Educational Data Mining: Applications and Trends

ISBN-13: 9783319027371 / Angielski / Twarda / 2013 / 468 str.

Alejandro Pena-Ayala

Opis

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 student's 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.

Recenzje

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)

Spis treści

Part I: Profile

1 Which Contribution Does EDM Provide to Computer Based Learning Environments?

    Nabila Bousbia, Idriss Belamri

2 A Survey on Pre-processing Educational Data

    Cristóbal Romero, José Raúl Romero, Sebastián Ventura

3 How Educational Data Mining Empowers Government Policies to Re-form Education: The Mexican Case Study

    Alejandro Peña-Ayala, Leonor Cárdenas

 

Part II: Student Modeling

4 Modeling Student Performance in Higher Education Using Data Mining

    Huseyin Guruler, Ayhan Istanbullu

5 Using Data Mining Techniques to Detect the Personality of Players in an Educational Game

    Fazel Keshtkar, Candice Burkett, Haiying Li, Arthur C. Graesser

6 Students’ Performance Prediction using Multi-Channel Decision Fusion

    H. Moradi, S. Abbas Moradi, L. Kashani

7 Predicting Student Performance from Combined Data Sources

    Annika Wolff, Zdenek Zdrahal, Drahomira Herrmannova, Petr Knoth

8 Predicting Learner Answers Correctness Through Eye Movements With Random Forest

    Alper Bayazit, Petek Askar, Erdal Cosgun

 

Part III: Assessment

9 Mining Domain Knowledge for Coherence Assessment of Students Proposal Drafts

    Samuel González López, Aurelio López-López

10 Adaptive Testing in Programming Courses Based on Educational Data Mining Techniques

     Vladimir Ivančević, Marko Knežević, Bojan Pušić, Ivan Luković

11 Plan Recognition and Visualization in Exploratory Learning Environments

      Ofra Amir, Kobi Gal, David Yaron, Michael Karabinos, Robert Bel-ford

12 Dependency of Test Items from Students' Response Data

      Xiaoxun Sun

 

Part IV : Trends

13 Mining Texts, Learner Productions and Strategies with ReaderBench

      Mihai Dascalu, Philippe Dessus, Maryse Bianco, Stefan Trausan-Matu, Aurélie Nardy

14 Maximizing the Value of Student Ratings Through Data Mining

      Kathryn Gates, Dawn Wilkins, Sumali Conlon, Susan Mossing, Mau-rice Eftink

15 Data Mining and Social Network Analysis in the Educational Field: An Application for Non-expert Users

      Diego García-Saiz, Camilo Palazuelos, Marta Zorrilla

16 Collaborative Learning of Students in Online Discussion Forums: A Social Network Analysis Perspective

      Reihaneh Rabbany, Samira ElAtia, Mansoureh Takaffoli, Osmar R. Zaïane