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Educational Data Mining

Overview of attention for book
Educational Data Mining
Springer International Publishing

Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    Chapter 1 Which Contribution Does EDM Provide to Computer-Based Learning Environments?
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    Chapter 2 A Survey on Pre-Processing Educational Data
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    Chapter 3 How Educational Data Mining Empowers State Policies to Reform Education: The Mexican Case Study
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    Chapter 4 Modeling Student Performance in Higher Education Using Data Mining
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    Chapter 5 Using Data Mining Techniques to Detect the Personality of Players in an Educational Game
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    Chapter 6 Students’ Performance Prediction Using Multi-Channel Decision Fusion
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    Chapter 7 Predicting Student Performance from Combined Data Sources
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    Chapter 8 Predicting Learner Answers Correctness Through Eye Movements with Random Forest
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    Chapter 9 Mining Domain Knowledge for Coherence Assessment of Students Proposal Drafts
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    Chapter 10 Adaptive Testing in Programming Courses Based on Educational Data Mining Techniques
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    Chapter 11 Educational Data Mining
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    Chapter 12 Finding Dependency of Test Items from Students’ Response Data
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    Chapter 13 Mining Texts, Learner Productions and Strategies with ReaderBench
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    Chapter 14 Maximizing the Value of Student Ratings Through Data Mining
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    Chapter 15 Data Mining and Social Network Analysis in the Educational Field: An Application for Non-Expert Users
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    Chapter 16 Collaborative Learning of Students in Online Discussion Forums: A Social Network Analysis Perspective
Attention for Chapter 1: Which Contribution Does EDM Provide to Computer-Based Learning Environments?
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Readers on

mendeley
55 Mendeley
Summary
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Chapter title
Which Contribution Does EDM Provide to Computer-Based Learning Environments?
Chapter number 1
Book title
Educational Data Mining
Published by
Springer, Cham, January 2014
DOI 10.1007/978-3-319-02738-8_1
Book ISBNs
978-3-31-902737-1, 978-3-31-902738-8
Authors

Nabila Bousbia, Idriss Belamri, Bousbia, Nabila, Belamri, Idriss

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 55 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 55 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 12 22%
Student > Master 9 16%
Researcher 4 7%
Lecturer 4 7%
Student > Postgraduate 3 5%
Other 10 18%
Unknown 13 24%
Readers by discipline Count As %
Computer Science 26 47%
Engineering 4 7%
Unspecified 2 4%
Business, Management and Accounting 1 2%
Chemical Engineering 1 2%
Other 5 9%
Unknown 16 29%

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