Works (82)

Updated: October 27th, 2024 05:04

2024 article

"I Am Confused! How to Differentiate Between.?" Adaptive Follow-Up Questions Facilitate Tutor Learning with Effective Time-On-Task

ARTIFICIAL INTELLIGENCE IN EDUCATION, PT II, AIED 2024, Vol. 14830, pp. 17–30.

By: T. Shahriar* & N. Matsuda*

author keywords: Learning by teaching; conversational questions; large language model; in-context learning
Sources: Web Of Science, NC State University Libraries
Added: October 21, 2024

2023 chapter

Machine-Generated Questions Attract Instructors When Acquainted with Learning Objectives

By: M. Shimmei n, N. Bier* & N. Matsuda n

UN Sustainable Development Goal Categories
4. Quality Education (OpenAlex)
Source: ORCID
Added: June 26, 2023

2023 chapter

What and How You Explain Matters: Inquisitive Teachable Agent Scaffolds Knowledge-Building for Tutor Learning

By: T. Shahriar n & N. Matsuda n

UN Sustainable Development Goal Categories
4. Quality Education (OpenAlex)
Source: ORCID
Added: February 2, 2024

2022 book

Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners’ and Doctoral Consortium

Noboru Matsuda

Event: 23rd International Conference, AIED 2022 at Durham, UK on July 27-31, 2022

Sources: Crossref, NC State University Libraries
Added: November 8, 2023

2022 article

Teaching How to Teach Promotes Learning by Teaching

Matsuda, N., Lv, D., & Zheng, G. (2022, August 31). INTERNATIONAL JOURNAL OF ARTIFICIAL INTELLIGENCE IN EDUCATION, Vol. 8.

By: N. Matsuda n, D. Lv* & G. Zheng n

author keywords: Teachable agent; Learning by teaching; Algebra; Personalized learning; Metacognitive scaffolding
UN Sustainable Development Goal Categories
4. Quality Education (Web of Science)
Sources: ORCID, Web Of Science, NC State University Libraries
Added: September 12, 2022

2021 article

"Can You Clarify What You Said?": Studying the Impact of Tutee Agents' Follow-Up Questions on Tutors' Learning

ARTIFICIAL INTELLIGENCE IN EDUCATION (AIED 2021), PT I, Vol. 12748, pp. 395–407.

By: T. Shahriar n & N. Matsuda n

author keywords: Learning by teaching; Deep questions; Teachable agents; Tutor learning; Knowledge-building; Wizard of Oz
UN Sustainable Development Goal Categories
4. Quality Education (Web of Science; OpenAlex)
Sources: Web Of Science, NC State University Libraries, ORCID
Added: November 28, 2022

2021 article

Learning Association Between Learning Objectives and Key Concepts to Generate Pedagogically Valuable Questions

ARTIFICIAL INTELLIGENCE IN EDUCATION (AIED 2021), PT II, Vol. 12749, pp. 320–324.

By: M. Shimmei n & N. Matsuda n

author keywords: Question generation; MOOCS; Learning engineering
TL;DR: 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. (via Semantic Scholar)
Sources: Web Of Science, NC State University Libraries, ORCID
Added: October 13, 2021

2021 article

Teachable Agent as an Interactive Tool for Cognitive Task Analysis: A Case Study for Authoring an Expert Model

Matsuda, N. (2021, July 12). INTERNATIONAL JOURNAL OF ARTIFICIAL INTELLIGENCE IN EDUCATION, Vol. 32.

By: N. Matsuda n

author keywords: Authoring tools and methods; Cognitive task analysis; Human-computer interaction; Intelligent tutoring systems; Interactive learning environment
TL;DR: It was observed that the failure of TA to learn expected cognitive skills helps the author identify missing essential cognitive factors that must be encoded in the OLE. (via Semantic Scholar)
UN Sustainable Development Goal Categories
4. Quality Education (Web of Science; OpenAlex)
Sources: ORCID, Web Of Science, NC State University Libraries
Added: July 26, 2021

2020 journal article

Development and validation of the teachers' digital learning identity survey

INTERNATIONAL JOURNAL OF EDUCATIONAL RESEARCH, 105.

By: W. Zimmer*, E. McTigue* & N. Matsuda n

author keywords: Improving classroom teaching; 21st century abilities; Teacher professional development; Lifelong learning
TL;DR: This study investigates the reliability and validity of theDLIS with pre-service teachers using exploratory and confirmatory factor analyses and found aspects of the DLIS validly measure DLI. (via Semantic Scholar)
UN Sustainable Development Goal Categories
4. Quality Education (Web of Science; OpenAlex)
Sources: Web Of Science, NC State University Libraries
Added: March 15, 2021

2020 journal article

The Effect of Metacognitive Scaffolding for Learning by Teaching a Teachable Agent

International Journal of Artificial Intelligence in Education, 30(1), 1–37.

By: N. Matsuda n, W. Weng* & N. Wall*

author keywords: Learning by teaching; Goal-oriented practice; Teachable agent; Algebra
TL;DR: Students’ proficiency in solving equations increased after using interventions for 4 days, but there was no difference in the effectiveness across three interventions, and learning by teaching with metacognitive scaffolding facilitated learning equally across various levels of students’ prior competency. (via Semantic Scholar)
UN Sustainable Development Goal Categories
4. Quality Education (Web of Science; OpenAlex)
Sources: Web Of Science, NC State University Libraries, Crossref, ORCID
Added: April 6, 2020

2019 chapter

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.

By: S. Shen, M. Shimmei, M. Chi & N. Matsuda

Ed(s): A. Sinatra, A. Graesser, X. Hu, K. Brawner & V. Rus

Source: NC State University Libraries
Added: November 28, 2020

2019 article

Evidence-Based Recommendation for Content Improvement Using Reinforcement Learning

ARTIFICIAL INTELLIGENCE IN EDUCATION, AIED 2019, PT II, Vol. 11626, pp. 369–373.

By: M. Shimmei n & N. Matsuda n

Contributors: M. Shimmei n & N. Matsuda n

author keywords: Learning-engineering; Self-improving online courseware; Reinforcement learning
TL;DR: An evidence-based learning-engineering method for validating the quality of instructional elements on online courseware is proposed and can detect more than a half of the ineffective educational elements on three types of courseware containing various ratios of ineffective instructional elements. (via Semantic Scholar)
UN Sustainable Development Goal Categories
4. Quality Education (Web of Science; OpenAlex)
Sources: Web Of Science, ORCID, NC State University Libraries
Added: December 2, 2019

2019 conference paper

PASTEL: Evidence-based learning engineering method to create intelligent online textbook at scale

CEUR Workshop Proceedings, 2384, 70–80. http://www.scopus.com/inward/record.url?eid=2-s2.0-85067813785&partnerID=MN8TOARS

By: N. Matsuda & M. Shimmei

Contributors: N. Matsuda & M. Shimmei

Source: ORCID
Added: February 11, 2020

2018 book

Metacognitive scaffolding amplifies the effect of learning by teaching a teachable agent

In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 311–323).

By: N. Matsuda*, V. Sekar* & N. Wall*

Contributors: N. Matsuda*, V. Sekar* & N. Wall*

author keywords: Learning by teaching; Goal-Oriented Practice; Teachable agent; Cognitive tutor; Algebra
TL;DR: The results suggest that with the metacognitive scaffolding, learning by teaching is equally effective as cognitive tutoring regardless of the prior competency. (via Semantic Scholar)
UN Sustainable Development Goal Categories
4. Quality Education (OpenAlex)
Source: ORCID
Added: February 11, 2020

2018 journal article

The State-of-the-Art Pedagogical Agent Technology in the Field of Learning Science

Transactions of Japanese Society for Information and Systems in Education, 35(1), 13–20. Retrieved from

By: N. Matsuda

Source: NC State University Libraries
Added: November 28, 2020

2018 conference paper

Using design patterns for math preservice teacher education

ACM International Conference Proceeding Series.

By: P. Inventado*, Y. Li*, N. Heffernan*, S. Inventado*, P. Scupelli*, S. Tu*, N. Matsuda*, K. Ostrow* ...

Contributors: P. Inventado*, Y. Li*, N. Heffernan*, S. Inventado*, P. Scupelli*, S. Tu*, N. Matsuda*, K. Ostrow* ...

author keywords: preservice teacher education; educational design patterns; evaluation; ASSISTments
TL;DR: Investigation of 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 indicated that design patterns helped preservICE teachers consider other aspects of feedback. (via Semantic Scholar)
UN Sustainable Development Goal Categories
4. Quality Education (OpenAlex)
Source: ORCID
Added: February 11, 2020

2017 chapter

Instructional Strategy

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

By: N. Matsuda

Ed(s): H. Matsubara

Source: NC State University Libraries
Added: November 28, 2020

2017 chapter

Intelligent Pedagogical Agents

In H. Matsubara (Ed.), Encyclopedia of Artificial Intelligence (pp. 1152–1153). Tokyo, Japan: Japan Society of Artificial Intelligence.

By: N. Matsuda

Ed(s): H. Matsubara

Source: NC State University Libraries
Added: November 28, 2020

2017 conference paper

Investigating the effects of cognitive and metacognitive scaffolding on learners using a learning by teaching environment

Proceedings of the 25th International Conference on Computers in Education, ICCE 2017 - Main Conference Proceedings, 1–10. http://www.scopus.com/inward/record.url?eid=2-s2.0-85051544348&partnerID=MN8TOARS

By: C. Dumdumaya, M. Banawan, M. Rodrigo, A. Ogan, E. Yarzebinski & N. Matsuda

Contributors: C. Dumdumaya, M. Banawan, M. Rodrigo, A. Ogan, E. Yarzebinski & N. Matsuda

Source: ORCID
Added: February 11, 2020

2017 chapter

Natural language processing in educational systems

In H. Matsubara (Ed.), Encyclopedia of Artificial Intelligence (p. 1101). Tokyo, Japan: Society of Artificial Intelligence.

By: N. Matsuda

Ed(s): H. Matsubara

Source: NC State University Libraries
Added: November 28, 2020

2017 book

Regional cultural differences in how students customize their avatars in technology-enhanced learning

In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 598–601).

author keywords: Avatar; Personalized learning systems; Culture
TL;DR: It is found that US students do customize as expected, while students in the Philippines tend to select names and hairstyles from outside their culture, showing the need for more nuanced system design to tailor options for regional-level preferences. (via Semantic Scholar)
Source: ORCID
Added: February 11, 2020

2016 book

Cognitive tutors produce adaptive online course: Inaugural field trial

In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9684, pp. 327–333).

By: N. Matsuda*, M. Velsen*, N. Barbalios*, S. Lin*, H. Vasa*, R. Hosseini*, K. Sutner*, N. Bier*

Contributors: N. Matsuda*, M. Velsen*, N. Barbalios*, S. Lin*, H. Vasa*, R. Hosseini*, K. Sutner*, N. Bier*

author keywords: Adaptive online course; Active learning; Cognitive tutors; Authoring by demonstration; SimStudent
TL;DR: An adaptive online course is developed on the Open Learning Initiative OLI platform by integrating four new instances of cognitive tutors into an existing OLI course, and the results show that the proposed adaptive onlinecourse technology is robust enough to be used in actual classroom with mixed effect for learning. (via Semantic Scholar)
UN Sustainable Development Goal Categories
4. Quality Education (OpenAlex)
Source: ORCID
Added: February 11, 2020

2016 journal article

Development of a Peer Review System for Art Education and its Evaluation

Transactions of Japan Society of Kansei Engineering, 15(4), 425–430.

By: M. Namatame* & N. Matsuda*

UN Sustainable Development Goal Categories
4. Quality Education (OpenAlex)
Sources: Crossref, NC State University Libraries
Added: September 13, 2020

2016 conference paper

How quickly can wheel spinning be detected?

Proceedings of the 9th International Conference on Educational Data Mining, EDM 2016, 607–608. http://www.scopus.com/inward/record.url?eid=2-s2.0-85067333848&partnerID=MN8TOARS

By: N. Matsuda, S. Chandrasekaran & J. Stamper

Contributors: N. Matsuda, S. Chandrasekaran & J. Stamper

Source: ORCID
Added: February 11, 2020

2016 book

Tell me how to teach, I’ll learn how to solve problems

In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9684, pp. 111–121).

By: N. Matsuda*, N. Barbalios*, Z. Zhao*, A. Ramamurthy*, G. Stylianides* & K. Koedinger*

Contributors: N. Matsuda*, N. Barbalios*, Z. Zhao*, A. Ramamurthy*, G. Stylianides* & K. Koedinger*

author keywords: Teachable agent; Learning by teaching; Algebra; Adaptive scaffolding; SimStudent
TL;DR: 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. (via Semantic Scholar)
UN Sustainable Development Goal Categories
4. Quality Education (OpenAlex)
Source: ORCID
Added: February 11, 2020

2015 journal article

Application of Waka-Kansei Database for Learning Japanese Waka in Middle School

Japan Journal of Educational Technology, 38(4), 329–340.

By: K. Toyose, N. Asaba, H. Yamaguchi, K. Nishino & N. Matsuda

Source: NC State University Libraries
Added: November 24, 2021

2015 chapter

Authoring Example-based Tutors for Procedural Tasks

In R. Sottilare, A. Graesser, X. Hu, & K. Brawner (Eds.), Design Recommendations for Intelligent Tutoring Systems: Authoring Tools & Expert Modeling Techniques (Vol. 3, pp. 71–94). Orlando, FL: U.S. Army Research Laboratory.

By: S. Blessing, V. Aleven, S. Gilbert, N. Heffernan, N. Matsuda & A. Mitrovic

Ed(s): R. Sottilare, A. Graesser, X. Hu & K. Brawner

Source: NC State University Libraries
Added: April 5, 2021

2015 conference paper

Authoring tutors with complex solutions: A comparative analysis of Example Tracing and SimStudent

CEUR Workshop Proceedings, 1432, 35–44. http://www.scopus.com/inward/record.url?eid=2-s2.0-84944328396&partnerID=MN8TOARS

By: C. Maclellan, E. Harpstead, E. Wiese, M. Zou, N. Matsuda, V. Aleven, K. Koedinger

Contributors: C. Maclellan, E. Harpstead, E. Wiese, M. Zou, N. Matsuda, V. Aleven, K. Koedinger

Source: ORCID
Added: February 11, 2020

2015 conference paper

Methods for evaluating simulated learners: Examples from SimStudent

CEUR Workshop Proceedings, 1432, 45–54. http://www.scopus.com/inward/record.url?eid=2-s2.0-84944315929&partnerID=MN8TOARS

By: K. Koedinger, N. Matsuda, C. Maclellan & E. McLaughlin

Contributors: K. Koedinger, N. Matsuda, C. Maclellan & E. McLaughlin

Source: ORCID
Added: February 11, 2020

2015 book

Understanding students’ use of code-switching in a learning by teaching technology

In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9112, pp. 504–513).

Contributors: E. Yarzebinski*, A. Ogan*, M. Rodrigo* & N. Matsuda*

author keywords: Teachable agents; Personalized learning systems; Self-explanations; Code-switching
TL;DR: This investigation in classrooms in the Philippines 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. (via Semantic Scholar)
UN Sustainable Development Goal Categories
4. Quality Education (OpenAlex)
Source: ORCID
Added: February 11, 2020

2014 book

Authoring tutors with simstudent: An evaluation of efficiency and model quality

In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 551–560).

By: C. MacLellan*, K. Koedinger* & N. Matsuda*

Contributors: C. MacLellan*, K. Koedinger* & N. Matsuda*

TL;DR: It is found that authoring an algebra tutor in SimStudent is faster than Example-Tracing while maintaining equivalent final model quality, and the SimStudent model generalizes beyond the problems that were used to author it. (via Semantic Scholar)
Source: ORCID
Added: February 11, 2020

2014 journal article

Integrating representation learning and skill learning in a human-like intelligent agent

Artificial Intelligence, 219, 67–91.

By: N. Li*, N. Matsuda*, W. Cohen* & K. Koedinger*

Contributors: N. Li*, N. Matsuda*, W. Cohen* & K. Koedinger*

author keywords: Agent learning; Representation learning; Student modeling
TL;DR: An efficient algorithm that acquires representation knowledge in the form of "deep features" reduces the requirements for knowledge engineering and is integrated 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. (via Semantic Scholar)
Source: ORCID
Added: February 11, 2020

2014 book

Investigating the effect of meta-cognitive scaffolding for learning by teaching

In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 104–113).

By: N. Matsuda*, C. Griger*, N. Barbalios*, G. Stylianides*, W. Cohen* & K. Koedinger*

Contributors: N. Matsuda*, C. Griger*, N. Barbalios*, G. Stylianides*, W. Cohen* & K. Koedinger*

TL;DR: The 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. (via Semantic Scholar)
UN Sustainable Development Goal Categories
4. Quality Education (OpenAlex)
Source: ORCID
Added: February 11, 2020

2014 journal article

Teaching the teacher: Tutoring simstudent leads to more effective cognitive tutor authoring

International Journal of Artificial Intelligence in Education, 25(1), 1–34.

By: N. Matsuda*, W. Cohen* & K. Koedinger*

Contributors: N. Matsuda*, W. Cohen* & K. Koedinger*

author keywords: Intelligent authoring system; Machine learning; Programming by demonstration; Inductive logic programming; Cognitive tutor authoring tools; SimStudent
TL;DR: This work conducted evaluation studies to investigate which authoring strategy better facilitates authoring and found two key results. (via Semantic Scholar)
UN Sustainable Development Goal Categories
4. Quality Education (OpenAlex)
Source: ORCID
Added: February 11, 2020

2013 journal article

Cognitive anatomy of tutor learning: Lessons learned with SimStudent.

Journal of Educational Psychology, 105(4), 1152–1163.

By: N. Matsuda*, E. Yarzebinski, V. Keiser, R. Raizada, W. Cohen, G. Stylianides, K. Koedinger

Contributors: N. Matsuda*, E. Yarzebinski, V. Keiser, R. Raizada, W. Cohen, G. Stylianides, K. Koedinger

author keywords: learning by teaching; machine learning; SimStudent; teachable agent; tutor learning
TL;DR: 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. (via Semantic Scholar)
UN Sustainable Development Goal Categories
4. Quality Education (OpenAlex)
Sources: ORCID, Crossref, NC State University Libraries
Added: February 11, 2020

2013 journal article

Exploring the Implications of Tutor Negativity Towards a Synthetic Agent in a Learning-by-Teaching Environment

Philippine Computing Journal, 8(1), 15–20.

By: M. Rodrigo, R. Geli, A. Ong, G. Vitug, R. Bringula, R. Basa, C. Dela Cruz, N. Matsuda

Source: NC State University Libraries
Added: November 24, 2021

2013 conference paper

Impact of prior knowledge and teaching strategies on learning by teaching

CEUR Workshop Proceedings, 1009, 71–80. http://www.scopus.com/inward/record.url?eid=2-s2.0-84924982067&partnerID=MN8TOARS

By: M. Rodrigo, A. Ong, R. Bringula, R. Basa, C. Dela Cruz & N. Matsuda

Contributors: M. Rodrigo, A. Ong, R. Bringula, R. Basa, C. Dela Cruz & N. Matsuda

Source: ORCID
Added: February 11, 2020

2013 journal article

Studying the effect of a competitive game show in a learning by teaching environment

International Journal of Artificial Intelligence in Education, 23(1-4), 1–21.

By: N. Matsuda*, E. Yarzebinski*, V. Keiser*, R. Raizada, G. Stylianides* & K. Koedinger*

Contributors: N. Matsuda*, E. Yarzebinski*, V. Keiser*, R. Raizada, G. Stylianides* & K. Koedinger*

author keywords: Learning by teaching; Teachable agent; Motivation and engagement; SimStudent; Algebra
TL;DR: 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, found no notable correlation between students' motivation (intrinsic or extrinsic) and tutor learning. (via Semantic Scholar)
UN Sustainable Development Goal Categories
4. Quality Education (OpenAlex)
Source: ORCID
Added: February 11, 2020

2013 conference paper

Toward a reflective SimStudent: Using experience to avoid generalization errors

CEUR Workshop Proceedings, 1009, 51–60. http://www.scopus.com/inward/record.url?eid=2-s2.0-84924980730&partnerID=MN8TOARS

By: C. MacLellan, N. Matsuda & K. Koedinger

Contributors: C. MacLellan, N. Matsuda & K. Koedinger

Source: ORCID
Added: February 11, 2020

2012 conference paper

"Oh, dear Stacy!" Social interaction, elaboration, and learning with teachable agents

Conference on Human Factors in Computing Systems - Proceedings, 39–48.

By: A. Ogan*, S. Finkelstein*, E. Mayfield*, C. D’Adamo*, N. Matsuda* & J. Cassell*

Contributors: A. Ogan*, S. Finkelstein*, E. Mayfield*, . C. D'Adamo, N. Matsuda* & J. Cassell*

TL;DR: Treating her as a partner, primarily through aligning oneself with Stacy using pronouns like you or the authors 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. (via Semantic Scholar)
UN Sustainable Development Goal Categories
4. Quality Education (OpenAlex)
Source: ORCID
Added: February 11, 2020

2012 conference paper

An application of peer review for art education: A tablet PC becomes a language for students who are hard of hearing

Proceedings 2012 17th IEEE International Conference on Wireless, Mobile and Ubiquitous Technology in Education, WMUTE 2012, 190–192.

By: M. Namatame* & N. Matsuda*

Contributors: M. Namatame* & N. Matsuda*

TL;DR: It was found that the hard-of-hearing students enjoyed collaborative learning using the PRAISE application and the improved design of the peer review application for art education that would be useful for hard- of- hearing students was proposed. (via Semantic Scholar)
UN Sustainable Development Goal Categories
4. Quality Education (OpenAlex)
Source: ORCID
Added: February 11, 2020

2012 journal article

An empirical study on the effect of Kansei-database for middle school students to learn Waka-reading comprehension

Japan Journal of Educational Technology, 36(2), 125–134.

By: H. Toyose, N. Nishino, N. Asaba & N. Matsuda

Source: NC State University Libraries
Added: November 24, 2021

2012 book

Building a conversational simstudent

In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 563–569).

By: R. Carlson*, V. Keiser*, N. Matsuda*, K. Koedinger* & C. Penstein Rosé*

Contributors: R. Carlson*, V. Keiser*, N. Matsuda*, K. Koedinger* & C. Penstein Rosé*

TL;DR: It is shown 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. (via Semantic Scholar)
UN Sustainable Development Goal Categories
4. Quality Education (OpenAlex)
Source: ORCID
Added: February 11, 2020

2012 book

Motivational factors for learning by teaching: The effect of a competitive game show in a virtual peer-Learning Environment

In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 101–111).

By: N. Matsuda*, E. Yarzebinski*, V. Keiser*, R. Raizada*, G. Stylianides* & K. Koedinger*

Contributors: N. Matsuda*, E. Yarzebinski*, V. Keiser*, R. Raizada*, G. Stylianides* & K. Koedinger*

TL;DR: To facilitate students' learning, the Game Show setting must be carefully designed so that its goal and learning goal are aligned, and it fosters a symbiotic scenario in which both winners and losers of the game show learn. (via Semantic Scholar)
UN Sustainable Development Goal Categories
4. Quality Education (OpenAlex)
Source: ORCID
Added: February 11, 2020

2012 conference paper

Studying the effect of tutor learning using a teachable agent that asks the student tutor for explanations

Proceedings 2012 4th IEEE International Conference on Digital Game and Intelligent Toy Enhanced Learning, DIGITEL 2012, 25–32.

By: N. Matsuda*, W. Cohen*, K. Koedinger*, V. Keiser*, R. Raizada*, E. Yarzebinski*, S. Watson*, G. Stylianides*

Contributors: N. Matsuda*, W. Cohen*, K. Koedinger*, V. Keiser*, R. Raizada*, E. Yarzebinski*, S. Watson*, G. Stylianides*

TL;DR: An empirical classroom study where it was evaluated whether asking students to provide explanations for their tutoring activities facilitates tutor learning - the self-explanation effect for tutor learning. (via Semantic Scholar)
UN Sustainable Development Goal Categories
4. Quality Education (OpenAlex)
Source: ORCID
Added: February 11, 2020

2011 conference paper

A machine learning approach for automatic student model discovery

EDM 2011 - Proceedings of the 4th International Conference on Educational Data Mining, 31–40. http://www.scopus.com/inward/record.url?eid=2-s2.0-84863408562&partnerID=MN8TOARS

By: N. Li, N. Matsuda, W. Cohen & K. Koedinger

Contributors: N. Li, N. Matsuda, W. Cohen & K. Koedinger

Source: ORCID
Added: February 11, 2020

2011 book

Learning by teaching simstudent - An initial classroom baseline study comparing with cognitive tutor

In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 213–221).

By: N. Matsuda*, E. Yarzebinski*, V. Keiser*, R. Raizada*, G. Stylianides, W. Cohen*, K. Koedinger*

Contributors: N. Matsuda*, E. Yarzebinski*, V. Keiser*, R. Raizada*, G. Stylianides, W. Cohen*, K. Koedinger*

TL;DR: It was found that students often use inappropriate problems to tutor SimStudent that did not effectively facilitate the tutor learning, and for students with insufficient training on the target problems, learning by teaching may have limited benefits compared to learning by tutored problem solving. (via Semantic Scholar)
UN Sustainable Development Goal Categories
4. Quality Education (OpenAlex)
Source: ORCID
Added: February 11, 2020

2011 book

Learning by teaching simstudent - Interactive event

In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (p. 623).

By: N. Matsuda*, V. Keiser*, R. Raizada*, G. Stylianides*, W. Cohen* & K. Koedinger*

Contributors: N. Matsuda*, V. Keiser*, R. Raizada*, G. Stylianides*, W. Cohen* & K. Koedinger*

TL;DR: SimStudent is an educational software infrastructure which is designed to leverage the tutor effect in an on-line learning environment to allow students to learn by teaching a computer agent instead of their peers. (via Semantic Scholar)
Source: ORCID
Added: February 11, 2020

2010 book

Learning by teaching SimStudent

In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (p. 449).

By: N. Matsuda*, V. Keiser*, R. Raizada*, G. Stylianides*, W. Cohen* & K. Koedinger*

Contributors: N. Matsuda*, V. Keiser*, R. Raizada*, G. Stylianides*, W. Cohen* & K. Koedinger*

TL;DR: An on-line learning environment where students learn by teaching a computer agent, called SimStudent, rather than their peers, to address the lack of understood cognitive and social factors that facilitate or inhibit tutor learning. (via Semantic Scholar)
Source: ORCID
Added: February 11, 2020

2010 book

Learning by teaching SimStudent: Technical accomplishments and an initial use with students

In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 317–326).

By: N. Matsuda*, V. Keiser*, R. Raizada*, A. Tu*, G. Stylianides*, W. Cohen*, K. Koedinger*

Contributors: N. Matsuda*, V. Keiser*, R. Raizada*, A. Tu*, G. Stylianides*, W. Cohen*, K. Koedinger*

TL;DR: The study showed that after tutoring SimStudent, the students improved their performance on equation solving and the number of correct answers on the error detection items was also significantly improved. (via Semantic Scholar)
UN Sustainable Development Goal Categories
4. Quality Education (OpenAlex)
Source: ORCID
Added: February 11, 2020

2010 conference paper

Towards a computational model of why some students learn faster than others

AAAI Fall Symposium - Technical Report, FS-10-01, 40–46. http://www.scopus.com/inward/record.url?eid=2-s2.0-79960149222&partnerID=MN8TOARS

By: N. Li, N. Matsuda, W. Cohen & K. Koedinger

Contributors: N. Li, N. Matsuda, W. Cohen & K. Koedinger

Source: ORCID
Added: February 11, 2020

2010 conference paper

Tuning cognitive tutors into a platform for learning-by-teaching with SimStudent technology

CEUR Workshop Proceedings, 587, 20–25. http://www.scopus.com/inward/record.url?eid=2-s2.0-84888214372&partnerID=MN8TOARS

By: N. Matsuda, W. Cohen, K. Koedinger, G. Stylianides, V. Keiser & R. Raizada

Contributors: N. Matsuda, W. Cohen, K. Koedinger, G. Stylianides, V. Keiser & R. Raizada

Source: ORCID
Added: February 11, 2020

2008 book

Why tutored problem solving may be better than example study: Theoretical implications from a simulated-student study

In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 111–121).

By: N. Matsuda, W. Cohen, J. Sewall, G. Lacerda & K. Koedinger

Contributors: N. Matsuda, W. Cohen, J. Sewall, G. Lacerda & K. Koedinger

Source: ORCID
Added: February 11, 2020

2007 book

Evaluating a simulated student using real students data for training and testing

In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 107–116). http://www.scopus.com/inward/record.url?eid=2-s2.0-37249054311&partnerID=MN8TOARS

By: N. Matsuda, W. Cohen, J. Sewall, G. Lacerda & K. Koedinger

Contributors: N. Matsuda, W. Cohen, J. Sewall, G. Lacerda & K. Koedinger

Source: ORCID
Added: February 11, 2020

2006 chapter

How to get a Ph.D in America

In A. Arimoto & I. Kitagaki (Eds.), University Authority (pp. 132–137). Tokyo, Japan: Minervashobo Publishers Inc.

By: N. Matsuda

Ed(s): A. Arimoto & I. Kitagaki

Source: NC State University Libraries
Added: November 24, 2021

2005 conference paper

Applying programming by demonstration in an intelligent authoring tool for cognitive tutors

AAAI Workshop - Technical Report, WS-05-04, 1–8. http://www.scopus.com/inward/record.url?eid=2-s2.0-33646048713&partnerID=MN8TOARS

By: N. Matsuda, W. Cohen & K. Koedinger

Contributors: N. Matsuda, W. Cohen & K. Koedinger

Source: ORCID
Added: February 11, 2020

2005 chapter

Instructional strategies

In H. Tanaka (Ed.), Encyclopedia of Artificial Intelligence. Tokyo, Japan: Japan Society of Artificial Intelligence.

By: N. Matsuda

Ed(s): H. Tanaka

Source: NC State University Libraries
Added: November 24, 2021

2005 chapter

Natural language processing in educational systems

In H. Tanaka (Ed.), Encyclopedia of Artificial Intelligence. Tokyo, Japan: Japan Society of Artificial Intelligence.

By: N. Matsuda

Ed(s): H. Tanaka

Source: NC State University Libraries
Added: December 26, 2021

2004 journal article

GRAMY: A geometry theorem prover capable of construction

Journal of Automated Reasoning, 32(1), 3–33.

By: N. Matsuda* & K. Vanlehn*

Contributors: N. Matsuda* & K. Vanlehn*

author keywords: automated geometry theorem proving; construction; search control; constraint satisfaction problem; intelligent tutoring system
TL;DR: This study investigates a procedure for proving arithmetic-free Euclidean geometry theorems that involve construction, and finds that the proof procedure is semi-complete and useful in practice. (via Semantic Scholar)
Source: ORCID
Added: February 11, 2020

2003 conference paper

Modeling hinting strategies for geometry theorem proving

Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science), 2702, 373–377. http://www.scopus.com/inward/record.url?eid=2-s2.0-8344255630&partnerID=MN8TOARS

By: N. Matsuda & K. VanLehn

Contributors: N. Matsuda & K. VanLehn

Source: ORCID
Added: February 11, 2020

2000 book

A reification of a strategy for geometry theorem proving

In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 1839, p. 660). http://www.scopus.com/inward/record.url?eid=2-s2.0-84944321228&partnerID=MN8TOARS

By: N. Matsuda & K. VanLehn

Contributors: N. Matsuda & K. VanLehn

Source: ORCID
Added: February 11, 2020

1999 chapter

Cognitive model of geometry theorem proving with construction and its application to intelligent tutoring systems

In Y. Sugiyama (Ed.), Towards new practical theories in mathematics education. Tokyo, Japan: Toyokan Publishers Inc.

By: N. Matsuda

Ed(s): Y. Sugiyama

Source: NC State University Libraries
Added: December 26, 2021

1998 journal article

An object oriented distributed working environment to integrate cooperative work and personal work

Transactions of Information Processing Society of Japan, 39(1), 123–130.

By: T. Ochi, N. Matsuda & T. Okamoto

Source: NC State University Libraries
Added: December 26, 2021

1997 journal article

The system for supporting to learn/diagnose Z notation

Transaction of Japan Society for Information and Systems in Education, 14(1), 3–12.

By: T. Yoshida, N. Matsuda & T. Okamoto

Source: NC State University Libraries
Added: December 26, 2021

1996 journal article

Intelligent CAI for geometric theorem proving with dynamic manipulative interface

Transactions of Information Processing Society of Japan, 37(9), 1679–1687.

By: T. Okamoto, N. Matsuda & H. Sasaki

Source: NC State University Libraries
Added: December 26, 2021

1995 journal article

A study of the relationship between programming abilities and academic achievement in junior high school mathematics

Japan Journal of Educational Technology, 19(2), 85–100.

By: T. Okamoto, N. Matsuda & T. Furiya

Source: NC State University Libraries
Added: December 26, 2021

1994 journal article

Application of analogical reasoning and extraction of tutoring rules for concept-formation learning

Electronics and Communications in Japan (Part III: Fundamental Electronic Science), 77(3), 75–86.

By: T. Okamoto*, K. Morihiro*, N. Matsuda* & S. Takuma*

author keywords: INTELLIGENT CAI; ANALOGICAL REASONING; LEARNING OF CONCEPT; DIAGNOSIS OF STUDENT MODEL
TL;DR: 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. (via Semantic Scholar)
Sources: Crossref, NC State University Libraries
Added: September 13, 2020

1994 journal article

Study of CAI with algorithm diagnosis system for novice C programmers

Journal of Japan Society for CAI, 11(2), 63–74.

By: T. Okamoto, N. Matsuda & K. Yasuda

Source: NC State University Libraries
Added: December 26, 2021

1993 chapter

Computer networking

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

By: N. Matsuda

Ed(s): T. Okamoto

Source: NC State University Libraries
Added: December 26, 2021

1993 journal article

On the system of learning and diagnosis for fostering space concept

Journal of Japan Society for CAI, 10(3), 114–121.

By: N. Matsuda, S. Nagashima, T. Okamoto & S. Takuma

Source: NC State University Libraries
Added: December 26, 2021

1993 journal article

Student modeling for procedural problem solving

IEICE Transactions on Information and Systems, E77-D(1), 49–56.

By: N. Matsuda & T. Okamoto

Source: NC State University Libraries
Added: December 26, 2021

1992 chapter

Foundations of Computers

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

By: N. Matsuda

Ed(s): T. Okamoto

Source: NC State University Libraries
Added: December 26, 2021

1992 journal article

Mental model of the process of composing geometric proofs using an intelligent tutoring system

Japan Journal of Educational Technology, 15(4), 167–182.

By: N. Matsuda & T. Okamoto

Source: NC State University Libraries
Added: December 26, 2021

1992 journal article

Overview on the studies of intelligent CAIs/ITSs in Japan

Educational Technology Research, 15(1-2), 1–8.

By: T. Okamoto & N. Matsuda

Source: NC State University Libraries
Added: December 26, 2021

1992 journal article

Student model and its recognition by hypothesis-based reasoning in ITS

Electronics and Communications in Japan (Part III: Fundamental Electronic Science), 75(8), 85–95.

By: N. Matsuda* & T. Okamoto*

TL;DR: A framework to infer a student's misconception from observed errors during problem-solving processes is described, which is defined a domain model and applied hypothesis-based reasoning to diagnose the student model. (via Semantic Scholar)
UN Sustainable Development Goal Categories
4. Quality Education (OpenAlex)
Sources: Crossref, NC State University Libraries
Added: September 13, 2020

1991 journal article

A knowledge based CAD to support students’ learning elementary geometric concepts and diagnosing their misconceptions

Japan Journal of Educational Technology, 14(4), 147–157.

By: T. Okamoto, N. Matsuda & S. Takuma

Source: NC State University Libraries
Added: December 26, 2021

1990 journal article

An automatic generation of knowledge-base for an intelligent CAI on geometry theorem proving and a GUI to draw geometric figures

Transactions of the Institution of Electronics, Information, and Communication Engineering, J73-D-II(1), 88–99.

By: N. Matsuda & T. Okamoto

Source: NC State University Libraries
Added: December 26, 2021

1990 chapter

Knowledge communication

In T. Okamoto & R. Mizoguchi (Eds.), Artificial Intelligence and Tutoring Systems (pp. 447–456). Tokyo, Japan: Ohmu Inc.

By: N. Matsuda & K. Hatano

Ed(s): T. Okamoto & R. Mizoguchi

Source: NC State University Libraries
Added: December 26, 2021

1990 chapter

What is CAI?

In T. Okamoto (Ed.), Introduction to C Programming (pp. 201–236). Tokyo, Japan: Personal Media.

By: N. Matsuda

Ed(s): T. Okamoto

Source: NC State University Libraries
Added: December 26, 2021

1989 journal article

Learning to recognize students’ plan in geometry proof using intelligent CAI

Transactions of Information Processing Society of Japan, 30(8), 1046–1057.

By: T. Okamoto & N. Matsuda

Source: NC State University Libraries
Added: December 26, 2021

1988 journal article

An intelligent CAI for geometry proof

Transactions of Information Processing Society of Japan, 29(3), 311–324.

By: T. Okamoto & N. Matsuda

Source: NC State University Libraries
Added: December 26, 2021

1988 chapter

Drill, Practice, and Machine Learning

In T. Okamoto, K. Akahori, & S. Yokoyama (Eds.), Computer environments for children (pp. 21–40). Tokyo, Japan: Personal Media.

By: N. Matsuda

Ed(s): T. Okamoto, K. Akahori & S. Yokoyama

Source: NC State University Libraries
Added: December 26, 2021

Employment

Updated: December 11th, 2019 00:14

2018 - present

North Carolina State University Raleigh, NC, US
Associate Professor Computer Science

2015 - 2018

Texas A&M University College Station, Texas, US
Associate Professor Teaching, Learning & Culture

2009 - 2015

Carnegie Mellon University Pittsburgh, PA, US
Systems Scientist Human-Computer Interaction Institute

2004 - 2009

Carnegie Mellon University Pittsburgh, PA, US
Postdoctoral Research Fellow Human-Computer Interaction Institute

1993 - 1998

University of Electro-Communications Tokyo, JP
Assistant Professor Graduate School of Information Systems

1988 - 1993

Kanazawa Institute of Technology Nonoichi, JP
Assistant Professor Center for Computer Assisted Instruction

Education

Updated: January 14th, 2024 20:40

Tokyo Gakugei University Tokyo, JP
BS Mathematics Education

Tokyo Gakugei University Tokyo, JP
MS Mathematics Education

1999 - 2004

University of Pittsburgh Pittsburgh, PA, US
PhD Intelligent Systems Program

Funding History

Funding history based on the linked ORCID record. Updated: December 10th, 2019 14:21

grant September 1, 2018 - August 31, 2021
Developing an Online Learning Environment for Learning Algebra by Teaching a Synthetic Peer
United States Department of Education
grant September 1, 2016 - August 31, 2018
EXP: Exploratory Study on the Adaptive Online Course and its Implication on Synergetic Competency
Directorate for Computer & Information Science & Engineering
grant September 1, 2015 - September 30, 2017
Learning by Teaching a Synthetic Peer: Investigating the effect of tutor scaffolding for tutor learning
Directorate for Education & Human Resources
grant September 1, 2015 - July 31, 2017
Data-Driven Methods to Improve Student Learning from Online Courses
Directorate for Education & Human Resources
grant August 1, 2014 - July 31, 2016
Data-Driven Methods to Improve Student Learning from Online Courses
Directorate for Education & Human Resources
grant April 1, 2014 - March 31, 2018
Study on Learning Effect for Lecture Videos with Scrolling Through Text Comments Sent by Learners and Development of System aiming at Stimulated Discussion
Japan Society for the Promotion of Science
grant October 1, 2013 - July 31, 2016
Learning by Teaching a Synthetic Peer: Investigating the effect of tutor scaffolding for tutor learning
Directorate for Education & Human Resources
grant August 1, 2009 - January 31, 2014
Empirical Research: Emerging Research: Learning by Teaching a Synthetic Student: Using SimStudent to Study the Effect of Tutor Learning
Directorate for Education & Human Resources
grant June 1, 2009 - May 1, 2012
Learning by Teaching Synthetic Student: Using SimStudent to Study the Effect of Tutor Learning
United States Department of Education
grant January 1, 2008 - December 31, 2011
The Release of the communication learning support system for a people with developmental disorders
Japan Society for the Promotion of Science
grant September 15, 2005 - August 31, 2009
Building Cognitive Tutors with Programming by Demonstration: When Simulated Students help Cognitive Modeling and Educational Studies
Directorate for Education & Human Resources
grant January 1 - December 31, 1996
知的教援システムを指向した超分散高次問題解決モデルの構築
Japan Society for the Promotion of Science
grant January 1, 1996 - December 31, 1997
Development of a Case-Based Reasoning System and a CSCL System for Practices of IT-Education to Teachers
Japan Society for the Promotion of Science
grant January 1 - December 31, 1995
問題解決を支援する知的CAIにおける教材知識の自己構成手法と学習者モデル診断
Japan Society for the Promotion of Science
grant January 1, 1995 - December 31, 1996
Intelligent interactive system for extracting knowledge of pedagogical strategies/tactics on instructional design
Japan Society for the Promotion of Science
grant January 1, 1993 - December 31, 1994
Study on the formative process of a self-identity in a distributed cooperative learning environment.
Japan Society for the Promotion of Science
grant January 1, 1993 - December 31, 1994
授業設計のためのエキスパートシステムの研究・開発
Japan Society for the Promotion of Science
grant January 1, 1993 - December 31, 1994
探究学習を支援するITSにおける学習者モデル診断システムの構築
Japan Society for the Promotion of Science
grant January 1 - December 31, 1990
知的CAIにおける知識獲得機能を組み入れた学習者モデルの構成について
Japan Society for the Promotion of Science
grant January 1 - December 31, 1989
知的CAIにおけるメンタルモデルを用いた学習者モデルの構成について
Japan Society for the Promotion of Science

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