國立虎尾科技大學 |

Machine Learning Paradigms = Advances in Learning Analytics /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Machine Learning Paradigms/ edited by Maria Virvou, Efthimios Alepis, George A. Tsihrintzis, Lakhmi C. Jain.
其他題名:	Advances in Learning Analytics /
其他作者:	Jain, Lakhmi C.
面頁冊數:	XVI, 223 p. 60 illus., 29 illus. in color.online resource. :
Contained By:	Springer Nature eBook
標題:	Data Mining and Knowledge Discovery. -
電子資源:	https://doi.org/10.1007/978-3-030-13743-4
ISBN:	9783030137434

Machine Learning Paradigms = Advances in Learning Analytics /
Machine Learning ParadigmsAdvances in Learning Analytics /[electronic resource] :edited by Maria Virvou, Efthimios Alepis, George A. Tsihrintzis, Lakhmi C. Jain. - 1st ed. 2020. - XVI, 223 p. 60 illus., 29 illus. in color.online resource. - Intelligent Systems Reference Library,1581868-4394 ;. - Intelligent Systems Reference Library,67.

This book presents recent machine learning paradigms and advances in learning analytics, an emerging research discipline concerned with the collection, advanced processing, and extraction of useful information from both educators’ and learners’ data with the goal of improving education and learning systems. In this context, internationally respected researchers present various aspects of learning analytics and selected application areas, including: • Using learning analytics to measure student engagement, to quantify the learning experience and to facilitate self-regulation; • Using learning analytics to predict student performance; • Using learning analytics to create learning materials and educational courses; and • Using learning analytics as a tool to support learners and educators in synchronous and asynchronous eLearning. The book offers a valuable asset for professors, researchers, scientists, engineers and students of all disciplines. Extensive bibliographies at the end of each chapter guide readers to probe further into their application areas of interest.

ISBN: 9783030137434

Standard No.: 10.1007/978-3-030-13743-4doiSubjects--Topical Terms:

677765
Data Mining and Knowledge Discovery.

LC Class. No.: Q342

Dewey Class. No.: 006.3

Machine Learning Paradigms = Advances in Learning Analytics /
LDR:02596nam a22003975i 4500 001 1024337
003 DE-He213
005 20200702025113.0
007 cr nn 008mamaa
008 210318s2020 gw | s |||| 0|eng d
020 $a 9783030137434 $9 978-3-030-13743-4
024 7 $a 10.1007/978-3-030-13743-4 $2 doi
035 $a 978-3-030-13743-4
050 4 $a Q342
072 7 $a UYQ $2 bicssc
072 7 $a TEC009000 $2 bisacsh
072 7 $a UYQ $2 thema
082 0 4 $a 006.3 $2 23
245 1 0 $a Machine Learning Paradigms $h [electronic resource] : $b Advances in Learning Analytics / $c edited by Maria Virvou, Efthimios Alepis, George A. Tsihrintzis, Lakhmi C. Jain.
250 $a 1st ed. 2020.
264 1 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2020.
300 $a XVI, 223 p. 60 illus., 29 illus. in color. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
490 1 $a Intelligent Systems Reference Library, $x 1868-4394 ; $v 158
520 $a This book presents recent machine learning paradigms and advances in learning analytics, an emerging research discipline concerned with the collection, advanced processing, and extraction of useful information from both educators’ and learners’ data with the goal of improving education and learning systems. In this context, internationally respected researchers present various aspects of learning analytics and selected application areas, including: • Using learning analytics to measure student engagement, to quantify the learning experience and to facilitate self-regulation; • Using learning analytics to predict student performance; • Using learning analytics to create learning materials and educational courses; and • Using learning analytics as a tool to support learners and educators in synchronous and asynchronous eLearning. The book offers a valuable asset for professors, researchers, scientists, engineers and students of all disciplines. Extensive bibliographies at the end of each chapter guide readers to probe further into their application areas of interest.
650 2 4 $a Data Mining and Knowledge Discovery. $3 677765
650 2 4 $a Learning & Instruction. $3 670152
650 2 4 $a Artificial Intelligence. $3 646849
650 1 4 $a Computational Intelligence. $3 768837
650 0 $a Data mining. $3 528622
650 0 $a Instruction. $3 1253701
650 0 $a Learning. $3 555256
650 0 $a Artificial intelligence. $3 559380
650 0 $a Computational intelligence. $3 568984
700 1 $a Jain, Lakhmi C. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 671306
700 1 $a Tsihrintzis, George A. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 681153
700 1 $a Alepis, Efthimios. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1023756
700 1 $a Virvou, Maria. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 665395
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9783030137427
776 0 8 $i Printed edition: $z 9783030137441
776 0 8 $i Printed edition: $z 9783030137458
830 0 $a Intelligent Systems Reference Library, $x 1868-4394 ; $v 67 $3 1253823
856 4 0 $u https://doi.org/10.1007/978-3-030-13743-4
912 $a ZDB-2-INR
912 $a ZDB-2-SXIT
950 $a Intelligent Technologies and Robotics (SpringerNature-42732)
950 $a Intelligent Technologies and Robotics (R0) (SpringerNature-43728)