Matsuda, N., Lv, D., & Zheng, G. (2023). Teaching How to Teach Promotes Learning by Teaching. International Journal of Artificial Intelligence in Education, 33, 720-751. doi: 10.1007/s40593-022-00306-1. [Impact factor: 4.9]

Matsuda, N. (2023). 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. [Impact factor: 4.9]

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. doi: 10.5281/zenodo.7086211.

Zhang, Z., Duan, X., & Matsuda, N. (2021). Building Place-based Research in a Study Abroad Program: Interdisciplinary Pedagogical Approaches to Learning about Cultural Sites. Perspectives on Undergraduate Research and Mentoring, 9(1), 1-16.

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, 1-18. [Impact factor: 1.794]

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. (2018). The State-of-the-Art Pedagogical Agent Technology in the Field of Learning Science. Journal of Japan Society for Information and Systems in Education, 35(1), 13-20.

Namatame, M., & Matsuda, N. (2016). Development of a Peer Review System for Art Education and its Evaluation. Transactions of Japan Society of Kansei Engineering. doi: 10.5057/jjske.TJSKE-D-15-00091

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

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.

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

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 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.

Toyose, K., Nishino, K., Asaba, N., Matsuda, N. (2012). The Waka-Kansei data base for learning Kansei-expression for Japanese Waka: Initial classroom use with a paper prototype. Japan Journal of Educatinal Technology, 36(2), 125-134.

Matsuda, N., & VanLehn, K. (2004). GRAMY: A geometry theorem prover capable of construction. Journal of Automated Reasoning, 32(1), 3-33.

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. 17-30): Springer. [0.15 acceptance rate out of 334 submissions]

Zhang, Z., Dong, Z., Shi, Y., Price, T., Matsuda, N., & Xu, D. (2024). Students' Perceptions and Preferences of Generative Artificial Intelligence Feedback for Programming. In M. Neumann & S. Rosenthal (Eds.), Proceedings on the 14th Symposium on Educational Advances in Artificial Intelligence (pp.1-9). Vancouver: AAAI.

Shimmei, M., & Matsuda, N. (2023). 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 (pp. 3-15): Springer. [0.21 acceptance rate out of 251 submissions]

Shahriar, T., & Matsuda, N. (2023). What and how you 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]

Shimmei, M., & Matsuda, N. (2023). 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 Conference on Educational Data Mining (pp. 286-295): Educational Data Mining Society. Recipient of the Honorable Mention Award

Shimmei, M., & Matsuda, N. (2022). Finding Key Concepts to Automatically Generate Pedagogically Valuable Questions for Learning Objectives. Paper presented at the Annual Meeting of the American Educational Research Association (pp. 1-7). Nominee for the Best Paper award and for the Best Student Paper award

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).

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]

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): EDM.

Shimmei, M., & Noboru, M. (2019). Evidence-Based Recommendation for Content Improvement UsingReinforcementLearning. 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. Recepient of the Doctoral Consortium Scholarship

Inventado, P. S., Inventado, S. G. F., Matsuda, N., Li, Y., Scupelli, P., Ostrow, K., . . . McGuire, P. (2019). Using Design Patterns for Math Preservice Teacher Education. In T. Isaku (Ed.), Proceedings of the 23rd European Conference on Pattern Languages of Programs (pp. 1-8). Irsee, Germany: ACM.

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). [0.25 acceptance rate out of 186 submissions].

Park, S., & Matsuda, N. (2018). A Scalable Method for Rapid and Efficient Skill Discovery for Next-Generation Adaptive Online Courseware.  Paper presented at the Annual Meeting of the American Educational Research Association(pp. 1-12). New York, NY.

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 Proceedings of the International Conference on Artificial Intelligence in Education (pp. 598-601).

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. In W. e. a. Chen (Ed.), Proceedings of the International Conference on Computers in Education (pp. 1-10). [0.23 acceptance rate out of 213 submissions].

Matsuda, N., Barbalios, N., Zhao, J., Ramamurthy, A., Stylianides, G., & Koedinge, 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. [0.15 acceptance rate out of 134 submissions, double blinded review].

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. [0.27 acceptance rate out of 119 submissions].

Matsuda, N., Chandrasekaran, S., & Stamper, J. (2016). How quickly can wheel spinning be detected? In T. Barnes, M. Chi & M. Feng (Eds.), Proceedings of the International Conference on Educational Data Mining (pp. 607-608).

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). [0.29 acceptance rate out of 170 submissions]

Matsuda, N., Furukawa, T., Bier, N., & Faloutsos, C. (2015). Machine beats experts: Automatic discovery of skill models for data-driven online course refinement. In J. G. Boticario, O. C. Santos, C. Romero, M. Pechenizkiy, A. Merceron, P. Mitros, J. M. Luna, C. Michaescu, P. Moreno, A. Hershkovitz, S. Ventura & M. C. Desmarais (Eds.), Proceedings of the International Conference on Educational Data Mining (pp. 101-108). Madrid, Spain. [0.35 acceptance rate out of 121 submission]

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., 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, M. Crosby & K. Panourgia (Eds.), Proceedings of the International Conference on Intelligent Tutoring Systems (pp. 104-113). Switzerland: Springer. [0.18 acceptance rate out of 177 submissions]

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. [0.18 acceptance rate out of 177 submissions]

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)

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)

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. [acceptance rate: 0.38 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. [acceptance rate: 0.16 out of 177 submissions] Niminee 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.

Ogan, A., Finkelstein, S., Mayfield, E., D'Adamo, C., N. Matsuda, & Cassell, J. (2012). "Oh, dear Stacy!" Social interaction, elaboration, and learning with teachable agents. Proceedings of CHI2012 (pp. 39-48). [acceptance rate: 0.23 out of 1577 submissions]

Matsuda, N., Keiser, V., Raizada, R., Yarzebinski, E., Watson, S., Stylianides, G. J., 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. [acceptance rate: 0.13 out of 56 submissions] Nominee for the best paper award

Namatame, M., & Matsuda, N. (2012). An Application of Peer Review for Art Education Proceedings of the International Conference on Wireless, Mobile& Ubiquitous Technologies in Education (WMUTE2012).

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 out of 153]

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).

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. [acceptance rate: 0.38]

Matsuda, N., Cohen, W. W., Koedinger, K. R., Stylianides, G., Keiser, V., & Raizada, R. (2010). Turning 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.

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).

Li, N., Matsuda, N., Cohen, W., & Koedinger, K. (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. Arlington, VA.

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. [acceptance rate: 0.32]

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. [acceptance rate: 0.33]

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. [acceptance rate: 0.30]

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. [acceptance rate: 0.20]

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.

Matsuda, N., & VanLehn, K. (2005). Advanced Geometry Tutor: An intelligent tutor that teaches proof-writing with construction. In C.-K. Looi, G. McCalla, B. Bredeweg & J. Breuker (Eds.), Proceedings of The 12th International Conference on Artificial Intelligence in Education (pp. 443-450). Amsterdam: IOS Press. [acceptance rate: 0.31]

Noboru Matsuda and Kurt VanLehn (2003). Modeling Hinting Strategies for Geometry Theorem Proving. In P. Brusilovsky, A. Corbett & F. de Rosis (Eds.), Proceedings of the 9th International Conference on User Modeling (pp.373-377), Berlin, Heidelberg: Springer.

Noboru Matsuda, Kurt VanLehn (2000). A Reification of a Strategy for Geometry Theorem Proving. In G. Gauthier, C. Frasson & K. VanLehn (Eds.), Proceedings of the International Conference on Intelligent Tutoring Systems (Lecture Notes in Computer Science, No.1839, p.660), Berlin, Heidelberg: Springer.

Noboru Matsuda, Kurt VanLehn, Decision Theoretic Instructional Planner for Intelligent Tutoring Systems. In B. du Boulay (Ed.), Workshop Proceedings on Modeling Human Teaching Tactics and Strategies (ITS2000, pp.72-83).

Noboru Matsuda. (2004). The Impact of Different Proof Strategies On Learning Geometry Theorem Proving. Unpublished Ph.D dissertation, University of Pittsburgh, Pittsburgh, PA.

Matsuda, N., Shimmei, M., Chaudhuri, P., Makam, D., Shrivastava, R., Wood, J., & Taneja, P. (2023). 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 (pp.93-108). 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

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

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

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

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).

Matsuda, N. (2006). How to get a Ph.D in America. In Akira Arimoto and Ikuo Kitagaki (Eds.) University Authority, (pp.132-137). Tokyo: Minervashobo Publishers Inc.

Noboru Matsuda (2005). Instructional strategies. In Hozumi Tanaka (Ed.) Encyclopedia of Artificial Intelligence. Tokyo: Japan Society of Artificial Intelligence.

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

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

Nan Li, William W. Cohen, Kenneth R. Koedinger, and Noboru Matsuda. An Intelligent System with Integrated Representation Learning and Skill Learning.  US Patent 20,160,026,932

Noboru Matsuda, 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.

Noboru Matsuda, 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.