Detecting learner's to-be-forgotten items using online handwritten data

Hiroki Asai; Hayato Yamana

doi:10.1145/2808047.2808049

Detecting learner's to-be-forgotten items using online handwritten data

Hiroki Asai, Hayato Yamana

School of Fundamental Science and Engineering

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

1 Citation (Scopus)

Abstract

An effective learning system is indispensable for human beings with a limited life span. Traditional learning systems schedule repetition based on both the results of a recall test and learning theories such as the spacing effect. However, there is room for improvement from the perspective of remembrance-level estimation. In this paper, we focus on on-line handwritten data obtained from handwriting using a computer. We collected handwritten data from remembrance tests to both analyze the problem of traditional estimation methods and to build a new estimation model using handwritten data as the input data. The evaluation found that our proposed model can output a continuous remembrance-level value of zero to 1, whereas traditional methods output a only binary decision. In addition, the experiment showed that our proposed model achieves the best performance with an F-value of 0.69.

Original language	English
Title of host publication	CHINZ 2015 - Proceedings of the 15th New Zealand Conference on Human-Computer Interaction
Editors	David M. Nichols, Masood Masoodian, Annika Hinze
Publisher	Association for Computing Machinery
Pages	17-20
Number of pages	4
ISBN (Print)	9781450336703
DOIs	https://doi.org/10.1145/2808047.2808049
Publication status	Published - 2015 Sep 3
Event	15th New Zealand Conference on Human-Computer Interaction, CHINZ 2015 - Hamilton, New Zealand Duration: 2015 Sep 3 → 2015 Sep 4

Publication series

Name	ACM International Conference Proceeding Series

Other

Other	15th New Zealand Conference on Human-Computer Interaction, CHINZ 2015
Country	New Zealand
City	Hamilton
Period	15/9/3 → 15/9/4

Keywords

Digital Ink
Handwriting
ITS
Language Learning
Rote Learning

ASJC Scopus subject areas

Software
Human-Computer Interaction
Computer Vision and Pattern Recognition
Computer Networks and Communications

Access to Document

10.1145/2808047.2808049

Fingerprint Dive into the research topics of 'Detecting learner's to-be-forgotten items using online handwritten data'. Together they form a unique fingerprint.

Cite this

Asai, H., & Yamana, H. (2015). Detecting learner's to-be-forgotten items using online handwritten data. In D. M. Nichols, M. Masoodian, & A. Hinze (Eds.), CHINZ 2015 - Proceedings of the 15th New Zealand Conference on Human-Computer Interaction (pp. 17-20). (ACM International Conference Proceeding Series). Association for Computing Machinery. https://doi.org/10.1145/2808047.2808049

Detecting learner's to-be-forgotten items using online handwritten data. / Asai, Hiroki; Yamana, Hayato.

CHINZ 2015 - Proceedings of the 15th New Zealand Conference on Human-Computer Interaction. ed. / David M. Nichols; Masood Masoodian; Annika Hinze. Association for Computing Machinery, 2015. p. 17-20 (ACM International Conference Proceeding Series).

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

Asai, H & Yamana, H 2015, Detecting learner's to-be-forgotten items using online handwritten data. in DM Nichols, M Masoodian & A Hinze (eds), CHINZ 2015 - Proceedings of the 15th New Zealand Conference on Human-Computer Interaction. ACM International Conference Proceeding Series, Association for Computing Machinery, pp. 17-20, 15th New Zealand Conference on Human-Computer Interaction, CHINZ 2015, Hamilton, New Zealand, 15/9/3. https://doi.org/10.1145/2808047.2808049

Asai H, Yamana H. Detecting learner's to-be-forgotten items using online handwritten data. In Nichols DM, Masoodian M, Hinze A, editors, CHINZ 2015 - Proceedings of the 15th New Zealand Conference on Human-Computer Interaction. Association for Computing Machinery. 2015. p. 17-20. (ACM International Conference Proceeding Series). https://doi.org/10.1145/2808047.2808049

@inproceedings{fdbc510d8e02407c8a5dfdd05327af16,

title = "Detecting learner's to-be-forgotten items using online handwritten data",

abstract = "An effective learning system is indispensable for human beings with a limited life span. Traditional learning systems schedule repetition based on both the results of a recall test and learning theories such as the spacing effect. However, there is room for improvement from the perspective of remembrance-level estimation. In this paper, we focus on on-line handwritten data obtained from handwriting using a computer. We collected handwritten data from remembrance tests to both analyze the problem of traditional estimation methods and to build a new estimation model using handwritten data as the input data. The evaluation found that our proposed model can output a continuous remembrance-level value of zero to 1, whereas traditional methods output a only binary decision. In addition, the experiment showed that our proposed model achieves the best performance with an F-value of 0.69.",

keywords = "Digital Ink, Handwriting, ITS, Language Learning, Rote Learning",

author = "Hiroki Asai and Hayato Yamana",

note = "Publisher Copyright: {\textcopyright} 2015 ACM International Conference Proceeding Series. All rights reserved. Copyright: Copyright 2018 Elsevier B.V., All rights reserved.; 15th New Zealand Conference on Human-Computer Interaction, CHINZ 2015 ; Conference date: 03-09-2015 Through 04-09-2015",

year = "2015",

month = sep,

day = "3",

doi = "10.1145/2808047.2808049",

language = "English",

isbn = "9781450336703",

series = "ACM International Conference Proceeding Series",

publisher = "Association for Computing Machinery",

pages = "17--20",

editor = "Nichols, {David M.} and Masood Masoodian and Annika Hinze",

booktitle = "CHINZ 2015 - Proceedings of the 15th New Zealand Conference on Human-Computer Interaction",

}

TY - GEN

T1 - Detecting learner's to-be-forgotten items using online handwritten data

AU - Asai, Hiroki

AU - Yamana, Hayato

PY - 2015/9/3

Y1 - 2015/9/3

N2 - An effective learning system is indispensable for human beings with a limited life span. Traditional learning systems schedule repetition based on both the results of a recall test and learning theories such as the spacing effect. However, there is room for improvement from the perspective of remembrance-level estimation. In this paper, we focus on on-line handwritten data obtained from handwriting using a computer. We collected handwritten data from remembrance tests to both analyze the problem of traditional estimation methods and to build a new estimation model using handwritten data as the input data. The evaluation found that our proposed model can output a continuous remembrance-level value of zero to 1, whereas traditional methods output a only binary decision. In addition, the experiment showed that our proposed model achieves the best performance with an F-value of 0.69.

AB - An effective learning system is indispensable for human beings with a limited life span. Traditional learning systems schedule repetition based on both the results of a recall test and learning theories such as the spacing effect. However, there is room for improvement from the perspective of remembrance-level estimation. In this paper, we focus on on-line handwritten data obtained from handwriting using a computer. We collected handwritten data from remembrance tests to both analyze the problem of traditional estimation methods and to build a new estimation model using handwritten data as the input data. The evaluation found that our proposed model can output a continuous remembrance-level value of zero to 1, whereas traditional methods output a only binary decision. In addition, the experiment showed that our proposed model achieves the best performance with an F-value of 0.69.

KW - Digital Ink

KW - Handwriting

KW - ITS

KW - Language Learning

KW - Rote Learning

UR - http://www.scopus.com/inward/record.url?scp=85051474896&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85051474896&partnerID=8YFLogxK

U2 - 10.1145/2808047.2808049

DO - 10.1145/2808047.2808049

M3 - Conference contribution

AN - SCOPUS:85051474896

SN - 9781450336703

T3 - ACM International Conference Proceeding Series

SP - 17

EP - 20

BT - CHINZ 2015 - Proceedings of the 15th New Zealand Conference on Human-Computer Interaction

A2 - Nichols, David M.

A2 - Masoodian, Masood

A2 - Hinze, Annika

PB - Association for Computing Machinery

T2 - 15th New Zealand Conference on Human-Computer Interaction, CHINZ 2015

Y2 - 3 September 2015 through 4 September 2015

ER -