Study Navigator: An Algorithmically Generated Aid for Learning from Electronic Textbooks



Published Jul 12, 2014
Rakesh Agrawal Sreenivas Gollapudi Anitha Kannan Krishnaram Kenthapadi


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 an extension of the study navigator specialized to accommodate information processing preference of the student. Specifically, this specialization allows a student to control the balance between references to sections that help refresh material already studied vs. sections that provide more advanced information. We also present two user studies that demonstrate the e?cacy of the proposed system across textbooks on di?erent subjects from di?erent grades.

How to Cite

Agrawal, R., Gollapudi, S., Kannan, A., & Kenthapadi, K. (2014). Study Navigator: An Algorithmically Generated Aid for Learning from Electronic Textbooks. JEDM | Journal of Educational Data Mining, 6(1), 53-75. Retrieved from
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CK-12 Foundation.

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

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

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

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

R. Agrawal, S. Gollapudi, A. Kannan, and
K. Kenthapadi. Data mining for improving textbooks.
ACM SIGKDD Explorations Newsletter, 13(2), 2011.

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

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

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

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

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.

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

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

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

G. Jeh and J. Widom. Scaling personalized web
search. In WWW, 2003.

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

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

M. Meeker. Internet trends. Technical report, KPCB,

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

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

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

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.

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

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

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

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.

E. Trianta?llou, 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.