Educational Data Mining: Applications and Trends

Front Cover
Alejandro Peņa-Ayala
Springer, Nov 8, 2013 - Technology & Engineering - 468 pages

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.

 

What people are saying - Write a review

We haven't found any reviews in the usual places.

Contents

1 Which Contribution Does EDM Provide to ComputerBased Learning Environments?
3
2 A Survey on PreProcessing Educational Data
29
The Mexican Case Study
65
Part II Student Modeling
102
4 Modeling Student Performance in Higher Education Using Data Mining
105
5 Using Data Mining Techniques to Detect the Personality of Players in an Educational Game
125
6 Students Performance Prediction Using MultiChannel Decision Fusion
151
7 Predicting Student Performance from Combined Data Sources
175
10 Adaptive Testing in Programming Courses Based on Educational Data Mining Techniques
257
11 Plan Recognition and Visualization in Exploratory Learning Environments
288
12 Finding Dependency of Test Items from Students Response Data
329
Part IV Trends
343
13 Mining Texts Learner Productions and Strategies with ReaderBench
344
14 Maximizing the Value of Student Ratings Through Data Mining
379
An Application for NonExpert Users
411
A Social Network Analysis Perspective
440

8 Predicting Learner Answers Correctness Through Eye Movements with Random Forest
203
Part III Assessment
228
9 Mining Domain Knowledge for Coherence Assessment of Students Proposal Drafts
229

Other editions - View all

Common terms and phrases

Bibliographic information