© 2014

Educational Data Mining

Applications and Trends

  • Alejandro Peña-Ayala

Part of the Studies in Computational Intelligence book series (SCI, volume 524)

Table of contents

  1. Front Matter
    Pages i-xviii
  2. Profile

    1. Front Matter
      Pages 1-1
    2. Cristóbal Romero, José Raúl Romero, Sebastián Ventura
      Pages 29-64
  3. Student Modeling

    1. Front Matter
      Pages 103-103
    2. Huseyin Guruler, Ayhan Istanbullu
      Pages 105-124
    3. Fazel Keshtkar, Candice Burkett, Haiying Li, Arthur C. Graesser
      Pages 125-150
    4. H. Moradi, S. Abbas Moradi, L. Kashani
      Pages 151-174
    5. Annika Wolff, Zdenek Zdrahal, Drahomira Herrmannova, Petr Knoth
      Pages 175-202
    6. Alper Bayazit, Petek Askar, Erdal Cosgun
      Pages 203-226
  4. Assessment

    1. Front Matter
      Pages 227-227
    2. Samuel González López, Aurelio López-López
      Pages 229-255
    3. Vladimir Ivančević, Marko Knežević, Bojan Pušić, Ivan Luković
      Pages 257-287
    4. Ofra Amir, Kobi Gal, David Yaron, Michael Karabinos, Robert Belford
      Pages 289-327
  5. Trends

    1. Front Matter
      Pages 343-343
    2. Mihai Dascalu, Philippe Dessus, Maryse Bianco, Stefan Trausan-Matu, Aurélie Nardy
      Pages 345-377
    3. Kathryn Gates, Dawn Wilkins, Sumali Conlon, Susan Mossing, Maurice Eftink
      Pages 379-410
    4. Diego García-Saiz, Camilo Palazuelos, Marta Zorrilla
      Pages 411-439

About this book


This book is devoted to the Educational Data Mining arena. It highlights works that show relevant proposals, developments, and achievements that shape trends and inspire future research.  After a rigorous revision process sixteen manuscripts were accepted and organized into four parts as follows:

·     Profile: The first part embraces three chapters oriented to: 1) describe the nature of educational data mining (EDM); 2) describe how to pre-process raw data to facilitate data mining (DM); 3) explain how EDM supports government policies to enhance education.

·     Student modeling: The second part contains five chapters concerned with: 4) explore the factors having an impact on the students academic success; 5) detect student's personality and behaviors in an educational game; 6) predict students performance to adjust content and strategies; 7) identify students who will most benefit from tutor support; 8) hypothesize the student answer correctness based on eye metrics and mouse click.

·     Assessment: The third part has four chapters related to: 9) analyze the coherence of student research proposals; 10) automatically generate tests based on competences; 11) recognize students activities and visualize these activities for being presented to teachers; 12) find the most dependent test items in students response data.

·     Trends: The fourth part encompasses four chapters about how to: 13) mine text for assessing students productions and supporting teachers; 14) scan student comments by statistical and text mining techniques; 15) sketch a social network analysis (SNA) to discover student behavior profiles and depict models about their collaboration; 16) evaluate the structure of interactions between the students in social networks.

This volume will be a source of interest to researchers, practitioners, professors, and postgraduate students aimed at updating their knowledge and find targets for future work in the field of educational data mining.


Computational Intelligence Data Mining EDM Applications EDM Methods EDM Models EDM Tasks Educational Data Mining

Editors and affiliations

  • Alejandro Peña-Ayala
    • 1
  1. 1.Escuela Superior de Ingeniería Mecánica y Eléctrica, Zacatenco (ESIME-Z)World Outreach Light to the Nations Ministries (WOLNM), Instituto Politécnico Nacional (IPN)Gustavo A. Madero, Mexico CityMexico

Bibliographic information

  • Book Title Educational Data Mining
  • Book Subtitle Applications and Trends
  • Editors Alejandro Peña-Ayala
  • Series Title Studies in Computational Intelligence
  • Series Abbreviated Title Studies Comp.Intelligence
  • DOI
  • Copyright Information Springer International Publishing Switzerland 2014
  • Publisher Name Springer, Cham
  • eBook Packages Engineering Engineering (R0)
  • Hardcover ISBN 978-3-319-02737-1
  • Softcover ISBN 978-3-319-34499-7
  • eBook ISBN 978-3-319-02738-8
  • Series ISSN 1860-949X
  • Series E-ISSN 1860-9503
  • Edition Number 1
  • Number of Pages XVIII, 468
  • Number of Illustrations 139 b/w illustrations, 0 illustrations in colour
  • Topics Computational Intelligence
    Artificial Intelligence
  • Buy this book on publisher's site
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From the book reviews:

“This book delivers on its promise to bring together the essence of educational data mining, both in terms of principle and practice. It deserves a place on the reading shelf of any researcher interested in advancing educational goals using advanced techniques and methodologies.” (Computing Reviews, July, 2014)