Learning using Pedagogic Conversational Agents (APLeC) (pp.20-25), Hawaii.

Other papers

Matsuda, N., Stylianides, G. J., & Koedinger, K. R. (2015). Studying the Effect of Guided Learning by Teaching in Learning Algebra Equations Paper presented at the Annual Meeting of the American Educational Research Association Chicago, IL.

Matsuda, N., Stylianides, G. J., Cohen, W. W., & Koedinger, K. R. (2014). Using a Synthetic Peer to Investigate the Effect of Competitive Learning by Teaching in Mathematics. Paper presented at the Annual Meeting of the American Educational Research Association Philadelphia, PA

Rodrigo, M. M. T., Ong, A., Bringula, R. P., Basa, R. S., Cruz, C. D., & Matsuda, N. (2013). Impact of Prior Knowledge and Teaching Strategies on Learning by Teaching. In G. McCalla & J. Champaign (Eds.), Proceedings of the AIED Workshop on Simulated Learners (pp. 71-80)

Matsuda, N., Cohen, W. W., Koedinger, K. R., & Stylianides, G. (2010). Learning to solve algebraic equations by teaching a computer agent. In M. F. Pinto & T. F. Kawasaki (Eds.), Proceedings of the Conference of the International Group for the Psychology of Mathematics Education (Vol. 2, pp. 69).

Computational Model of Learning

Journal Papers:

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: http://dx.doi.org/10.1016/j.artint.2014.11.002

Li, N., Schreiber, A., Cohen, W., & Koedinger, K. (2012). Efficient complex skill acquisition through representation learning. Advances in Cognitive Systems, 2, 149-166

Peer Reviewed Conference Papers:

Li, N., Stampfer, E., Cohen, W., & Koedinger, K.R. (2013). General and efficient cognitive model discovery using a simulated student. In M. Knauff, N. Sebanz, M. Pauen, I. Wachsmuth (Eds.), Proceedings of the 35th Annual Conference of the Cognitive Science Society. (pp. 894-9) Austin, TX: Cognitive Science Society.

Li, N., Tian, Y., Cohen, W., & Koedinger, K.R. (2013). Integrating perceptual learning with external world knowledge in a simulated student. InH.C. Lane, K. Yacef, J. Mostow, & P. Pavlik (Eds.), Proceedings of the 16th International Conference on Artificial Intelligence in Education, pp. 400-410. 

Li, N., Cohen, W. W., & Koedinger, K. R. (2012). Efficient cross-domain learning of complex skills. In S. Cerri & W. Clancey (Eds.), Proceedings of International Conference on Intelligent Tutoring Systems (pp.493-498).

Li, N., Cohen, W. W., & Koedinger, K. R. (2012). Problem order implications for learning transfer. In S. Cerri & W. Clancey (Eds.), Proceedings of International Conference on Intelligent Tutoring Systems.

Li, N., Matsuda, N., Cohen, W. W., & Koedinger, K. R. (2011). A Machine Learning Approach for Automatic Student Model Discovery. In M. Pechenizkiy, T. Calders, C. Conati, S. Ventura, C. Romero  & J. Stamper (Eds.), Proceedings of the International Conference on Educational Data Mining (EDM2011) (pp. 31-40).

Li, N., Matsuda, N., Cohen, W. W., &  Koedinger, K. R. (2010). Towards a computational model of why some students learn faster than others. Proceedings of the AAAI 2010 Fall Symposium on the Cognitive and Metacognitive Educational Systems (pp.40-46). Arlington, VA. 

Li, N., Cohen, W. W., & Koedinger, K. R. (2010). A computational model of accelerated future learning through feature recognition. In ITS’10: Proceedings of 10th International Conference on Intelligent Tutoring Systems, pp. 368–370.

Matsuda, N., Lee, A., Cohen, W. W., & Koedinger, K. R. (2009). A Computational Model of How Learner Errors Arise from Weak Prior Knowledge. In N. Taatgen & H. van Rijn (Eds.), Proceedings of the Annual Conference of the Cognitive Science Society (pp. 1288-1293). Austin, TX: Cognitive Science Society.

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 B. P. Woolf, E. Aimeur, R. Nkambou & S. Lajoie (Eds.), Proceedings of the International Conference on Intelligent Tutoring Systems (pp. 111-121). Heidelberg, Berlin: Springer.

Matsuda, N., Cohen, W. W., Sewall, J., Lacerda, G., & Koedinger, K. R. (2007). Predicting students performance with SimStudent that learns cognitive skills from observation. In R. Luckin, K. R. Koedinger & J. Greer (Eds.), Proceedings of the international conference on Artificial Intelligence in Education (pp. 467-476). Amsterdam, Netherlands: IOS Press.

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 C. Conati, K. McCoy & G. Paliouras (Eds.), Proceedings of the international conference on User Modeling (LNAI 4511) (pp. 107-116). Berlin, Heidelberg: Springer.

Other papers

MacLellan, C. J., Matsuda, N., & Koedinger, K. R. (2013). Toward a reflective SimStudent: Using experience to avoid generalization errors. In G. McCalla & J. Champaign (Eds.), Proceedings of the AIED Workshop on Simulated Learners (pp. 51-60)

Intelligent Authoring

Journal Papers:

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. 

Book Chapters:

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).

Peer Reviewed Conference Papers:

Matsuda, N., Van Velsen, M., Barbalios, N., Lin, L., Vasa, H., Hosseini, R., . . . Bier, N. (2016). Cognitive Tutors Produce Adaptive Online Course: Inaugural Field Trial. In A. Micarelli, J. Stamper & K. Panourgia (Eds.), Proceedings of the International Conference on Intelligent Tutoring Systems (pp. 327-333). Switzerland: Springer.

MacLellan, C., Koedinger, R. K., & Matsuda, N. (2014). Authoring Tutors with SimStudent: An Evaluation of Efficiency and Model Quality. In S. Trausen-Matu & K. Boyer (Eds.), Proceedings of the International Conference on Intelligent Tutoring Systems (pp. 551-560). Switzerland: Springer. [17.5% acceptance rate out of 177 submissions]

Other papers

MacLellan, C.J., Wiese, E. S., Matsuda, N., Koedinger, K. R. (2014) SimStudent: Authoring Expert Models by Tutoring. Second Annual GIFT Users Symposium. June 12-13, 2014. 

Matsuda, N., William W. Cohen, Jonathan Sewall, and Kenneth R. Koedinger (2006). Applying Machine Learning to Cognitive Modeling for Cognitive Tutors, Technical report CMU-ML-06-105, School of Computer Science, Carnegie Mellon University.

Matsuda, N., William W. Cohen, Jonathan Sewall, and Kenneth R. Koedinger (2006). What characterizes a better demonstration for cognitive modeling by demonstration? Technical report CMU-ML-06-106, School of Computer Science, Carnegie Mellon University.

Matsuda, N., Cohen, W. W., & Koedinger, K. R. (2005). Building Cognitive Tutors with Programming by Demonstration. In S. Kramer & B. Pfahringer (Eds.), Technical report: TUM-I0510 (Proceedings of the International Conference on Inductive Logic Programming) (pp. 41-46): Institut fur Informatik, Technische Universitat Munchen.

Matsuda, N., Cohen, W. W., & Koedinger, K. R. (2005). Applying Programming by Demonstration in an Intelligent Authoring Tool for Cognitive Tutors. In AAAI Workshop on Human Comprehensible Machine Learning (Technical Report WS-05-04) (pp. 1-8). Menlo Park, CA: AAAI association. 

Previous SimStudent Related Studies

Jarvis, M. P., Nuzzo-Jones, G., & Heffernan, N. T. (2004). Applying Machine Learning Techniques to Rule Generation in Intelligent Tutoring Systems. In J. C. Lester (Ed.), Proceedings of the International Conference on Intelligent Tutoring Systems (pp 541-553). Heidelberg, Berlin: Springer.

Koedinger, K. R., Aleven, V., Heffernan, N., McLaren, B., & Hockenberry, M. (2004). Opening the Door to Non-Programmers: Authoring Intelligent Tutor Behavior by Demonstration. In J. C. Lester, R. M. Vicari & F. Paraguaçu (Eds.), Proceedings of the Seventh International Conference on Intelligent Tutoring Systems.

Koedinger, K. R., Aleven, V., & Heffernan, N. (2003). Toward a Rapid Development Environment for Cognitive Tutors. In U. Hoppe, F. Verdejo & J. Kay (Eds.), Proceedings of the International Conference on Artificial Intelligence in Education (pp. 455-457). Amsterdam: IOS Press