| 000 | 03357Cam#a22003855i#4500 | ||
|---|---|---|---|
| 001 | INGC-EBK-000308 | ||
| 003 | AR-LpUFI | ||
| 005 | 20220927105758.0 | ||
| 007 | cr nn 008mamaa | ||
| 008 | 131106s2014 gw | s |||| 0|eng d | ||
| 020 | _a9783319027388 | ||
| 024 | 7 |
_a10.1007/978-3-319-02738-8 _2doi |
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| 050 | 4 | _aQ342 | |
| 072 | 7 |
_aUYQ _2bicssc |
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| 072 | 7 |
_aCOM004000 _2bisacsh |
|
| 245 | 1 | 0 |
_aEducational Data Mining _h[libro electrónico] : ; _bApplications and Trends / _cedited by Alejandro Peña-Ayala. |
| 260 | 1 |
_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2014. |
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| 300 |
_axviii, 468 p. : _bil. |
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| 336 |
_atext _btxt _2rdacontent |
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| 337 |
_acomputer _bc _2rdamedia |
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| 338 |
_aonline resource _bcr _2rdacarrier |
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| 347 |
_atext file _bPDF _2rda |
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| 490 | 1 |
_aStudies in Computational Intelligence, _x1860-949X ; _v524 |
|
| 520 | _aThis 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. | ||
| 650 | 1 | 4 |
_aEngineering. _9259622 |
| 650 | 2 | 4 |
_aComputational Intelligence. _9259845 |
| 650 | 2 | 4 |
_aArtificial Intelligence (incl. Robotics). _9259846 |
| 700 | 1 |
_aPeña-Ayala, Alejandro, _eed. _9260731 |
|
| 776 | 0 | 8 |
_iPrinted edition: _z9783319027371 |
| 856 | 4 | 0 | _uhttp://dx.doi.org/10.1007/978-3-319-02738-8 |
| 912 | _aZDB-2-ENG | ||
| 929 | _aCOM | ||
| 942 | _cEBK | ||
| 999 |
_aSKV _c27736 _d27736 |
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