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

Authors: Agrawal, Rakesh; Gollapudi, Sreenivas; Kannan, Anitha; Kenthapadi, Krishnaram;

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

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 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.

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Keywords

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

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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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