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Detecting student frustration based on handwriting behavior

Authors:

Hiroki Asai,

Hayato YamanaAuthors Info & Claims

UIST '13 Adjunct: Adjunct Proceedings of the 26th Annual ACM Symposium on User Interface Software and Technology

Pages 77 - 78

https://doi.org/10.1145/2508468.2514718

Published: 08 October 2013 Publication History

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Abstract

Detecting states of frustration among students engaged in learning activities is critical to the success of teaching assistance tools. We examine the relationship between a student's pen activity and his/her state of frustration while solving handwritten problems. Based on a user study involving mathematics problems, we found that our detection method was able to detect student frustration with a precision of 87% and a recall of 90%. We also identified several particularly discriminative features, including writing stroke number, erased stroke number, pen activity time, and air stroke speed.

References

[1]

Lazard, D.P., Poulovassilis, A., and Geraniou, E. The design of teacher assistance tools in an exploratory learning environment for mathematics generalization. In Proc. EC-TEL 2010, LNCS, No.6383 (2010), 260--275.

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[2]

Kapoor, A., Burleson, W., and Picard, R.W. Automatics prediction of frustration, Int'l J. Human-Computer Studies 65, Elsevier (2007), 724--736.

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[3]

Oviatt, S., and Arthur, A., and Cohen, J. Quiet interfaces that help students think. In Proc. UIST 2006, ACM Press (2006), 191--200.

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[4]

Yu, K., Epps, J., and Chen, F. Cognitive Load Evaluation of Handwriting Using Stroke-level Features. In Proc. IUI 2011, ACM Press (2011), 423--426.

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Cited By

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Urayama KShimakawa H(2024)Analyzing Handwritten Log Data for Evaluating Computational Thinking: Investigating State Transitions in Drawing Tasks Using Pen-Based TabletsProceedings of Ninth International Congress on Information and Communication Technology10.1007/978-981-97-3305-7_50(639-647)Online publication date: 30-Jul-2024
https://doi.org/10.1007/978-981-97-3305-7_50
Schrapel MGrannemann DRohs M(2022)Sign H3re: Symbol and X-Mark Writer Identification Using Audio and Motion Data from a Digital PenProceedings of Mensch und Computer 202210.1145/3543758.3543764(209-218)Online publication date: 4-Sep-2022
https://dl.acm.org/doi/10.1145/3543758.3543764
Zhou MZhou M(2022)Operationalization of Goal FrustrationGoal Frustration in Academic Achievement Settings10.1007/978-981-19-7477-9_2(23-47)Online publication date: 4-Dec-2022
https://doi.org/10.1007/978-981-19-7477-9_2
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Index Terms

Detecting student frustration based on handwriting behavior
1. Computing methodologies
  1. Artificial intelligence
    1. Philosophical/theoretical foundations of artificial intelligence
      1. Cognitive science
2. Human-centered computing
  1. Human computer interaction (HCI)

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Published In

UIST '13 Adjunct: Adjunct Proceedings of the 26th Annual ACM Symposium on User Interface Software and Technology

October 2013

126 pages

ISBN:9781450324069

DOI:10.1145/2508468

General Chairs:
Shahram Izadi
Microsoft Research, UK
,
Aaron Quigley
University of St Andrews, UK
,
Program Chairs:
Ivan Poupyrev
Disney Research, USA
,
Takeo Igarashi
The University of Tokyo, Japan

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

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Published: 08 October 2013

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UIST'13

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UIST'13: The 26th Annual ACM Symposium on User Interface Software and Technology

October 8 - 11, 2013

St. Andrews, Scotland, United Kingdom

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UIST '13 Adjunct Paper Acceptance Rate 49 of 317 submissions, 15%;

Overall Acceptance Rate 355 of 1,733 submissions, 20%

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UIST '25

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The 38th Annual ACM Symposium on User Interface Software and Technology

September 28 - October 1, 2025

Busan , Republic of Korea

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Urayama KShimakawa H(2024)Analyzing Handwritten Log Data for Evaluating Computational Thinking: Investigating State Transitions in Drawing Tasks Using Pen-Based TabletsProceedings of Ninth International Congress on Information and Communication Technology10.1007/978-981-97-3305-7_50(639-647)Online publication date: 30-Jul-2024
https://doi.org/10.1007/978-981-97-3305-7_50
Schrapel MGrannemann DRohs M(2022)Sign H3re: Symbol and X-Mark Writer Identification Using Audio and Motion Data from a Digital PenProceedings of Mensch und Computer 202210.1145/3543758.3543764(209-218)Online publication date: 4-Sep-2022
https://dl.acm.org/doi/10.1145/3543758.3543764
Zhou MZhou M(2022)Operationalization of Goal FrustrationGoal Frustration in Academic Achievement Settings10.1007/978-981-19-7477-9_2(23-47)Online publication date: 4-Dec-2022
https://doi.org/10.1007/978-981-19-7477-9_2
Maruichi TUragami TVargo AKise KTentori MWeibel NVan Laerhoven KAbowd GSalim F(2020)Handwriting behavior as a self-confidence discriminatorAdjunct Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computers10.1145/3410530.3414383(78-81)Online publication date: 10-Sep-2020
https://dl.acm.org/doi/10.1145/3410530.3414383
Maruichi TIshimaru SKise K(2019)Self-Confidence Estimation on Vocabulary Tests with Stroke-Level Handwriting Logs2019 International Conference on Document Analysis and Recognition Workshops (ICDARW)10.1109/ICDARW.2019.20047(18-22)Online publication date: Sep-2019
https://doi.org/10.1109/ICDARW.2019.20047
Maruichi TAugereau OIwata MKise K(2018)Keystrokes Tell You How Confident You AreProceedings of the 2018 ACM International Joint Conference and 2018 International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers10.1145/3267305.3267609(154-157)Online publication date: 8-Oct-2018
https://dl.acm.org/doi/10.1145/3267305.3267609
Schrapel MStadler MRohs MMandryk RHancock MPerry MCox A(2018)PentelligenceProceedings of the 2018 CHI Conference on Human Factors in Computing Systems10.1145/3173574.3173705(1-11)Online publication date: 21-Apr-2018
https://dl.acm.org/doi/10.1145/3173574.3173705
Tsuruoka SKimura SHayakawa KTakase HKawanaka H(2016)Support teachers for quiz in large class — Analysis of typing processes for descriptive answers2016 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)10.1109/FSKD.2016.7603452(1803-1807)Online publication date: Aug-2016
https://doi.org/10.1109/FSKD.2016.7603452
Asai HYamana H(2015)Detecting Learner's To-Be-Forgotten Items using Online Handwritten DataProceedings of the 15th New Zealand Conference on Human-Computer Interaction10.1145/2808047.2808049(17-20)Online publication date: 3-Sep-2015
https://dl.acm.org/doi/10.1145/2808047.2808049
Williams DWang MChang CAhamed SChu W(2014)ShowMeHow: Using Smart, Interactive Tutorials in Elderly Software DevelopmentSmart Homes and Health Telematics10.1007/978-3-319-14424-5_6(49-58)Online publication date: 27-Dec-2014
https://doi.org/10.1007/978-3-319-14424-5_6

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Handwriting Recognition in Indian Regional Scripts: A Survey of Offline Techniques

Effect of Text/Non-text Classification for Ink Search Employing String Recognition

Arabic online handwriting recognition: a survey

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