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Let's learn!: enhancing user's engagement levels through passive brain-computer interfaces

Published: 27 April 2013 Publication History

Abstract

This work in progress paper describes a proof-of-concept that explores the question: "Can users retain more information by incrementing their reading engagement physiologically?" This may help users to better learn the material even when it is uninteresting. Further, this paper explains how using Brain-Computer Interfaces can be used to measure/capture the engagement levels of a user while he or she is performing a task, in this case reading. Also this explores, how beneficial it may be for reading engagement physiologically to deliver the best reading experience.

References

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Andujar, M., Ekandem, J., Alvarez, I., James, M., & Gilbert, J. (2011). Are Educational Video Games All They're Cracked Up to Be?: A Physiological Approach for Measuring Engagement in Educational Video Games vs. Conventional Learning Techniques. In Proceedings of World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education 2011 (pp. 539--544). Chesapeake, VA: AACE.
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Szafir, D., and Mutlu, B. 2012. Pay Attention! Designing Adaptive Agents that Monitor and Improve User Engagement. Proceedings of the 2012 ACM Annual Conference on Human Factors in Computing Systems (Austin, Texas, USA, May 5--10, 2012). CHI'12. New York, NY, 11--20.
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      cover image ACM Conferences
      CHI EA '13: CHI '13 Extended Abstracts on Human Factors in Computing Systems
      April 2013
      3360 pages
      ISBN:9781450319522
      DOI:10.1145/2468356
      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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      Published: 27 April 2013

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      Author Tags

      1. brain-computer interface
      2. engagement
      3. learning
      4. passive bci

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      CHI EA '13 Paper Acceptance Rate 630 of 1,963 submissions, 32%;
      Overall Acceptance Rate 6,164 of 23,696 submissions, 26%

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      • (2024)Novel Insights into Elementary Girls' Experiences in Physiological ComputingProceedings of the 55th ACM Technical Symposium on Computer Science Education V. 110.1145/3626252.3630900(764-770)Online publication date: 7-Mar-2024
      • (2024)Exploring Computational Thinking Perspectives in Black Communities with Physiological Computing2024 Black Issues in Computing Education (BICE)10.1109/BICE60192.2024.00013(27-32)Online publication date: 1-Feb-2024
      • (2024)Engaging students of color in physiological computing with insights from eye-trackingJournal of Research on Technology in Education10.1080/15391523.2024.2381226(1-22)Online publication date: 22-Jul-2024
      • (2024)Towards Neuro-Enhanced Education: A Systematic Review of BCI-Assisted Development for Non-academic Skills and AbilitiesGenerative Intelligence and Intelligent Tutoring Systems10.1007/978-3-031-63031-6_5(49-66)Online publication date: 1-Jun-2024
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      • (2022)Target Speaker Detection with EEG/EOG Glasses: A Pilot StudyProceedings of the Future Technologies Conference (FTC) 2022, Volume 210.1007/978-3-031-18458-1_30(433-446)Online publication date: 13-Oct-2022
      • (2022)Electroencephalography and Self-assessment Evaluation of Engagement with Online Exhibitions: Case Study of Google Arts and CultureCulture and Computing10.1007/978-3-031-05434-1_21(316-331)Online publication date: 26-Jun-2022
      • (2021)A Literature Review of Physiological-Based Mobile Educational SystemsIEEE Transactions on Learning Technologies10.1109/TLT.2021.309831514:3(272-291)Online publication date: 1-Jun-2021
      • (2021)EEG Correlates of Difficulty Levels in Dynamical Transitions of Simulated Flying and Mapping TasksIEEE Transactions on Human-Machine Systems10.1109/THMS.2020.303833951:2(99-108)Online publication date: Apr-2021
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