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. "
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