@article{shahriar_matsuda_2024, title={"I Am Confused! How to Differentiate Between.?" Adaptive Follow-Up Questions Facilitate Tutor Learning with Effective Time-On-Task}, volume={14830}, ISBN={["978-3-031-64298-2"]}, ISSN={["1611-3349"]}, DOI={10.1007/978-3-031-64299-9_2}, journal={ARTIFICIAL INTELLIGENCE IN EDUCATION, PT II, AIED 2024}, author={Shahriar, Tasmia and Matsuda, Noboru}, year={2024}, pages={17–30} }
 @inbook{shimmei_bier_matsuda_2023, title={Machine-Generated Questions Attract Instructors When Acquainted with Learning Objectives}, url={https://doi.org/10.1007/978-3-031-36272-9_1}, DOI={10.1007/978-3-031-36272-9_1}, abstractNote={Answering questions is an essential learning activity on online courseware. It has been shown that merely answering questions facilitates learning. However, generating pedagogically effective questions is challenging. Although there have been studies on automated question generation, the primary research concern thus far is about if and how those question generation techniques can generate answerable questions and their anticipated effectiveness. We propose Quadl, a pragmatic method for generating questions that are aligned with specific learning objectives. We applied Quadl to an existing online course and conducted an evaluation study with in-service instructors. The results showed that questions generated by Quadl were evaluated as on-par with human-generated questions in terms of their relevance to the learning objectives. The instructors also expressed that they would be equally likely to adapt Quadl-generated questions to their course as they would human-generated questions. The results further showed that Quadl-generated questions were better than those generated by a state-of-the-art question generation model that generates questions without taking learning objectives into account.}, author={Shimmei, Machi and Bier, Norman and Matsuda, Noboru}, year={2023} }
 @inbook{shahriar_matsuda_2023, title={What and How You Explain Matters: Inquisitive Teachable Agent Scaffolds Knowledge-Building for Tutor Learning}, url={https://doi.org/10.1007/978-3-031-36272-9_11}, DOI={10.1007/978-3-031-36272-9_11}, abstractNote={Students learn by teaching a teachable agent, a phenomenon called tutor learning. Literature suggests that tutor learning happens when students (who tutor the teachable agent) actively reflect on their knowledge when responding to the teachable agent's inquiries (aka knowledge-building). However, most students often lean towards delivering what they already know instead of reflecting on their knowledge (aka knowledge-telling). The knowledge-telling behavior weakens the effect of tutor learning. We hypothesize that the teachable agent can help students commit to knowledge-building by being inquisitive and asking follow-up inquiries when students engage in knowledge-telling. Despite the known benefits of knowledge-building, no prior work has operationalized the identification of knowledge-building and knowledge-telling features from students' responses to teachable agent's inquiries and governed them toward knowledge-building. We propose a Constructive Tutee Inquiry that aims to provide follow-up inquiries to guide students toward knowledge-building when they commit to a knowledge-telling response. Results from an evaluation study show that students who were treated by Constructive Tutee Inquiry not only outperformed those who were not treated but also learned to engage in knowledge-building without the aid of follow-up inquiries over time.}, author={Shahriar, Tasmia and Matsuda, Noboru}, year={2023} }
 @book{rodrigo_matsuda_cristea_dimitrova_2022, place={Cham, Switzerland}, series={Lecture Notes in Computer Science}, title={Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners’ and Doctoral Consortium}, ISBN={9783031116469 9783031116476}, ISSN={0302-9743 1611-3349}, url={http://dx.doi.org/10.1007/978-3-031-11647-6}, DOI={10.1007/978-3-031-11647-6}, abstractNote={The conference proceedings AIED 2022 focuses on topics such as intelligent systems and the cognitive sciences for the improvement and much more.}, publisher={Springer International Publishing}, year={2022}, collection={Lecture Notes in Computer Science} }
 @article{matsuda_lv_zheng_2022, title={Teaching How to Teach Promotes Learning by Teaching}, volume={8}, ISSN={["1560-4306"]}, url={https://doi.org/10.1007/s40593-022-00306-1}, DOI={10.1007/s40593-022-00306-1}, journal={INTERNATIONAL JOURNAL OF ARTIFICIAL INTELLIGENCE IN EDUCATION}, author={Matsuda, Noboru and Lv, Dan and Zheng, Guoguo}, year={2022}, month={Aug} }
 @article{shahriar_matsuda_2021, title={"Can You Clarify What You Said?": Studying the Impact of Tutee Agents' Follow-Up Questions on Tutors' Learning}, volume={12748}, ISBN={["978-3-030-78291-7"]}, ISSN={["1611-3349"]}, url={https://doi.org/10.1007/978-3-030-78292-4_32}, DOI={10.1007/978-3-030-78292-4_32}, abstractNote={Students learn by teaching others as tutors. Advancement in the theory of learning by teaching has given rise to many pedagogical agents. In this paper, we exploit a known cognitive theory that states if a tutee asks deep questions in a peer tutoring environment, a tutor benefits from it. Little is known about a computational model of such deep questions. This paper aims to formalize the deep tutee questions and proposes a generalized model of inquiry-based dialogue, called the constructive tutee inquiry, to ask follow-up questions to have tutors reflect their current knowledge (aka knowledge-building activity). We conducted a Wizard of Oz study to evaluate the proposed constructive tutee inquiry. The results showed that the constructive tutee inquiry was particularly effective for the low prior knowledge students to learn conceptual knowledge.}, journal={ARTIFICIAL INTELLIGENCE IN EDUCATION (AIED 2021), PT I}, author={Shahriar, Tasmia and Matsuda, Noboru}, year={2021}, pages={395–407} }
 @article{shimmei_matsuda_2021, title={Learning Association Between Learning Objectives and Key Concepts to Generate Pedagogically Valuable Questions}, volume={12749}, ISBN={["978-3-030-78269-6"]}, ISSN={["1611-3349"]}, url={https://doi.org/10.1007/978-3-030-78270-2_57}, DOI={10.1007/978-3-030-78270-2_57}, abstractNote={It has been shown that answering questions contributes to students learning effectively. However, generating questions is an expensive task and requires a lot of effort. Although there has been research reported on the automation of question generation in the literature of Natural Language Processing, these technologies do not necessarily generate questions that are useful for educational purposes. To fill this gap, we propose QUADL, a method for generating questions that are aligned with a given learning objective. The learning objective reflects the skill or concept that students need to learn. The QUADL method first identifies a key concept, if any, in a given sentence that has a strong connection with the given learning objective. It then converts the given sentence into a question for which the predicted key concept becomes the answer. The results from the survey using Amazon Mechanical Turk suggest that the QUADL method can be a step towards generating questions that effectively contribute to students’ learning.}, journal={ARTIFICIAL INTELLIGENCE IN EDUCATION (AIED 2021), PT II}, publisher={Springer International Publishing}, author={Shimmei, Machi and Matsuda, Noboru}, year={2021}, pages={320–324} }
 @article{matsuda_2021, title={Teachable Agent as an Interactive Tool for Cognitive Task Analysis: A Case Study for Authoring an Expert Model}, volume={32}, ISSN={["1560-4306"]}, url={https://doi.org/10.1007/s40593-021-00265-z}, DOI={10.1007/s40593-021-00265-z}, number={1}, journal={INTERNATIONAL JOURNAL OF ARTIFICIAL INTELLIGENCE IN EDUCATION}, publisher={Springer Science and Business Media LLC}, author={Matsuda, Noboru}, year={2021}, month={Jul} }
 @article{zimmer_mctigue_matsuda_2021, title={Development and validation of the teachers' digital learning identity survey}, volume={105}, ISSN={["1873-538X"]}, DOI={10.1016/j.ijer.2020.101717}, abstractNote={Research emphasizes teachers' attitudes and methods towards classroom digital literacy (DL) integration with minimal studies accenting teachers' attitudes towards personal DL use. Specifically, recognizing how teachers use DL to learn (i.e., their digital learning identity (DLI) — the identity developed from perceived DL competence). The Digital Learning Identity Survey (DLIS) was created to assist teachers in self-identification and recognition of their learning identity related to DL. This study investigates the reliability and validity of the DLIS with pre-service teachers using exploratory and confirmatory factor analyses. A correlation analysis was conducted to determine if survey items correlated logically and aligned with existing theory, including elements needed for digital identity development – DL, self-regulated learning, and motivation. Results found aspects of the DLIS validly measure DLI.}, journal={INTERNATIONAL JOURNAL OF EDUCATIONAL RESEARCH}, author={Zimmer, Wendi K. and McTigue, Erin M. and Matsuda, Noboru}, year={2021} }
 @article{matsuda_weng_wall_2020, title={The Effect of Metacognitive Scaffolding for Learning by Teaching a Teachable Agent}, volume={30}, ISSN={1560-4292 1560-4306}, url={http://dx.doi.org/10.1007/s40593-019-00190-2}, DOI={10.1007/s40593-019-00190-2}, number={1}, journal={International Journal of Artificial Intelligence in Education}, publisher={Springer Science and Business Media LLC}, author={Matsuda, Noboru and Weng, Wenting and Wall, Natalie}, year={2020}, month={Jan}, pages={1–37} }
 @inbook{shen_shimmei_chi_matsuda_2019, place={Orlando, FL}, title={Applications of Reinforcement Learning to Self-Improving Educational Systems}, volume={7: Self-Improving Systems}, booktitle={Design Recommendations for Intelligent Tutoring Systems}, publisher={US Army Research Lab}, author={Shen, S. and Shimmei, M. and Chi, M. and Matsuda, N.}, editor={Sinatra, A.M. and Graesser, A.C. and Hu, X. and Brawner, K. and Rus, V.Editors}, year={2019}, pages={77–96} }
 @article{shimmei_matsuda_2019, title={Evidence-Based Recommendation for Content Improvement Using Reinforcement Learning}, volume={11626}, ISBN={["978-3-030-23206-1"]}, ISSN={["1611-3349"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85068349976&partnerID=MN8TOARS}, DOI={10.1007/978-3-030-23207-8_68}, abstractNote={One of the most challenging issues for online-courseware engineering is to maintain the quality of instructional elements. However, it is hard to know how each instructional element on the courseware contributes to students’ learning. To address this challenge, an evidence-based learning-engineering method for validating the quality of instructional elements on online courseware is proposed. Students’ learning trajectories on particular online courseware and their final learning outcomes are consolidated into a state transition graph. The value iteration technique is applied to compute the worst actions taken (a converse policy) to yield the least successful learning. We hypothesize that the converse policy reflects the quality of instructional elements. As a proof of concept, this paper describes an evaluation study where we simulated online learning data on three hypothetical pieces of online courseware. The result showed that our method can detect more than a half of the ineffective instructional elements on three types of courseware containing various ratios of ineffective instructional elements.}, journal={ARTIFICIAL INTELLIGENCE IN EDUCATION, AIED 2019, PT II}, author={Shimmei, Machi and Matsuda, Noboru}, year={2019}, pages={369–373} }
 @inproceedings{matsuda_shimmei_2019, title={PASTEL: Evidence-based learning engineering method to create intelligent online textbook at scale}, volume={2384}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85067813785&partnerID=MN8TOARS}, booktitle={CEUR Workshop Proceedings}, author={Matsuda, N. and Shimmei, M.}, year={2019}, pages={70–80} }
 @book{matsuda_sekar_wall_2018, title={Metacognitive scaffolding amplifies the effect of learning by teaching a teachable agent}, volume={10947 LNAI}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85049377501&partnerID=MN8TOARS}, DOI={10.1007/978-3-319-93843-1_23}, abstractNote={Learning by teaching has been compared with learning by being tutored, aka cognitive tutoring, to learn algebra linear equations for 7th to 8th grade algebra. Two randomized-controlled trials with 46 and 141 6th through 8th grade students were conducted in 3 public schools in two different years. Students in the learning by teaching (LBT) condition used an online learning environment (APLUS), where they interactively taught a teachable agent (SimStudent) how to solve equations with a goal to have the teachable agent pass the quiz. Students in the learning by being tutored condition used a version of cognitive tutor that uses the same user interface as APLUS, but no teachable agent. Instead, a teacher agent tutored students how to solve equations. The goal for students in this condition was to pass the quiz by themselves. Students selected and entered problems to be tutored by themselves. This condition is hence called Goal-Oriented Practice (GOP). For both conditions, students received metacognitive scaffolding on how to teach the teachable agent (LBT) and how to regulate their learning (GOP). The results from the classroom studies show that (1) students in both conditions learned equally well, measured as pre- and post-test scores, (2) prior competency does not influence the effect of LBT nor GOP (i.e., no aptitude-treatment interaction observed), and (3) GOP students primarily focused on submitting the quiz rather than practicing on problems. These results suggest that with the metacognitive scaffolding, learning by teaching is equally effective as cognitive tutoring regardless of the prior competency.}, journal={Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}, author={Matsuda, N. and Sekar, V.P.C. and Wall, N.}, year={2018}, pages={311–323} }
 @article{matsuda_2018, title={The State-of-the-Art Pedagogical Agent Technology in the Field of Learning Science}, volume={35}, url={https://doi.org/10.14926/jsise.35.13}, number={1}, journal={Transactions of Japanese Society for Information and Systems in Education}, author={Matsuda, N.}, year={2018}, pages={13–20} }
 @inproceedings{inventado_li_heffernan_inventado_scupelli_tu_matsuda_ostrow_mason_logue_et al._2018, title={Using design patterns for math preservice teacher education}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85061277780&partnerID=MN8TOARS}, DOI={10.1145/3282308.3282340}, abstractNote={Experienced teachers effectively address student misconceptions and learning difficulties by employing high-quality pedagogical strategies that they have developed through experience. It is difficult to discover effective strategies because it takes a lot of trial and error before a teacher can conclude that a particular approach improves student learning. Researchers have used educational design patterns to encapsulate high-quality strategies that facilitate the transfer of pedagogical knowledge. In this work, we investigated the feasibility of utilizing three educational design patterns as a teaching tool for math preservice teacher education: Feedback Sandwich, Pitfall Diagnosis and Prevention, and Worked Examples. Specifically, we used design patterns to investigate how preservice teachers provided feedback to students who gave common wrong answers to a given math problem and compared their feedback before and after they were introduced to educational design patterns. Results indicated that design patterns helped preservice teachers consider other aspects of feedback such as students' self-regard, common misconceptions, and prior knowledge into their feedback for common wrong answers on math problems. The limited set of three design patterns introduced in the study focused on feedback presentation. Results also indicated preservice teachers may benefit from design patterns that address other aspects of feedback such as content granularity, feedback length, and presentation medium. Implications of this study, including the potential of using educational design patterns to improve preservice teacher education, are discussed.}, booktitle={ACM International Conference Proceeding Series}, author={Inventado, P.S. and Li, Y. and Heffernan, N. and Inventado, S.G.F. and Scupelli, P. and Tu, S. and Matsuda, N. and Ostrow, K. and Mason, C. and Logue, M. and et al.}, year={2018} }
 @inbook{matsuda_2017, place={Tokyo, Japan}, title={Instructional Strategy}, booktitle={Encyclopedia of Artificial Intelligence}, publisher={Japan Society of Artificial Intelligence}, author={Matsuda, N.}, editor={Matsubara, HitoshiEditor}, year={2017}, pages={1157–1159} }
 @inbook{matsuda_2017, place={Tokyo, Japan}, title={Intelligent Pedagogical Agents}, booktitle={Encyclopedia of Artificial Intelligence}, publisher={Japan Society of Artificial Intelligence}, author={Matsuda, N.}, editor={Matsubara, HitoshiEditor}, year={2017}, pages={1152–1153} }
 @inproceedings{dumdumaya_banawan_rodrigo_ogan_yarzebinski_matsuda_2017, title={Investigating the effects of cognitive and metacognitive scaffolding on learners using a learning by teaching environment}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85051544348&partnerID=MN8TOARS}, booktitle={Proceedings of the 25th International Conference on Computers in Education, ICCE 2017 - Main Conference Proceedings}, author={Dumdumaya, C. and Banawan, M. and Rodrigo, Ma.M. and Ogan, A. and Yarzebinski, E. and Matsuda, N.}, year={2017}, pages={1–10} }
 @inbook{matsuda_2017, place={Tokyo, Japan}, title={Natural language processing in educational systems}, booktitle={Encyclopedia of Artificial Intelligence}, publisher={Society of Artificial Intelligence}, author={Matsuda, N.}, editor={Matsubara, HitoshiEditor}, year={2017}, pages={1101} }
 @book{yarzebinski_dumdumaya_rodrigo_matsuda_ogan_2017, title={Regional cultural differences in how students customize their avatars in technology-enhanced learning}, volume={10331 LNAI}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85022225246&partnerID=MN8TOARS}, DOI={10.1007/978-3-319-61425-0_73}, abstractNote={As AIED systems with agents and avatars are used by students in different world regions, we expect students to prefer ones that look like them according to the Similarity Attraction Hypothesis. We investigate this effect via a system with a customizable avatar deployed in 2 US regions and 2 Philippines regions. We find that US students do customize as expected, while students in the Philippines tend to select names and hairstyles from outside their culture. These results show the need for more nuanced system design to tailor options for regional-level preferences.}, journal={Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}, author={Yarzebinski, E. and Dumdumaya, C. and Rodrigo, M.M.T. and Matsuda, N. and Ogan, A.}, year={2017}, pages={598–601} }
 @book{matsuda_velsen_barbalios_lin_vasa_hosseini_sutner_bier_2016, title={Cognitive tutors produce adaptive online course: Inaugural field trial}, volume={9684}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84976646519&partnerID=MN8TOARS}, DOI={10.1007/978-3-319-39583-8_37}, abstractNote={We hypothesize that when cognitive tutors are integrated into online courseware, the online courseware can provide a new type of adaptive instructions, such as impasse-driven adaptive remediation and need-based assessments. As a proof of concept, we have developed an adaptive online course on the Open Learning Initiative (OLI) platform by integrating four new instances of cognitive tutors into an existing OLI course. Cognitive tutors were created with an innovative cognitive tutor authoring system called Watson. To evaluate the effectiveness of the adaptive online course, a quasi-experiment was conducted in a gateway course at Carnegie Mellon University. The results show that the proposed adaptive online course technology is robust enough to be used in actual classroom with mixed effect for learning.}, journal={Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}, author={Matsuda, N. and Velsen, M. and Barbalios, N. and Lin, S. and Vasa, H. and Hosseini, R. and Sutner, K. and Bier, N.}, year={2016}, pages={327–333} }
 @article{namatame_matsuda_2016, title={Development of a Peer Review System for Art Education and its Evaluation}, volume={15}, ISSN={1884-0833 1884-5258}, url={http://dx.doi.org/10.5057/jjske.tjske-d-15-00091}, DOI={10.5057/jjske.tjske-d-15-00091}, abstractNote={: We have developed a tablet application to support peer review learning for visual art education. The tablet-based front-end allows students to review others’ work and provide comments in the form of written text and also comments drawn directly on the artwork (i.e., the direct comment). We hypothesize that (1) peer review would facilitate learning various techniques for visual art, and (2) students would find it useful to directly provide comment on other’s artwork. To test these hypotheses, we conducted three classroom (in-vivo) studies over three years. The goal of this paper is to introduce the tablet-based peer review system and report results from the three in-vivo studies. The results show that (a) the table-based peer review system is as effective as the traditional classroom instruction, and (b) students tend to have a high expectation for the direct comment.}, number={4}, journal={Transactions of Japan Society of Kansei Engineering}, publisher={Japan Society of Kansei Engineering}, author={Namatame, Miki and Matsuda, Noboru}, year={2016}, pages={425–430} }
 @inproceedings{matsuda_chandrasekaran_stamper_2016, title={How quickly can wheel spinning be detected?}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85067333848&partnerID=MN8TOARS}, booktitle={Proceedings of the 9th International Conference on Educational Data Mining, EDM 2016}, author={Matsuda, N. and Chandrasekaran, S. and Stamper, J.}, year={2016}, pages={607–608} }
 @book{matsuda_barbalios_zhao_ramamurthy_stylianides_koedinger_2016, title={Tell me how to teach, I’ll learn how to solve problems}, volume={9684}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84976643436&partnerID=MN8TOARS}, DOI={10.1007/978-3-319-39583-8_11}, abstractNote={In this paper we study the effect of adaptive scaffolding to learning by teaching. We hypothesize that learning by teaching is facilitated if (1) students receive adaptive scaffolding on how to teach and how to prepare for teaching (the metacognitive hypothesis), (2) students receive adaptive scaffolding on how to solve problems (the cognitive hypothesis), or (3) both (the hybrid hypothesis). We conducted a classroom study to test these hypotheses in the context of learning to solve equations by teaching a synthetic peer, SimStudent. The results show that the metacognitive scaffolding facilitated tutor learning (regardless of the presence of the cognitive scaffolding), whereas cognitive scaffolding had virtually no effect. The same pattern was confirmed by two additional datasets collected from two previous school studies we conducted.}, journal={Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}, author={Matsuda, N. and Barbalios, N. and Zhao, Z. and Ramamurthy, A. and Stylianides, G.J. and Koedinger, K.R.}, year={2016}, pages={111–121} }
 @article{toyose_asaba_yamaguchi_nishino_matsuda_2015, title={Application of Waka-Kansei Database for Learning Japanese Waka in Middle School}, volume={38}, number={4}, journal={Japan Journal of Educational Technology}, author={Toyose, K. and Asaba, N. and Yamaguchi, H. and Nishino, K. and Matsuda, N.}, year={2015}, pages={329–340} }
 @inbook{blessing_aleven_gilbert_heffernan_matsuda_mitrovic_2015, place={Orlando, FL}, title={Authoring Example-based Tutors for Procedural Tasks}, volume={3}, ISBN={9780989392372}, booktitle={Design Recommendations for Intelligent Tutoring Systems: Authoring Tools & Expert Modeling Techniques}, publisher={U.S. Army Research Laboratory}, author={Blessing, S.B. and Aleven, V. and Gilbert, S.B. and Heffernan, N.T. and Matsuda, N. and Mitrovic, A.}, editor={Sottilare, R. and Graesser, A. and Hu, X. and Brawner, K.Editors}, year={2015}, pages={71–94} }
 @inproceedings{maclellan_harpstead_wiese_zou_matsuda_aleven_koedinger_2015, title={Authoring tutors with complex solutions: A comparative analysis of Example Tracing and SimStudent}, volume={1432}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84944328396&partnerID=MN8TOARS}, booktitle={CEUR Workshop Proceedings}, author={Maclellan, C.J. and Harpstead, E. and Wiese, E.S. and Zou, M. and Matsuda, N. and Aleven, V. and Koedinger, K.R.}, year={2015}, pages={35–44} }
 @inproceedings{koedinger_matsuda_maclellan_mclaughlin_2015, title={Methods for evaluating simulated learners: Examples from SimStudent}, volume={1432}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84944315929&partnerID=MN8TOARS}, booktitle={CEUR Workshop Proceedings}, author={Koedinger, K.R. and Matsuda, N. and Maclellan, C.J. and McLaughlin, E.A.}, year={2015}, pages={45–54} }
 @book{yarzebinski_ogan_rodrigo_matsuda_2015, title={Understanding students’ use of code-switching in a learning by teaching technology}, volume={9112}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84948967288&partnerID=MN8TOARS}, DOI={10.1007/978-3-319-19773-9_50}, abstractNote={Personalized learning systems have shown significant learning gains when used in formal classroom teaching. Systems that use pedagogical agents for teaching have become popular, but typically their design does not account for multilingual classrooms. We investigated one such system in classrooms in the Philippines to see if and how students used code-switching when providing explanations of algebra problem solving. We found significant amounts of code-switching and explored cognitive and social factors such as explanation quality and affective valence that serve as evidence for code-switching motivations and effects. These results uncover complex social and cognitive interactions that occur during learning interactions with a virtual peer, and call for more affordances to support multilingual students.}, journal={Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}, author={Yarzebinski, E. and Ogan, A. and Rodrigo, M.M.T. and Matsuda, N.}, year={2015}, pages={504–513} }
 @book{maclellan_koedinger_matsuda_2014, title={Authoring tutors with simstudent: An evaluation of efficiency and model quality}, volume={8474 LNCS}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84958536161&partnerID=MN8TOARS}, DOI={10.1007/978-3-319-07221-0_70}, abstractNote={Authoring Intelligent Tutoring Systems is expensive and time consuming. To reduce costs, the Cognitive Tutor Authoring Tools and the Example-Tracing Tutor paradigm were developed to make the tutor authoring process more efficient. Under this paradigm, tutors are constructed by demonstrating behavior directly in a tutor interface, reducing the need for programming expertise. This paper evaluates the efficiency of authoring a tutor with SimStudent, an extension to the Example-Tracing paradigm that is designed to produce greater generality in less time by induction from past demonstrations and feedback. We found that authoring an algebra tutor in SimStudent is faster than Example-Tracing while maintaining equivalent final model quality. Furthermore, we found that the SimStudent model generalizes beyond the problems that were used to author it.}, journal={Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}, author={MacLellan, C.J. and Koedinger, K.R. and Matsuda, N.}, year={2014}, pages={551–560} }
 @article{li_matsuda_cohen_koedinger_2015, title={Integrating representation learning and skill learning in a human-like intelligent agent}, volume={219}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84919717470&partnerID=MN8TOARS}, DOI={10.1016/j.artint.2014.11.002}, abstractNote={Building an intelligent agent that simulates human learning of math and science could potentially benefit both cognitive science, by contributing to the understanding of human learning, and artificial intelligence, by advancing the goal of creating human-level intelligence. However, constructing such a learning agent currently requires manual encoding of prior domain knowledge; in addition to being a poor model of human acquisition of prior knowledge, manual knowledge-encoding is both time-consuming and error-prone. Previous research has shown that one of the key factors that differentiates experts and novices is their different representations of knowledge. Experts view the world in terms of deep functional features, while novices view it in terms of shallow perceptual features. Moreover, since the performance of learning algorithms is sensitive to representation, the deep features are also important in achieving effective machine learning. In this paper, we present an efficient algorithm that acquires representation knowledge in the form of "deep features", and demonstrate its effectiveness in the domain of algebra as well as synthetic domains. We integrate this algorithm into a machine-learning agent, SimStudent, which learns procedural knowledge by observing a tutor solve sample problems, and by getting feedback while actively solving problems on its own. We show that learning "deep features" reduces the requirements for knowledge engineering. Moreover, we propose an approach that automatically discovers student models using the extended SimStudent. By fitting the discovered model to real student learning curve data, we show that it is a better student model than human-generated models, and demonstrate how the discovered model may be used to improve a tutoring system's instructional strategy.}, journal={Artificial Intelligence}, author={Li, N. and Matsuda, N. and Cohen, W.W. and Koedinger, K.R.}, year={2015}, pages={67–91} }
 @book{matsuda_griger_barbalios_stylianides_cohen_koedinger_2014, title={Investigating the effect of meta-cognitive scaffolding for learning by teaching}, volume={8474 LNCS}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84958535516&partnerID=MN8TOARS}, DOI={10.1007/978-3-319-07221-0_13}, abstractNote={This paper investigates the effect of meta-cognitive help in the context of learning by teaching. Students learned to solve algebraic equations by tutoring a teachable agent, called SimStudent, using an online learning environment, called APLUS. A version of APLUS was developed to provide meta-cognitive help on what problems students should teach, as well as when to quiz SimStudent. A classroom study comparing APLUS with and without the meta-cognitive help was conducted with 173 seventh to ninth grade students. The data showed that students with the meta-cognitive help showed better problem selection and scored higher on the post-test than those who tutored SimStudent without the meta-cognitive help. These results suggest that, when carefully designed, learning by teaching can support students to not only learn cognitive skills but also employ meta-cognitive skills for effective tutoring.}, journal={Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}, author={Matsuda, N. and Griger, C.L. and Barbalios, N. and Stylianides, G.J. and Cohen, W.W. and Koedinger, K.R.}, year={2014}, pages={104–113} }
 @article{matsuda_cohen_koedinger_2015, title={Teaching the teacher: Tutoring simstudent leads to more effective cognitive tutor authoring}, volume={25}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84920862842&partnerID=MN8TOARS}, DOI={10.1007/s40593-014-0020-1}, abstractNote={SimStudent is a machine-learning agent initially developed to help novice authors to create cognitive tutors without heavy programming. Integrated into an existing suite of software tools called Cognitive Tutor Authoring Tools (CTAT), SimStudent helps authors to create an expert model for a cognitive tutor by tutoring SimStudent on how to solve problems. There are two different ways to author an expert model with SimStudent. In the context of Authoring by Tutoring, the author interactively tutors SimStudent by posing problems to SimStudent, providing feedback on the steps performed by SimStudent, and also demonstrating steps as a response to SimStudent's hint requests when SimStudent cannot perform steps correctly. In the context of Authoring by Demonstration, the author demonstrates solution steps, and SimStudent attempts to induce underlying domain principles by generalizing those worked-out examples. We conducted evaluation studies to investigate which authoring strategy better facilitates authoring and found two key results. First, the expert model generated with Authoring by Tutoring is better and has higher accuracy while maintaining the same level of completeness than the one generated with Authoring by Demonstration. The reason for this better accuracy is that the expert model generated by tutoring benefits from negative feedback provided for SimStudent's incorrect production applications. Second, authoring by Tutoring requires less time than Authoring by Demonstration. This enhanced authoring efficiency is partially because (a) when Authoring by Demonstration, the author needs to test the quality of the expert model, whereas the formative assessment of the expert model is done naturally by observing SimStudent's performance when Authoring by Tutoring, and (b) the number of steps that need to be demonstrated during tutoring decreases as learning progresses.}, number={1}, journal={International Journal of Artificial Intelligence in Education}, author={Matsuda, N. and Cohen, W.W. and Koedinger, K.R.}, year={2015}, pages={1–34} }
 @article{matsuda_yarzebinski_keiser_raizada_cohen_stylianides_koedinger_2013, title={Cognitive anatomy of tutor learning: Lessons learned with SimStudent.}, volume={105}, ISSN={1939-2176 0022-0663}, url={http://dx.doi.org/10.1037/a0031955}, DOI={10.1037/a0031955}, abstractNote={This article describes an advanced learning technology used to investigate hypotheses about learning by teaching. The proposed technology is an instance of a teachable agent, called SimStudent, that learns skills (e.g., for solving linear equations) from examples and from feedback on performance. SimStudent has been integrated into an online, gamelike environment in which students act as “tutors” and can interactively teach SimStudent by providing it with examples and feedback. We conducted 3 classroom “in vivo” studies to better understand how and when students learn (or fail to learn) by teaching. One of the strengths of interactive technologies is their ability to collect detailed process data on the nature and timing of student activities. The primary purpose of this article is to provide an in-depth analysis across 3 studies to understand the underlying cognitive and social factors that contribute to tutor learning by making connections between outcome and process data. The results show several key cognitive and social factors that are correlated with tutor learning. The accuracy of students’ responses (i.e., feedback and hints), the quality of students’ explanations during tutoring, and the appropriateness of tutoring strategy (i.e., problem selection) all positively affected SimStudent’s learning, which further positively affected students’ learning. The results suggest that implementing adaptive help for students on how to tutor and solve problems is a crucial component for successful learning by teaching.}, number={4}, journal={Journal of Educational Psychology}, publisher={American Psychological Association (APA)}, author={Matsuda, Noboru and Yarzebinski, Evelyn and Keiser, Victoria and Raizada, Rohan and Cohen, William W. and Stylianides, Gabriel J. and Koedinger, Kenneth R.}, year={2013}, pages={1152–1163} }
 @article{rodrigo_geli_ong_vitug_bringula_basa_dela cruz_matsuda_2013, title={Exploring the Implications of Tutor Negativity Towards a Synthetic Agent in a Learning-by-Teaching Environment}, volume={8}, number={1}, journal={Philippine Computing Journal}, author={Rodrigo, M.M.T. and Geli, R.I.A.M. and Ong, A. and Vitug, G.J.G. and Bringula, R. and Basa, R.S. and Dela Cruz, C and Matsuda, N.}, year={2013}, month={Dec}, pages={15–20} }
 @inproceedings{rodrigo_ong_bringula_basa_dela cruz_matsuda_2013, title={Impact of prior knowledge and teaching strategies on learning by teaching}, volume={1009}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84924982067&partnerID=MN8TOARS}, booktitle={CEUR Workshop Proceedings}, author={Rodrigo, Ma.M.T. and Ong, A. and Bringula, R. and Basa, R.S. and Dela Cruz, C. and Matsuda, N.}, year={2013}, pages={71–80} }
 @article{matsuda_yarzebinski_keiser_raizada_stylianides_koedinger_2013, title={Studying the effect of a competitive game show in a learning by teaching environment}, volume={23}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84902484678&partnerID=MN8TOARS}, DOI={10.1007/s40593-013-0009-1}, abstractNote={In this paper we investigate how competition among tutees in the context of learning by teaching affects tutors' engagement as well as tutor learning. We conducted this investigation by incorporating a competitive Game Show feature into an online learning environment where students learn to solve algebraic equations by teaching a synthetic peer, called SimStudent. In the Game Show, pairs of SimStudents trained by students beforehand competed against each other by solving challenging problems to attain higher ratings. The results of a classroom study with 141 7th through 9th grade students showed the following: (1) Students improved their proficiency to solve equations after teaching SimStudent, but there was no observed improvement in their conceptual understanding. (2) Overall, the competitive Game Show promoted students' extrinsic and intrinsic motivations—when the competitive Game Show was available, students' engagement in tutoring (intrinsic motivation) was increased; students who arguably had a higher desire to win strategically selected opponents with lower proficiency for an easy win (extrinsic motivation). (3) The availability of the competitive Game Show did not affect tutor learning; there was no notable correlation between students' motivation (intrinsic or extrinsic) and tutor learning. Based on these findings, we propose design improvements to increase tutor learning.}, number={1-4}, journal={International Journal of Artificial Intelligence in Education}, author={Matsuda, N. and Yarzebinski, E. and Keiser, V. and Raizada, R. and Stylianides, G.J. and Koedinger, K.R.}, year={2013}, pages={1–21} }
 @inproceedings{maclellan_matsuda_koedinger_2013, title={Toward a reflective SimStudent: Using experience to avoid generalization errors}, volume={1009}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84924980730&partnerID=MN8TOARS}, booktitle={CEUR Workshop Proceedings}, author={MacLellan, C.J. and Matsuda, N. and Koedinger, K.R.}, year={2013}, pages={51–60} }
 @inproceedings{ogan_finkelstein_mayfield_d’adamo_matsuda_cassell_2012, title={"Oh, dear Stacy!" Social interaction, elaboration, and learning with teachable agents}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84862093939&partnerID=MN8TOARS}, DOI={10.1145/2207676.2207684}, abstractNote={Understanding how children perceive and interact with teachable agents (systems where children learn through teaching a synthetic character embedded in an intelligent tutoring system) can provide insight into the effects of so-cial interaction on learning with intelligent tutoring systems. We describe results from a think-aloud study where children were instructed to narrate their experience teaching Stacy, an agent who can learn to solve linear equations with the student's help. We found treating her as a partner, primarily through aligning oneself with Stacy using pronouns like you or we rather than she or it significantly correlates with student learning, as do playful face-threatening comments such as teasing, while elaborate explanations of Stacy's behavior in the third-person and formal tutoring statements reduce learning gains. Additionally, we found that the agent's mistakes were a significant predictor for students shifting away from alignment with the agent.}, booktitle={Conference on Human Factors in Computing Systems - Proceedings}, author={Ogan, A. and Finkelstein, S. and Mayfield, E. and D’Adamo, C. and Matsuda, N. and Cassell, J.}, year={2012}, pages={39–48} }
 @inproceedings{namatame_matsuda_2012, title={An application of peer review for art education: A tablet PC becomes a language for students who are hard of hearing}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84860789561&partnerID=MN8TOARS}, DOI={10.1109/WMUTE.2012.43}, abstractNote={This paper reports on a classroom practice that focuses on the effectiveness of peer review in drawing picture education for hard-of-hearing students. We developed a peer review application for art in special education (PRAISE). PRAISE consists of a basic evaluation function and a direct comment function that has been implemented in a tablet PC. We followed up on 2 sets of 30 artworks over two years and compared the results of paper review and using our application. We found that the hard-of-hearing students enjoyed collaborative learning using the application. We proposed an improved design of the peer review application for art education that would be useful for hard-of-hearing students.}, booktitle={Proceedings 2012 17th IEEE International Conference on Wireless, Mobile and Ubiquitous Technology in Education, WMUTE 2012}, author={Namatame, M. and Matsuda, N.}, year={2012}, pages={190–192} }
 @article{toyose_nishino_asaba_matsuda_2012, title={An empirical study on the effect of Kansei-database for middle school students to learn Waka-reading comprehension}, volume={36}, number={2}, journal={Japan Journal of Educational Technology}, author={Toyose, H. and Nishino, N. and Asaba, N. and Matsuda, N.}, year={2012}, pages={125–134} }
 @book{carlson_keiser_matsuda_koedinger_penstein rosé_2012, title={Building a conversational simstudent}, volume={7315 LNCS}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84862502036&partnerID=MN8TOARS}, DOI={10.1007/978-3-642-30950-2_73}, abstractNote={SimStudent, an intelligent-agent architecture that generates a cognitive model from worked-out examples, currently interacts with human subjects only in a limited capacity. In our application, SimStudent attempts to solve algebra equations, querying the user about the correctness of each step as it solves, and the user explains the step in natural language. Based on that input, SimStudent can choose to ask further questions that prompt the user to think harder about the problem in an attempt to elicit deeper responses. We show how text classification techniques can be used to train models that can distinguish between different categories of student feedback to SimStudent, and how this enables interaction with SimStudent in a pilot study.}, journal={Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}, author={Carlson, R. and Keiser, V. and Matsuda, N. and Koedinger, K.R. and Penstein Rosé, C.}, year={2012}, pages={563–569} }
 @book{matsuda_yarzebinski_keiser_raizada_stylianides_koedinger_2012, title={Motivational factors for learning by teaching: The effect of a competitive game show in a virtual peer-Learning Environment}, volume={7315 LNCS}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84862497137&partnerID=MN8TOARS}, DOI={10.1007/978-3-642-30950-2_14}, abstractNote={To study the impact of extrinsic motivational intervention, a competitive Game Show was integrated into an on-line learning environment where students learn algebra equation solving by teaching a synthetic peer learner, called SimStudent. In the Game Show, a pair of SimStudents competed with each other by solving challenging problems to achieve higher ratings. To evaluate the effectiveness of the Game Show in the context of learning by teaching, we conducted a classroom study with 141 students in 7thto 9th grade. The results showed that to facilitate students’ learning, the Game Show setting must be carefully designed so that (1) the Game Show goal and learning goal are aligned, and (2) it fosters a symbiotic scenario in which both winners and losers of the game show learn.}, journal={Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}, author={Matsuda, N. and Yarzebinski, E. and Keiser, V. and Raizada, R. and Stylianides, G. and Koedinger, K.R.}, year={2012}, pages={101–111} }
 @inproceedings{matsuda_cohen_koedinger_keiser_raizada_yarzebinski_watson_stylianides_2012, title={Studying the effect of tutor learning using a teachable agent that asks the student tutor for explanations}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84860821398&partnerID=MN8TOARS}, DOI={10.1109/DIGITEL.2012.12}, abstractNote={We have built Sim Student, a computational model of learning, and applied it as a peer learner that allows students to learn by teaching. Using Sim Student, we study the effect of tutor learning. In this paper, we discuss an empirical classroom study where we evaluated whether asking students to provide explanations for their tutoring activities facilitates tutor learning - the self-explanation effect for tutor learning. The results showed that students in the self-explanation condition displayed the same amount of learning gain as students in the non-self-explanation condition, but with a significantly smaller number of problems tutored (during the same time). The study also showed an apparent increase in effectiveness relative to a prior study, which is arguably due to improvement of the system based on the iterative system-engineering effort.}, booktitle={Proceedings 2012 4th IEEE International Conference on Digital Game and Intelligent Toy Enhanced Learning, DIGITEL 2012}, author={Matsuda, N. and Cohen, W.W. and Koedinger, K.R. and Keiser, V. and Raizada, R. and Yarzebinski, E. and Watson, S.P. and Stylianides, G.}, year={2012}, pages={25–32} }
 @inproceedings{li_matsuda_cohen_koedinger_2011, title={A machine learning approach for automatic student model discovery}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84863408562&partnerID=MN8TOARS}, booktitle={EDM 2011 - Proceedings of the 4th International Conference on Educational Data Mining}, author={Li, N. and Matsuda, N. and Cohen, W.W. and Koedinger, K.R.}, year={2011}, pages={31–40} }
 @book{matsuda_yarzebinski_keiser_raizada_stylianides_cohen_koedinger_2011, title={Learning by teaching simstudent - An initial classroom baseline study comparing with cognitive tutor}, volume={6738 LNAI}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-79959316637&partnerID=MN8TOARS}, DOI={10.1007/978-3-642-21869-9_29}, abstractNote={This paper describes an application of a machine-learning agent, SimStudent, as a teachable peer learner that allows a student to learn by teaching. SimStudent has been integrated into APLUS (Artificial Peer Learning environment Using SimStudent), an on-line game-like learning environment. The first classroom study was conducted in local public high schools to test the effectiveness of APLUS for learning linear algebra equations. In the study, learning by teaching (i.e., APLUS) was compared with learning by tutored-problem solving (i.e., Cognitive Tutor). The results show that the prior knowledge has a strong influence on tutor learning – for students with insufficient training on the target problems, learning by teaching may have limited benefits compared to learning by tutored problem solving. It was also found that students often use inappropriate problems to tutor SimStudent that did not effectively facilitate the tutor learning.}, journal={Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}, author={Matsuda, N. and Yarzebinski, E. and Keiser, V. and Raizada, R. and Stylianides, G.J. and Cohen, W.W. and Koedinger, K.R.}, year={2011}, pages={213–221} }
 @book{matsuda_keiser_raizada_stylianides_cohen_koedinger_2011, title={Learning by teaching simstudent - Interactive event}, volume={6738 LNAI}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-79959303781&partnerID=MN8TOARS}, DOI={10.1007/978-3-642-21869-9_124}, abstractNote={SimStudent is an educational software infrastructure which is designed to leverage the tutor effect in an on-line learning environment. Tutor effect is the phenomenon that students learn when they teach others. SimStudent allows students to learn by teaching a computer agent instead of their peers. SimStudent is a lively computer agent that inductively learns skills through its own tutored-problem solving experience. SimStudent is integrated into an on-line learning environment where students can interactively tutor SimStudent in how to solve equations [1].}, journal={Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}, author={Matsuda, N. and Keiser, V. and Raizada, R. and Stylianides, G. and Cohen, W.W. and Koedinger, K.R.}, year={2011}, pages={623} }
 @book{matsuda_keiser_raizada_stylianides_cohen_koedinger_2010, title={Learning by teaching SimStudent}, volume={6095 LNCS}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-77955899194&partnerID=MN8TOARS}, DOI={10.1007/978-3-642-13437-1_106}, abstractNote={The effect of tutor learning has been studied in various contexts, providing ample evidence to suggest that students learn when they teach others. Yet, the cognitive and social factors that facilitate or inhibit tutor learning are still not well understood. One factor that prohibited research progress in this area is that studying the tutor learning effect could often be done only at the cost of tutees’ learning. To address this problem, we built an on-line learning environment where students learn by teaching a computer agent, called SimStudent, rather than their peers [1].}, number={PART 2}, journal={Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}, author={Matsuda, N. and Keiser, V. and Raizada, R. and Stylianides, G. and Cohen, W.W. and Koedinger, K.}, year={2010}, pages={449} }
 @book{matsuda_keiser_raizada_tu_stylianides_cohen_koedinger_2010, title={Learning by teaching SimStudent: Technical accomplishments and an initial use with students}, volume={6094 LNCS}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-79957441827&partnerID=MN8TOARS}, DOI={10.1007/978-3-642-13388-6_36}, abstractNote={The purpose of the current study is to test whether we could create a system where students can learn by teaching a live machine-learning agent, called SimStudent. SimStudent is a computer agent that interactively learns cognitive skills through its own tutored-problem solving experience. We have developed a game-like learning environment where students learn algebra equations by tutoring SimStudent. While Simulated Students, Teachable Agents and Learning Companion systems have been created, our study is unique that it genuinely learns skills from student input. This paper describes the overview of the learning environment and some results from an evaluation study. The study showed that after tutoring SimStudent, the students improved their performance on equation solving. The number of correct answers on the error detection items was also significantly improved. On average students spent 70.0 minutes on tutoring SimStudent and used an average of 15 problems for tutoring.}, number={PART 1}, journal={Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}, author={Matsuda, N. and Keiser, V. and Raizada, R. and Tu, A. and Stylianides, G. and Cohen, W.W. and Koedinger, K.R.}, year={2010}, pages={317–326} }
 @inproceedings{li_matsuda_cohen_koedinger_2010, title={Towards a computational model of why some students learn faster than others}, volume={FS-10-01}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-79960149222&partnerID=MN8TOARS}, booktitle={AAAI Fall Symposium - Technical Report}, author={Li, N. and Matsuda, N. and Cohen, W.W. and Koedinger, K.R.}, year={2010}, pages={40–46} }
 @inproceedings{matsuda_cohen_koedinger_stylianides_keiser_raizada_2010, title={Tuning cognitive tutors into a platform for learning-by-teaching with SimStudent technology}, volume={587}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84888214372&partnerID=MN8TOARS}, booktitle={CEUR Workshop Proceedings}, author={Matsuda, N. and Cohen, W.W. and Koedinger, K.R. and Stylianides, G. and Keiser, V. and Raizada, R.}, year={2010}, pages={20–25} }
 @book{matsuda_cohen_sewall_lacerda_koedinger_2008, title={Why tutored problem solving may be better than example study: Theoretical implications from a simulated-student study}, volume={5091 LNCS}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-70349865310&partnerID=MN8TOARS}, DOI={10.1007/978-3-540-69132-7-16}, journal={Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}, author={Matsuda, N. and Cohen, W.W. and Sewall, J. and Lacerda, G. and Koedinger, K.R.}, year={2008}, pages={111–121} }
 @book{matsuda_cohen_sewall_lacerda_koedinger_2007, title={Evaluating a simulated student using real students data for training and testing}, volume={4511 LNCS}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-37249054311&partnerID=MN8TOARS}, journal={Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}, author={Matsuda, N. and Cohen, W.W. and Sewall, J. and Lacerda, G. and Koedinger, K.R.}, year={2007}, pages={107–116} }
 @inbook{matsuda_2006, place={Tokyo, Japan}, title={How to get a Ph.D in America}, booktitle={University Authority}, publisher={Minervashobo Publishers Inc}, author={Matsuda, N.}, editor={Arimoto, Akira and Kitagaki, IkuoEditors}, year={2006}, pages={132–137} }
 @inproceedings{matsuda_cohen_koedinger_2005, title={Applying programming by demonstration in an intelligent authoring tool for cognitive tutors}, volume={WS-05-04}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-33646048713&partnerID=MN8TOARS}, booktitle={AAAI Workshop - Technical Report}, author={Matsuda, N. and Cohen, W.W. and Koedinger, K.R.}, year={2005}, pages={1–8} }
 @inbook{matsuda_2005, place={Tokyo, Japan}, title={Instructional strategies}, booktitle={Encyclopedia of Artificial Intelligence}, publisher={Japan Society of Artificial Intelligence}, author={Matsuda, N.}, editor={Tanaka, HozumiEditor}, year={2005} }
 @inbook{matsuda_2005, place={Tokyo, Japan}, title={Natural language processing in educational systems}, booktitle={Encyclopedia of Artificial Intelligence}, publisher={Japan Society of Artificial Intelligence}, author={Matsuda, N.}, editor={Tanaka, HozumiEditor}, year={2005} }
 @article{matsuda_vanlehn_2004, title={GRAMY: A geometry theorem prover capable of construction}, volume={32}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-3543078258&partnerID=MN8TOARS}, DOI={10.1023/B:JARS.0000021960.39761.b7}, number={1}, journal={Journal of Automated Reasoning}, author={Matsuda, N. and Vanlehn, K.}, year={2004}, pages={3–33} }
 @inproceedings{matsuda_vanlehn_2003, title={Modeling hinting strategies for geometry theorem proving}, volume={2702}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-8344255630&partnerID=MN8TOARS}, booktitle={Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)}, author={Matsuda, N. and VanLehn, K.}, year={2003}, pages={373–377} }
 @book{matsuda_vanlehn_2000, title={A reification of a strategy for geometry theorem proving}, volume={1839}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84944321228&partnerID=MN8TOARS}, journal={Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}, author={Matsuda, N. and VanLehn, K.}, year={2000}, pages={660} }
 @inbook{matsuda_1999, place={Tokyo, Japan}, title={Cognitive model of geometry theorem proving with construction and its application to intelligent tutoring systems}, booktitle={Towards new practical theories in mathematics education}, publisher={Toyokan Publishers Inc}, author={Matsuda, N.}, editor={Sugiyama, YoshishigeEditor}, year={1999} }
 @article{ochi_matsuda_okamoto_1998, title={An object oriented distributed working environment to integrate cooperative work and personal work}, volume={39}, number={1}, journal={Transactions of Information Processing Society of Japan}, author={Ochi, T. and Matsuda, N. and Okamoto, T.}, year={1998}, pages={123–130} }
 @article{yoshida_matsuda_okamoto_1997, title={The system for supporting to learn/diagnose Z notation}, volume={14}, number={1}, journal={Transaction of Japan Society for Information and Systems in Education}, author={Yoshida, T. and Matsuda, N. and Okamoto, T.}, year={1997}, pages={3–12} }
 @article{okamoto_matsuda_sasaki_1996, title={Intelligent CAI for geometric theorem proving with dynamic manipulative interface}, volume={37}, number={9}, journal={Transactions of Information Processing Society of Japan}, author={Okamoto, T. and Matsuda, N. and Sasaki, H.}, year={1996}, pages={1679–1687} }
 @article{okamoto_matsuda_furiya_1995, title={A study of the relationship between programming abilities and academic achievement in junior high school mathematics}, volume={19}, number={2}, journal={Japan Journal of Educational Technology}, author={Okamoto, T. and Matsuda, N. and Furiya, T.}, year={1995}, pages={85–100} }
 @article{okamoto_morihiro_matsuda_takuma_1994, title={Application of analogical reasoning and extraction of tutoring rules for concept-formation learning}, volume={77}, ISSN={1042-0967 1520-6440}, url={http://dx.doi.org/10.1002/ecjc.4430770307}, DOI={10.1002/ecjc.4430770307}, abstractNote={AbstractThe concept formation in humans is made through a process where the common items and rules in the observed events are extracted. The observed events may contain an event which is not consistent with the already formed concept. In such a case, the modification or restructuring of the concept takes place and an effort is made toward differentiation or recombination of the concept.With respect to the process where a human forms or acquires a concept, Klausmeier et al. considered the development of the levels in the concept formation. They defined four levels which are the real level, equivalence level, analogy level, and formal level and discussed the order among the levels. In this study, the inference engine is built into the expert module of ITS and the system model to support the concept formation is considered, which contains the intellectual function corresponding to the analogy level.As a domain to handle a realistic problem, a simple transformation of a geometrical figure is considered and the following functions are realized: (1) construction of analogical reasoning engine in the transformation of geometrical figures, (2) construction of the student model which represents the student's analogical reasoning process, and (3) generation of problems based on the student model.}, number={3}, journal={Electronics and Communications in Japan (Part III: Fundamental Electronic Science)}, publisher={Wiley}, author={Okamoto, Toshio and Morihiro, Koichiro and Matsuda, Noboru and Takuma, Shimpei}, year={1994}, pages={75–86} }
 @article{okamoto_matsuda_yasuda_1994, title={Study of CAI with algorithm diagnosis system for novice C programmers}, volume={11}, number={2}, journal={Journal of Japan Society for CAI}, author={Okamoto, T. and Matsuda, N. and Yasuda, K.}, year={1994}, pages={63–74} }
 @inbook{matsuda_1993, place={Tokyo}, title={Computer networking}, booktitle={Introduction to Information Education for Teachers: Cases in High-School Education}, publisher={Personal Media}, author={Matsuda, N.}, editor={Okamoto, T.Editor}, year={1993}, pages={180–197} }
 @article{matsuda_nagashima_okamoto_takuma_1993, title={On the system of learning and diagnosis for fostering space concept}, volume={10}, number={3}, journal={Journal of Japan Society for CAI}, author={Matsuda, N. and Nagashima, S. and Okamoto, T. and Takuma, S.}, year={1993}, pages={114–121} }
 @article{matsuda_okamoto_1993, title={Student modeling for procedural problem solving}, volume={E77-D}, number={1}, journal={IEICE Transactions on Information and Systems}, author={Matsuda, N. and Okamoto, T.}, year={1993}, pages={49–56} }
 @inbook{matsuda_1992, place={Tokyo, Japan}, title={Foundations of Computers}, booktitle={Introduction to Information Education for Teachers: Cases in Middle-School Education}, publisher={Personal Media}, author={Matsuda, N.}, editor={Okamoto, T.Editor}, year={1992}, pages={88–119} }
 @article{matsuda_okamoto_1992, title={Mental model of the process of composing geometric proofs using an intelligent tutoring system}, volume={15}, number={4}, journal={Japan Journal of Educational Technology}, author={Matsuda, N. and Okamoto, T.}, year={1992}, pages={167–182} }
 @article{okamoto_matsuda_1992, title={Overview on the studies of intelligent CAIs/ITSs in Japan}, volume={15}, number={1-2}, journal={Educational Technology Research}, author={Okamoto, T. and Matsuda, N.}, year={1992}, pages={1–8} }
 @article{matsuda_okamoto_1992, title={Student model and its recognition by hypothesis-based reasoning in ITS}, volume={75}, ISSN={1042-0967 1520-6440}, url={http://dx.doi.org/10.1002/ecjc.4430750807}, DOI={10.1002/ecjc.4430750807}, abstractNote={AbstractThis paper describes a framework to infer a student's misconception from observed errors during problem‐solving processes. A human teacher can generate hypotheses about reasons for an error by observing a student's problem‐solving process. The teacher is also able to identify a student's misconception during the process of verifying these hypotheses. Furthermore, by using these hypotheses, the teacher can generate new tasks to evaluate the student's understanding level. In this way, appropriate instructions based on the student's knowledge structure can be provided.To accomplish such a behavior within an intelligent tutoring system (ITS), the authors have defined a domain model and applied hypothesis‐based reasoning to diagnose the student model. When the system finds an error in a student's problem‐solving process, it attempts to generate hypotheses which explain that error in terms of the domain model.}, number={8}, journal={Electronics and Communications in Japan (Part III: Fundamental Electronic Science)}, publisher={Wiley}, author={Matsuda, Noboru and Okamoto, Toshio}, year={1992}, pages={85–95} }
 @article{okamoto_matsuda_takuma_1991, title={A knowledge based CAD to support students’ learning elementary geometric concepts and diagnosing their misconceptions}, volume={14}, number={4}, journal={Japan Journal of Educational Technology}, author={Okamoto, T. and Matsuda, N. and Takuma, S.}, year={1991}, pages={147–157} }
 @article{matsuda_okamoto_1990, title={An automatic generation of knowledge-base for an intelligent CAI on geometry theorem proving and a GUI to draw geometric figures}, volume={J73-D-II}, number={1}, journal={Transactions of the Institution of Electronics, Information, and Communication Engineering}, author={Matsuda, N. and Okamoto, T.}, year={1990}, pages={88–99} }
 @inbook{matsuda_hatano_1990, place={Tokyo, Japan}, title={Knowledge communication}, booktitle={Artificial Intelligence and Tutoring Systems}, publisher={Ohmu Inc}, author={Matsuda, N. and Hatano, Kazuhiko}, editor={Okamoto, T. and Mizoguchi, RiichiroEditors}, year={1990}, pages={447–456} }
 @inbook{matsuda_1990, place={Tokyo, Japan}, title={What is CAI?}, booktitle={Introduction to C Programming}, publisher={Personal Media}, author={Matsuda, N.}, editor={Okamoto, T.Editor}, year={1990}, pages={201–236} }
 @article{okamoto_matsuda_1989, title={Learning to recognize students’ plan in geometry proof using intelligent CAI}, volume={30}, number={8}, journal={Transactions of Information Processing Society of Japan}, author={Okamoto, T. and Matsuda, N.}, year={1989}, pages={1046–1057} }
 @article{okamoto_matsuda_1988, title={An intelligent CAI for geometry proof}, volume={29}, number={3}, journal={Transactions of Information Processing Society of Japan}, author={Okamoto, T. and Matsuda, N.}, year={1988}, pages={311–324} }
 @inbook{matsuda_1988, place={Tokyo, Japan}, title={Drill, Practice, and Machine Learning}, booktitle={Computer environments for children}, publisher={Personal Media}, author={Matsuda, N.}, editor={Okamoto, T. and Akahori, Kanji and Yokoyama, SetsuoEditors}, year={1988}, pages={21–40} }