Educational Data Mining (Record no. 54356)
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fixed length control field | 03662nam a22004575i 4500 |
001 - CONTROL NUMBER | |
control field | 978-3-319-02738-8 |
005 - DATE AND TIME OF LATEST TRANSACTION | |
control field | 20200421111650.0 |
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
fixed length control field | 131106s2014 gw | s |||| 0|eng d |
020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
ISBN | 9783319027388 |
-- | 978-3-319-02738-8 |
082 04 - CLASSIFICATION NUMBER | |
Call Number | 006.3 |
245 10 - TITLE STATEMENT | |
Title | Educational Data Mining |
Sub Title | Applications and Trends / |
300 ## - PHYSICAL DESCRIPTION | |
Number of Pages | XVIII, 468 p. 139 illus. |
490 1# - SERIES STATEMENT | |
Series statement | Studies in Computational Intelligence, |
520 ## - SUMMARY, ETC. | |
Summary, etc | 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 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. |
700 1# - AUTHOR 2 | |
Author 2 | Pe�na-Ayala, Alejandro. |
856 40 - ELECTRONIC LOCATION AND ACCESS | |
Uniform Resource Identifier | http://dx.doi.org/10.1007/978-3-319-02738-8 |
942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
Koha item type | eBooks |
264 #1 - | |
-- | Cham : |
-- | Springer International Publishing : |
-- | Imprint: Springer, |
-- | 2014. |
336 ## - | |
-- | text |
-- | txt |
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337 ## - | |
-- | computer |
-- | c |
-- | rdamedia |
338 ## - | |
-- | online resource |
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347 ## - | |
-- | text file |
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-- | rda |
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
-- | Engineering. |
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
-- | Artificial intelligence. |
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
-- | Computational intelligence. |
650 14 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
-- | Engineering. |
650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
-- | Computational Intelligence. |
650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
-- | Artificial Intelligence (incl. Robotics). |
830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE | |
-- | 1860-949X ; |
912 ## - | |
-- | ZDB-2-ENG |
No items available.