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商品詳細
Educational Data Mining: Applications and Trends. Softcover reprint of the original 1st ed. 2014
・ISBN 978-3-319-34499-7 paper EUR 149.99
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| 著者・編者 | Pena-Ayala, Alejandro (ed.), |
|---|---|
| シリーズ | (Studies in Computational Intelligence) |
| 出版社 | (Springer International Publishing AG, SZ) |
| 出版年月 | 2016 |
| ページ数 | 468 pp. |
| 言語 | ENG |
| ニュース番号 | ＜A05-16777＞ |
解説
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.