research-article

Studying from electronic textbooks

Authors:

Rakesh Agrawal,

Sreenivas Gollapudi,

Krishnaram KenthapadiAuthors Info & Claims

CIKM '13: Proceedings of the 22nd ACM international conference on Information & Knowledge Management

Pages 1715 - 1720

https://doi.org/10.1145/2505515.2505604

Published: 27 October 2013 Publication History

Abstract

We present study navigator, an algorithmically-generated aid for enhancing the experience of studying from electronic textbooks. The study navigator for a section of the book consists of helpful concept references for understanding this section. Each concept reference is a pair consisting of a concept phrase explained elsewhere and the link to the section in which it has been explained. We propose a novel reader model for textbooks and an algorithm for generating the study navigator based on this model. We also present the results of an extensive user study that demonstrates the efficacy of the proposed system across textbooks on different subjects from different grades.

References

[1]

Amazon Mechanical Turk, Requester Best Practices Guide. Amazon Web Services, June 2011.

[2]

California Education Technology Task Force Recommendations. California Department of Education, 2012.

[3]

Report on Aakash tablet. Indian Ministry of Human Resource Development, 2012.

[4]

R. Agrawal, S. Chakraborty, S. Gollapudi, A. Kannan, and K. Kenthapadi. Empowering authors to diagnose comprehension burden in textbooks. In KDD, 2012.

Digital Library

[5]

R. Agrawal, S. Gollapudi, A. Kannan, and K. Kenthapadi. Study navigator: An algorithmically generated aid for learning from electronic textbooks. Technical Report MSR-TR-2013--68, Microsoft Research, 2013.

[6]

R. Bareiss and R. Osgood. Applying AI models to the design of exploratory hypermedia systems. In ACM Conference on Hypertext, 1993.

Digital Library

[7]

S. Birkerts. The Gutenberg Elegies: The Fate of Reading in an Electronic Age. Faber & Faber, 2006.

[8]

P. Brusilovsky, J. Eklund, and E. Schwarz. Web-based education for all: A tool for development adaptive courseware. In WWW, 1998.

Digital Library

[9]

E. H. Chi, L. Hong, M. Gumbrecht, and S. K. Card. ScentHighlights: Highlighting conceptually-related sentences during reading. In IUI, 2005.

Digital Library

[10]

E. H. Chi, L. Hong, J. Heiser, and S. K. Card. ScentIndex: Conceptually reorganizing subject indexes for reading. In IEEE Symposium On Visual Analytics Science And Technology, 2006.

[11]

C. Cleary and R. Bareiss. Practical methods for automatically generating typed links. In ACM Conference on Hypertext, 1996.

Digital Library

[12]

S. Fortunato, M. Boguñá, A. Flammini, and F. Menczer. Approximating pagerank from in-degree. Algorithms and Models for the Web-Graph, LNCS 4936, 2008.

Digital Library

[13]

N. Henze and W. Nejdl. Adaptation in open corpus hypermedia. International Journal of Artificial Intelligence in Education, 12(4), 2001.

[14]

D. Jurafsky and J. Martin. Speech and language processing. Prentice Hall, 2008.

Digital Library

[15]

D. Kelly. Adaptive versus learner control in a multiple intelligence learning environment. Journal of Educational Multimedia and Hypermedia, 17(3), 2008.

[16]

R. Motwani and P. Raghavan. Randomized Algorithms. Cambridge University Press, 1995.

Digital Library

[17]

N. Mulvany. Indexing books. University of Chicago Press, 2005.

[18]

W. J. Ong. Orality & Literacy: The Technologizing of the Word. Methuen, 1982.

[19]

K. A. Papanikolaou, M. Grigoriadou, H. Kornilakis, and G. D. Magoulas. Personalizing the interaction in a web-based educational hypermedia system: The case of INSPIRE. User Modeling and User-Adapted Interaction, 13(3), 2003.

Digital Library

[20]

J. R. Remde, L. M. Gomez, and T. K. Landauer. SuperBook: An automatic tool for information exploration -- hypertext. In ACM Conference on Hypertext, 1987.

Digital Library

[21]

D. Saari. Decisions and elections: Explaining the unexpected. Cambridge University Press, 2001.

[22]

G. Salton, J. Allan, C. Buckley, and A. Singhal. Automatic analysis, theme generation, and summarization of machine-readable texts. Science, 264(5164), 1994.

[23]

A. Thayer, C. P. Lee, L. H. Hwang, H. Sales, P. Sen, and N. Dalal. The imposition and superimposition of digital reading technology: The academic potential of e-readers. In CHI, 2011.

Digital Library

[24]

E. Triantafillou, A. Pomportsis, S. Demetriadis, and E. Georgiadou. The value of adaptivity based on cognitive style: an empirical study. British Journal of Educational Technology, 35(1), 2004.

Cited By

Kushwaha R(2023)Big graph based online learning through social networksPrinciples of Big Graph: In-depth Insight10.1016/bs.adcom.2021.10.012(313-328)Online publication date: 2023
https://doi.org/10.1016/bs.adcom.2021.10.012
Lu WMa PYu JZhou YWei B(2019)Metro maps for efficient knowledge learning by summarizing massive electronic textbooksInternational Journal on Document Analysis and Recognition10.1007/s10032-019-00319-y22:2(99-111)Online publication date: 1-Jun-2019
https://dl.acm.org/doi/10.1007/s10032-019-00319-y
Mariooryad SKannan AHakkani-Tur DShriberg E(2014)Automatic characterization of speaking styles in educational videos2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)10.1109/ICASSP.2014.6854523(4848-4852)Online publication date: May-2014
https://doi.org/10.1109/ICASSP.2014.6854523

Index Terms

Studying from electronic textbooks
1. Information systems
  1. Information retrieval

Recommendations

Empowering authors to diagnose comprehension burden in textbooks
KDD '12: Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining

Good textbooks are organized in a systematically progressive fashion so that students acquire new knowledge and learn new concepts based on known items of information. We provide a diagnostic tool for quantitatively assessing the comprehension burden ...
Students' expectation, confirmation, and continuance intention to use electronic textbooks

Technology applied to education has produced numerous changes. One of these changes is the availability of electronic textbooks. However, the adoption of electronic textbooks has been slower than anticipated. This research examines adoption of ...
On Automated Lesson Construction from Electronic Textbooks

Abstract--An electronic book may be viewed as an application with a multimedia database. We define an electronic textbook as an electronic book that is used in conjunction with instructional resources such as lectures. In this paper, we propose an ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Conferences

CIKM '13: Proceedings of the 22nd ACM international conference on Information & Knowledge Management

October 2013

2612 pages

ISBN:9781450322638

DOI:10.1145/2505515

General Chairs:
Qi He
LinkedIn, USA
,
Arun Iyengar
IBM T.J. Watson Research Center, USA
,
Program Chairs:
Wolfgang Nejdl
L3S Research Center, Germany
,
Jian Pei
Simon Fraser University, Canada
,
Rajeev Rastogi
Amazon, India

Copyright © 2013 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]

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 27 October 2013

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

CIKM'13

Sponsor:

CIKM'13: 22nd ACM International Conference on Information and Knowledge Management

October 27 - November 1, 2013

California, San Francisco, USA

Acceptance Rates

CIKM '13 Paper Acceptance Rate 143 of 848 submissions, 17%;

Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

Upcoming Conference

CIKM '25

Sponsor:
sigir
sigir

The 34th ACM International Conference on Information and Knowledge Management

November 10 - 14, 2025

Seoul , Republic of Korea

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

3
Total Citations
View Citations
264
Total Downloads

Downloads (Last 12 months)1
Downloads (Last 6 weeks)1

Reflects downloads up to 16 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

Kushwaha R(2023)Big graph based online learning through social networksPrinciples of Big Graph: In-depth Insight10.1016/bs.adcom.2021.10.012(313-328)Online publication date: 2023
https://doi.org/10.1016/bs.adcom.2021.10.012
Lu WMa PYu JZhou YWei B(2019)Metro maps for efficient knowledge learning by summarizing massive electronic textbooksInternational Journal on Document Analysis and Recognition10.1007/s10032-019-00319-y22:2(99-111)Online publication date: 1-Jun-2019
https://dl.acm.org/doi/10.1007/s10032-019-00319-y
Mariooryad SKannan AHakkani-Tur DShriberg E(2014)Automatic characterization of speaking styles in educational videos2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)10.1109/ICASSP.2014.6854523(4848-4852)Online publication date: May-2014
https://doi.org/10.1109/ICASSP.2014.6854523

View Options

Login options

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

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents