Conceptualization of IMS that estimates learners’ mental states from learners’ physiological information using deep neural network algorithm

Tatsunori Matsui; Yoshimasa Tawatsuji; Siyuan Fang; Tatsuro Uno

doi:10.1007/978-3-030-22244-4_9

Conceptualization of IMS that estimates learners’ mental states from learners’ physiological information using deep neural network algorithm

Tatsunori Matsui^*, Yoshimasa Tawatsuji, Siyuan Fang, Tatsuro Uno

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

4 Citations (Scopus)

Abstract

To improve the efficiency of teaching and learning, it is substantially important to know learners’ mental states during their learning processes. In this study, we tried to extract the relationships between the learner’s mental states and the learner’s physiological information complemented by the teacher’s speech acts using machine learning. The results of the system simulation showed that the system could estimate the learner’s mental states in high accuracy. Based on the construction of the system, we further discussed the concept of IMS and the necessary future work for IMS development.

Original language	English
Title of host publication	Intelligent Tutoring Systems - 15th International Conference, ITS 2019, Proceedings
Editors	Maiga Chang, Yugo Hayashi, Andre Coy
Publisher	Springer Verlag
Pages	63-71
Number of pages	9
ISBN (Print)	9783030222437
DOIs	https://doi.org/10.1007/978-3-030-22244-4_9
Publication status	Published - 2019
Event	15th International Conference on Intelligent Tutoring Systems, ITS 2019 - Kingston, Jamaica Duration: 2019 Jun 3 → 2019 Jun 7

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11528 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	15th International Conference on Intelligent Tutoring Systems, ITS 2019
Country/Territory	Jamaica
City	Kingston
Period	19/6/3 → 19/6/7

Keywords

Deep neural network
Intelligent mentoring system
Physiological information

ASJC Scopus subject areas

Theoretical Computer Science
Computer Science(all)

Access to Document

10.1007/978-3-030-22244-4_9

Cite this

Matsui, T., Tawatsuji, Y., Fang, S., & Uno, T. (2019). Conceptualization of IMS that estimates learners’ mental states from learners’ physiological information using deep neural network algorithm. In M. Chang, Y. Hayashi, & A. Coy (Eds.), Intelligent Tutoring Systems - 15th International Conference, ITS 2019, Proceedings (pp. 63-71). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11528 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-030-22244-4_9

Conceptualization of IMS that estimates learners’ mental states from learners’ physiological information using deep neural network algorithm. / Matsui, Tatsunori ; Tawatsuji, Yoshimasa; Fang, Siyuan et al.
Intelligent Tutoring Systems - 15th International Conference, ITS 2019, Proceedings. ed. / Maiga Chang; Yugo Hayashi; Andre Coy. Springer Verlag, 2019. p. 63-71 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11528 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Matsui, T , Tawatsuji, Y, Fang, S & Uno, T 2019, Conceptualization of IMS that estimates learners’ mental states from learners’ physiological information using deep neural network algorithm. in M Chang, Y Hayashi & A Coy (eds), Intelligent Tutoring Systems - 15th International Conference, ITS 2019, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11528 LNCS, Springer Verlag, pp. 63-71, 15th International Conference on Intelligent Tutoring Systems, ITS 2019, Kingston, Jamaica, 19/6/3. https://doi.org/10.1007/978-3-030-22244-4_9

Matsui T , Tawatsuji Y, Fang S, Uno T. Conceptualization of IMS that estimates learners’ mental states from learners’ physiological information using deep neural network algorithm. In Chang M, Hayashi Y, Coy A, editors, Intelligent Tutoring Systems - 15th International Conference, ITS 2019, Proceedings. Springer Verlag. 2019. p. 63-71. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-22244-4_9

Matsui, Tatsunori ; Tawatsuji, Yoshimasa ; Fang, Siyuan et al. / Conceptualization of IMS that estimates learners’ mental states from learners’ physiological information using deep neural network algorithm. Intelligent Tutoring Systems - 15th International Conference, ITS 2019, Proceedings. editor / Maiga Chang ; Yugo Hayashi ; Andre Coy. Springer Verlag, 2019. pp. 63-71 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{f5a67116661f499286e8f676f02a9cc8,

title = "Conceptualization of IMS that estimates learners{\textquoteright} mental states from learners{\textquoteright} physiological information using deep neural network algorithm",

abstract = "To improve the efficiency of teaching and learning, it is substantially important to know learners{\textquoteright} mental states during their learning processes. In this study, we tried to extract the relationships between the learner{\textquoteright}s mental states and the learner{\textquoteright}s physiological information complemented by the teacher{\textquoteright}s speech acts using machine learning. The results of the system simulation showed that the system could estimate the learner{\textquoteright}s mental states in high accuracy. Based on the construction of the system, we further discussed the concept of IMS and the necessary future work for IMS development.",

keywords = "Deep neural network, Intelligent mentoring system, Physiological information",

author = "Tatsunori Matsui and Yoshimasa Tawatsuji and Siyuan Fang and Tatsuro Uno",

note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2019.; 15th International Conference on Intelligent Tutoring Systems, ITS 2019 ; Conference date: 03-06-2019 Through 07-06-2019",

year = "2019",

doi = "10.1007/978-3-030-22244-4_9",

language = "English",

isbn = "9783030222437",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

pages = "63--71",

editor = "Maiga Chang and Yugo Hayashi and Andre Coy",

booktitle = "Intelligent Tutoring Systems - 15th International Conference, ITS 2019, Proceedings",

}

TY - GEN

T1 - Conceptualization of IMS that estimates learners’ mental states from learners’ physiological information using deep neural network algorithm

AU - Matsui, Tatsunori

AU - Tawatsuji, Yoshimasa

AU - Fang, Siyuan

AU - Uno, Tatsuro

N1 - Publisher Copyright: © Springer Nature Switzerland AG 2019.

PY - 2019

Y1 - 2019

N2 - To improve the efficiency of teaching and learning, it is substantially important to know learners’ mental states during their learning processes. In this study, we tried to extract the relationships between the learner’s mental states and the learner’s physiological information complemented by the teacher’s speech acts using machine learning. The results of the system simulation showed that the system could estimate the learner’s mental states in high accuracy. Based on the construction of the system, we further discussed the concept of IMS and the necessary future work for IMS development.

AB - To improve the efficiency of teaching and learning, it is substantially important to know learners’ mental states during their learning processes. In this study, we tried to extract the relationships between the learner’s mental states and the learner’s physiological information complemented by the teacher’s speech acts using machine learning. The results of the system simulation showed that the system could estimate the learner’s mental states in high accuracy. Based on the construction of the system, we further discussed the concept of IMS and the necessary future work for IMS development.

KW - Deep neural network

KW - Intelligent mentoring system

KW - Physiological information

UR - http://www.scopus.com/inward/record.url?scp=85067236405&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85067236405&partnerID=8YFLogxK

U2 - 10.1007/978-3-030-22244-4_9

DO - 10.1007/978-3-030-22244-4_9

M3 - Conference contribution

AN - SCOPUS:85067236405

SN - 9783030222437

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 63

EP - 71

BT - Intelligent Tutoring Systems - 15th International Conference, ITS 2019, Proceedings

A2 - Chang, Maiga

A2 - Hayashi, Yugo

A2 - Coy, Andre

PB - Springer Verlag

T2 - 15th International Conference on Intelligent Tutoring Systems, ITS 2019

Y2 - 3 June 2019 through 7 June 2019

ER -

Conceptualization of IMS that estimates learners’ mental states from learners’ physiological information using deep neural network algorithm

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this