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 the 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 efficacy of the proposed system across textbooks on different subjects from different grades.

How to Cite

Agrawal, R., Gollapudi, S., Kannan, A., & Kenthapadi, K. (2014). Study Navigator: An Algorithmically Generated Aid for Learning from Electronic Textbooks. Journal of Educational Data Mining, 6(1), 53–75.
Abstract 691 | PDF Downloads 310



electronic textbooks, reader model for textbooks, significance score, concept references

AGRAWAL, R., GOLLAPUDI, S., KANNAN, A., AND KENTHAPADI, K. 2011. Data mining for improving textbooks. ACM SIGKDD Explorations Newsletter 13, 2. AMAZON. 2011. Amazon Mechanical Turk, Requester Best Practices Guide. Amazon Web Services.

ANDERSON, J. R. 1982. Acquisition of cognitive skill. Psychological review 89, 4.

BAKEWELL, K. 1993. Research in indexing: More needed? Indexer 18, 3.

BAREISS, R. AND OSGOOD, R. 1993. Applying AI models to the design of exploratory hypermedia systems. In ACM Conference on Hypertext.

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

BRUSILOVSKY, P. 2001. Adaptive hypermedia. User modeling and user-adapted interaction 11, 1-2.

BRUSILOVSKY, P., EKLUND, J., AND SCHWARZ, E. 1998. Web-based education for all: A tool for development adaptive courseware. In WWW.

BRUSILOVSKY, P., SCHWARZ, E., AND WEBER, G. 1996. ELM-ART: An intelligent tutoring system on World Wide Web. In ITS. CDE. 2012. California Education Technology Task Force Recommendations. California Department of Education.

CHI, E. H., HONG, L., GUMBRECHT, M., AND CARD, S. K. 2005. ScentHighlights: Highlighting conceptually-related sentences during reading. In IUI.

CHI, E. H., HONG, L., HEISER, J., AND CARD, S. K. 2006. ScentIndex: Conceptually reorganizing subject indexes for reading. In IEEE Symposium On Visual Analytics Science And Technology. CK-12. CK-12 Foundation.

CLEARY, C. AND BAREISS, R. 1996. Practical methods for automatically generating typed links. In ACM Conference on Hypertext.

FELLBAUM, C. 1998. WordNet: An electronic lexical database. MIT Press.

FIDEL, R. 1994. User-centered indexing. Journal of the American Society for Information Science 45, 8.

FORTUNATO, S., BOGU˜N´A, M., FLAMMINI, A., AND MENCZER, F. 2008. Approximating pagerank from in-degree. Algorithms and Models for the Web-Graph, LNCS 4936.

HENZE, N. AND NEJDL, W. 2001. Adaptation in open corpus hypermedia. International Journal of Artificial Intelligence in Education 12, 4. IMHRD. 2012. Report on Aakash tablet. Indian Ministry of Human Resource Development.

JEH, G. AND WIDOM, J. 2003. Scaling personalized web search. In WWW.

JURAFSKY, D. AND MARTIN, J. 2008. Speech and language processing. Prentice Hall.

JUSTESON, J. S. AND KATZ, S. M. 1995. Technical terminology: Some linguistic properties and an algorithm for indentification in text. Natural Language Engineering 1, 1.

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

MEEKER, M. 2012. Internet trends. Tech. rep., KPCB.

MOTWANI, R. AND RAGHAVAN, P. 1995. Randomized Algorithms. Cambridge University Press.

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

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

PAPANIKOLAOU, K. A., GRIGORIADOU, M., KORNILAKIS, H., AND MAGOULAS, G. D. 2003. Personalizing the interaction in a web-based educational hypermedia system: The case of INSPIRE. User Modeling and User-Adapted Interaction 13, 3.

REMDE, J. R., GOMEZ, L. M., AND LANDAUER, T. K. 1987. SuperBook: An automatic tool for information exploration – hypertext? In ACM Conference on Hypertext.

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

SALTON, G., ALLAN, J., BUCKLEY, C., AND SINGHAL, A. 1994. Automatic analysis, theme generation, and summarization of machine-readable texts. Science 264, 5164.

THAYER, A., LEE, C. P., HWANG, L. H., SALES, H., SEN, P., AND DALAL, N. 2011. The imposition and superimposition of digital reading technology: The academic potential of e-readers. In CHI.

TOUTANOVA, K., KLEIN, D., MANNING, C. D., AND SINGER, Y. 2003. Feature-rich part-of-speech tagging with a cyclic dependency network. In NAACL–HLT.

TRIANTAFILLOU, E., POMPORTSIS, A., DEMETRIADIS, S., AND GEORGIADOU, E. 2004. The value of adaptivity based on cognitive style: an empirical study. British Journal of Educational Technology 35, 1.

WANG, K., THRASHER, C., VIEGAS, E., LI, X., AND HSU, P. 2010. An overview of Microsoft Web N-gram corpus and applications. In NAACL–HLT.

WEBER, G. AND SPECHT, M. 1997. User modeling and adaptive navigation support in WWW-based tutoring systems. In UM.