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020 _a9783662559932
_9978-3-662-55993-2
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100 1 _aKosheleva, Olga.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
245 1 0 _aHow Interval and Fuzzy Techniques Can Improve Teaching
_h[electronic resource] :
_bProcessing Educational Data: From Traditional Statistical Techniques to an Appropriate Combination of Probabilistic, Interval, and Fuzzy Approaches /
_cby Olga Kosheleva, Karen Villaverde.
250 _a1st ed. 2018.
264 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg :
_bImprint: Springer,
_c2018.
300 _aX, 362 p. 1 illus.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aStudies in Computational Intelligence,
_x1860-949X ;
_v750
500 _aAcceso multiusuario
520 _aThis book explains how to teach better and presents the latest research on processing educational data and presents traditional statistical techniques as well as probabilistic, interval, and fuzzy approaches. Teaching is a very rewarding activity; it is also a very difficult one - because it is largely an art. There is a lot of advice on teaching available, but it is usually informal and is not easy to follow. To remedy this situation, it is reasonable to use techniques specifically designed to handle such imprecise knowledge: the fuzzy logic techniques. Since there are a large number of statistical studies of different teaching techniques, the authors combined statistical and fuzzy approaches to process the educational data in order to provide insights into improving all the stages of the education process: from forming a curriculum to deciding in which order to present the material to grading the assignments and exams. The authors do not claim to have solved all the problems of education. Instead they show, using numerous examples, that an innovative combination of different uncertainty techniques can improve teaching. The book offers teachers and instructors valuable advice and provides researchers in pedagogical and fuzzy areas with techniques to further advance teaching.
541 _fUABC ;
_cTemporal ;
_d01/01/2021-12/31/2023.
650 0 _aComputational intelligence.
650 0 _aInformation technology.
650 0 _aBusiness-Data processing.
650 0 _aE-commerce.
650 1 4 _aComputational Intelligence.
_0https://scigraph.springernature.com/ontologies/product-market-codes/T11014
650 2 4 _aIT in Business.
_0https://scigraph.springernature.com/ontologies/product-market-codes/522000
650 2 4 _ae-Commerce/e-business.
_0https://scigraph.springernature.com/ontologies/product-market-codes/I26000
700 1 _aVillaverde, Karen.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783662559918
776 0 8 _iPrinted edition:
_z9783662559925
776 0 8 _iPrinted edition:
_z9783662572566
830 0 _aStudies in Computational Intelligence,
_x1860-949X ;
_v750
856 4 0 _zLibro electrónico
_uhttp://148.231.10.114:2048/login?url=https://doi.org/10.1007/978-3-662-55993-2
912 _aZDB-2-ENG
912 _aZDB-2-SXE
942 _cLIBRO_ELEC
999 _c244177
_d244176