skip to main content
10.1145/3384419.3430428acmconferencesArticle/Chapter ViewAbstractPublication PagessensysConference Proceedingsconference-collections
short-paper

Towards recognizing perceived level of understanding for online lectures using earables: poster abstract

Published:16 November 2020Publication History

ABSTRACT

We envision that our earbuds recognize how much we understand learning materials while taking online lectures for effective learning and teaching, e.g., to pinpoint the part for which we need to put more effort to learn. To this end, we explore the feasibility of recognizing the perceived level of understanding of online learners based on IMU sensor data from earbuds. We present an exploratory study to identify head-related behaviors that can be detected by in-ear IMU data, which are associated with the perceived level of understanding for online lectures.

References

  1. Davide Figo, Pedro C Diniz, Diogo R Ferreira, and Joao MP Cardoso. 2010. Preprocessing techniques for context recognition from accelerometer data. Personal and Ubiquitous Computing 14, 7 (2010), 645--662.Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Fahim Kawsar, Chulhong Min, Akhil Mathur, and Alesandro Montanari. 2018. Earables for personal-scale behavior analytics. IEEE Pervasive Computing 17, 3 (2018), 83--89.Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. David R Krathwohl. 2002. A revision of Bloom's taxonomy: An overview. Theory into practice 41, 4 (2002), 212--218.Google ScholarGoogle Scholar

Index Terms

  1. Towards recognizing perceived level of understanding for online lectures using earables: poster abstract

      Recommendations

      Comments

      Login options

      Check if you have access through your login credentials or your institution to get full access on this article.

      Sign in
      • Published in

        cover image ACM Conferences
        SenSys '20: Proceedings of the 18th Conference on Embedded Networked Sensor Systems
        November 2020
        852 pages
        ISBN:9781450375900
        DOI:10.1145/3384419

        Copyright © 2020 ACM

        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]

        Publisher

        Association for Computing Machinery

        New York, NY, United States

        Publication History

        • Published: 16 November 2020

        Permissions

        Request permissions about this article.

        Request Permissions

        Check for updates

        Qualifiers

        • short-paper

        Acceptance Rates

        Overall Acceptance Rate174of867submissions,20%

      PDF Format

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader