|About the 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 students 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.