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Educational Data Mining
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| Reihe | Studies in Computational Intelligence |
|---|---|
| Themen | Informatik und Informationstechnologie Informatik Künstliche Intelligenz (KI) |
| ISBN | 9783319027371 |
| Sprache | Englisch |
| Erscheinungsdatum | 20.11.2013 |
| Größe | 23.5 x 15.5 cm |
| Verlag | Springer International Publishing |
| Herausgegeben von | Alejandro Peña-Ayala |
| Lieferzeit | Lieferung in 7-14 Werktagen |
| Herstellerangaben | Anzeigen Springer Nature Customer Service Center GmbH Europaplatz 3 | DE-69115 Heidelberg ProductSafety@springernature.com |
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 knowledgeand find targets for future work in the field of educational data mining.
| Reihe | Studies in Computational Intelligence |
|---|---|
| Themen | Informatik und Informationstechnologie Informatik Künstliche Intelligenz (KI) |
| ISBN | 9783319027371 |
| Sprache | Englisch |
| Erscheinungsdatum | 20.11.2013 |
| Größe | 23.5 x 15.5 cm |
| Verlag | Springer International Publishing |
| Herausgegeben von | Alejandro Peña-Ayala |
| Lieferzeit | Lieferung in 7-14 Werktagen |
| Herstellerangaben | Anzeigen Springer Nature Customer Service Center GmbH Europaplatz 3 | DE-69115 Heidelberg ProductSafety@springernature.com |
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