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Cognitive skills learning: pen input patterns in computer-based athlete training

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Fang ChenAuthors Info & Claims

ICMI-MLMI '10: International Conference on Multimodal Interfaces and the Workshop on Machine Learning for Multimodal Interaction

Article No.: 41, Pages 1 - 4

https://doi.org/10.1145/1891903.1891955

Published: 08 November 2010 Publication History

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Abstract

In this paper, we describe a longitudinal user study with athletes using a cognitive training tool, equipped with an interactive pen interface, and think-aloud protocols. The aim is to verify whether cognitive load can be inferred directly from changes in geometric and temporal features of the pen trajectories. We compare trajectories across cognitive load levels and overall Pre and Post training tests. The results show trajectory durations and lengths decrease while speeds increase, all significantly, as cognitive load increases. These changes are attributed to mechanisms for dealing with high cognitive load in working memory, with minimal rehearsal. With more expertise, trajectory durations further decrease and speeds further increase, which is attributed in part to cognitive skill acquisition and to schema development, both in extraneous and intrinsic networks, between Pre and Post tests. As such, these pen trajectory features offer insight into implicit communicative changes related to load fluctuations.

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Cited By

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Kosch TKarolus JZagermann JReiterer HSchmidt AWoźniak P(2023)A Survey on Measuring Cognitive Workload in Human-Computer InteractionACM Computing Surveys10.1145/358227255:13s(1-39)Online publication date: 13-Jul-2023
https://dl.acm.org/doi/10.1145/3582272
Chen RXie TXie YLin TTang NNakano YAndré ENishida TMorency LBusso CPelachaud C(2016)Do speech features for detecting cognitive load depend on specific languages?Proceedings of the 18th ACM International Conference on Multimodal Interaction10.1145/2993148.2993149(76-83)Online publication date: 31-Oct-2016
https://dl.acm.org/doi/10.1145/2993148.2993149
Lin TWu ZChen Y(2015)Using High-Frequency Interaction Events to Automatically Classify Cognitive LoadHuman Behavior, Psychology, and Social Interaction in the Digital Era10.4018/978-1-4666-8450-8.ch010(210-228)Online publication date: 2015
https://doi.org/10.4018/978-1-4666-8450-8.ch010
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Index Terms

Cognitive skills learning: pen input patterns in computer-based athlete training
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)
    1. Interaction paradigms

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Information

Published In

ICMI-MLMI '10: International Conference on Multimodal Interfaces and the Workshop on Machine Learning for Multimodal Interaction

November 2010

311 pages

ISBN:9781450304146

DOI:10.1145/1891903

General Chairs:
Wen Gao
PKU, China
,
Chin-Hui Lee
Georgia Tech
,
Jie Yang
Carnegie Mellon
,
Program Chairs:
Xilin Chen
ICT, CAS, China
,
Maxine Eskenazi
Carnegie Mellon
,
Zhengyou Zhang
MSR

Permission to make digital or hard copies of all or part 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 components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

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Published: 08 November 2010

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ICMI-MLMI '10

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SIGCHI

ICMI-MLMI '10: INTERNATIONAL CONFERENCE ON MULTIMODAL INTERFACES/WORKSHOP ON MACHINE LEARNING FOR MULTIMODAL INTERFACES

November 8 - 10, 2010

Beijing, China

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ICMI-MLMI '10 Paper Acceptance Rate 41 of 100 submissions, 41%;

Overall Acceptance Rate 453 of 1,080 submissions, 42%

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Cited By

View all

Kosch TKarolus JZagermann JReiterer HSchmidt AWoźniak P(2023)A Survey on Measuring Cognitive Workload in Human-Computer InteractionACM Computing Surveys10.1145/358227255:13s(1-39)Online publication date: 13-Jul-2023
https://dl.acm.org/doi/10.1145/3582272
Chen RXie TXie YLin TTang NNakano YAndré ENishida TMorency LBusso CPelachaud C(2016)Do speech features for detecting cognitive load depend on specific languages?Proceedings of the 18th ACM International Conference on Multimodal Interaction10.1145/2993148.2993149(76-83)Online publication date: 31-Oct-2016
https://dl.acm.org/doi/10.1145/2993148.2993149
Lin TWu ZChen Y(2015)Using High-Frequency Interaction Events to Automatically Classify Cognitive LoadHuman Behavior, Psychology, and Social Interaction in the Digital Era10.4018/978-1-4666-8450-8.ch010(210-228)Online publication date: 2015
https://doi.org/10.4018/978-1-4666-8450-8.ch010
Lin TLi XWu ZTang N(2013)Automatic Cognitive Load Classification Using High-Frequency Interaction EventsInternational Journal of Technology and Human Interaction10.4018/jthi.20130701069:3(73-88)Online publication date: 1-Jul-2013
https://dl.acm.org/doi/10.4018/jthi.2013070106
Aslan IMurer MFuchsberger VFugard ATscheligi MQuigley AJacucci GHorn MNacenta M(2013)Workload on your fingertipsProceedings of the 2013 ACM international conference on Interactive tabletops and surfaces10.1145/2512349.2514918(417-420)Online publication date: 6-Oct-2013
https://dl.acm.org/doi/10.1145/2512349.2514918
Lin TXie TChen YTang N(2013)Automatic cognitive load evaluation using writing features: An exploratory studyInternational Journal of Industrial Ergonomics10.1016/j.ergon.2013.02.00243:3(210-217)Online publication date: May-2013
https://doi.org/10.1016/j.ergon.2013.02.002

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Cognitive load evaluation of handwriting using stroke-level features

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