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

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.

[1]  George D. Magoulas,et al.  Personalizing the Interaction in a Web-based Educational Hypermedia System: the case of INSPIRE , 2003, User Modeling and User-Adapted Interaction.

[2]  Gerhard Weber,et al.  User Modeling and Adaptive Navigation Support in WWW-Based Tutoring Systems , 1997 .

[3]  Marián Boguñá,et al.  Approximating PageRank from In-Degree , 2007, WAW.

[4]  Raya Fidel.  User-centered indexing , 1994 .

[5]  D. Saari.  Decisions and elections : explaining the unexpected , 2001 .

[6]  佐藤 順一,et al.  Ex ante and Ex post , 2013 .

[7]  Ray Bareiss,et al.  Applying AI models to the design of exploratory hypermedia systems , 1993, Hypertext.

[8]  K. Bakewell.  Research in indexing: more needed? , 1993 .

[9]  Slava M. Katz,et al.  Technical terminology: some linguistic properties and an algorithm for identification in text , 1995, Natural Language Engineering.

[10]  John R. Anderson.  Acquisition of cognitive skill. , 1982 .

[11]  P. Zimmermann.  Automatic analysis , 2000 .

[12]  Xiaolong Li,et al.  An Overview of Microsoft Web N-gram Corpus and Applications , 2010, NAACL.

[13]  Jennifer Widom,et al.  Scaling personalized web search , 2003, WWW '03.

[14]  Xingyun Peng.  Demand for Money , 2015 .

[15]  Declan Kelly,et al.  Adaptive versus Learner Control in a Multiple Intelligence Learning Environment , 2008 .

[16]  Christiane Fellbaum,et al.  Book Reviews: WordNet: An Electronic Lexical Database , 1999, CL.

[17]  Sreenivas Gollapudi,et al.  Empowering authors to diagnose comprehension burden in textbooks , 2012, KDD.

[18]  Ed H. Chi,et al.  ScentHighlights: highlighting conceptually-related sentences during reading , 2005, IUI.

[19]  Nancy C. Mulvany,et al.  Indexing Books , 1994 .

[20]  James H. Martin,et al.  Speech and language processing: an introduction to natural language processing, computational linguistics, and speech recognition, 2nd Edition , 2000, Prentice Hall series in artificial intelligence.

[21]  Peter Brusilovsky,et al.  ELM-ART: An Intelligent Tutoring System on World Wide Web , 1996, Intelligent Tutoring Systems.

[22]  Sven Birkerts,et al.  The Gutenberg Elegies: The Fate of Reading in an Electronic Age , 1994 .

[23]  Charlotte P. Lee,et al.  The imposition and superimposition of digital reading technology: the academic potential of e-readers , 2011, CHI.

[24]  David F. Gleich,et al.  Algorithms and Models for the Web Graph , 2014, Lecture Notes in Computer Science.

[25]  Peter Brusilovsky,et al.  Adaptive Hypermedia , 2001, User Modeling and User-Adapted Interaction.

[26]  Sreenivas Gollapudi,et al.  Data mining for improving textbooks , 2012, SKDD.

[27]  W. Ong,et al.  Orality and literacy : the technologizing of the word , 1982 .

[28]  Raya Fidel,et al.  User-Centered Indexing , 1994, J. Am. Soc. Inf. Sci..

[29]  Louis M. Gomez,et al.  SuperBook: an automatic tool for information exploration—hypertext? , 1987, Hypertext.

[30]  Sun Guofeng,et al.  Foreign Exchange Market , 2015 .

[31]  Dan Klein,et al.  Feature-Rich Part-of-Speech Tagging with a Cyclic Dependency Network , 2003, NAACL.

[32]  Peter Brusilovsky,et al.  Web-Based Education for All: A Tool for Development Adaptive Courseware , 1998, Comput. Networks.

[33]  Wolfgang Nejdl,et al.  Adaptation in Open Corpus Hypermedia , 2001 .

[34]  Russ Bubley,et al.  Randomized algorithms , 1995, CSUR.

[35]  G Salton,et al.  Automatic Analysis, Theme Generation, and Summarization of Machine-Readable Texts , 1994, Science.

[36]  Alfred Kobsa.  User Modeling and User-Adapted Interaction , 2005, User Modeling and User-Adapted Interaction.

[37]  Ed H. Chi,et al.  Scentindex: Conceptually Reorganizing Subject Indexes for Reading , 2006, 2006 IEEE Symposium On Visual Analytics Science And Technology.

[38]  Ray Bareiss,et al.  Practical methods for automatically generating typed links , 1996, HYPERTEXT '96.

[39]  Andreas S. Pomportsis,et al.  The value of adaptivity based on cognitive style: an empirical study , 2004, Br. J. Educ. Technol..