東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Educational data science: essentials...

Pena-Ayala, Alejandro.

Linked to FindBook

Google Book

Amazon

博客來

Educational data science: essentials, approaches, and tendencies = proactive education based on empirical big data evidence /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Educational data science: essentials, approaches, and tendencies/ edited by Alejandro Pena-Ayala.
Reminder of title:	proactive education based on empirical big data evidence /
other author:	Pena-Ayala, Alejandro.
Published:	Singapore :Springer Nature Singapore : : 2023.,
Description:	xiii, 291 p. :ill., digital ;24 cm.
[NT 15003449]:	1. Engaging in Student-Centered Educational Data Science through Learning Engineering -- 2. A review of clustering models in educational data science towards fairness-aware learning -- 3. Educational Data Science: Is an "Umbrella Term" or an Emergent Domain? -- 4. Educational Data Science Approach for End-to-End Quality Assurance Process for Building Credit-Worthy Online Courses -- 5. Understanding the Effect of Cohesion in Academic Writing Clarity Using Education Data Science -- 6. Sequential pattern mining in educational data: the application context, potential, strengths, and limitations -- 7. Sync Ratio and Cluster Heat Map for Visualizing Student Engagement.
Contained By:	Springer Nature eBook
Subject:	Education - Data processing. -
Online resource:	https://doi.org/10.1007/978-981-99-0026-8
ISBN:	9789819900268

Educational data science: essentials, approaches, and tendencies = proactive education based on empirical big data evidence /
Educational data science: essentials, approaches, and tendenciesproactive education based on empirical big data evidence /[electronic resource] :edited by Alejandro Pena-Ayala. - Singapore :Springer Nature Singapore :2023. - xiii, 291 p. :ill., digital ;24 cm. - Big data management,2522-0187. - Big data management..

1. Engaging in Student-Centered Educational Data Science through Learning Engineering -- 2. A review of clustering models in educational data science towards fairness-aware learning -- 3. Educational Data Science: Is an "Umbrella Term" or an Emergent Domain? -- 4. Educational Data Science Approach for End-to-End Quality Assurance Process for Building Credit-Worthy Online Courses -- 5. Understanding the Effect of Cohesion in Academic Writing Clarity Using Education Data Science -- 6. Sequential pattern mining in educational data: the application context, potential, strengths, and limitations -- 7. Sync Ratio and Cluster Heat Map for Visualizing Student Engagement.

This book describes theoretical elements, practical approaches, and specialized tools that systematically organize, characterize, and analyze big data gathered from educational affairs and settings. Moreover, the book shows several inference criteria to leverage and produce descriptive, explanatory, and predictive closures to study and understand education phenomena at in classroom and online environments. This is why diverse researchers and scholars contribute with valuable chapters to ground with well--sounded theoretical and methodological constructs in the novel field of Educational Data Science (EDS), which examines academic big data repositories, as well as to introduces systematic reviews, reveals valuable insights, and promotes its application to extend its practice. EDS as a transdisciplinary field relies on statistics, probability, machine learning, data mining, and analytics, in addition to biological, psychological, and neurological knowledge about learning science. With this in mind, the book is devoted to those that are in charge of educational management, educators, pedagogues, academics, computer technologists, researchers, and postgraduate students, who pursue to acquire a conceptual, formal, and practical landscape of how to deploy EDS to build proactive, real- time, and reactive applications that personalize education, enhance teaching, and improve learning!

ISBN: 9789819900268

Standard No.: 10.1007/978-981-99-0026-8doiSubjects--Topical Terms:

524970
Education
--Data processing.

LC Class. No.: LB1028.43

Dewey Class. No.: 370.28557

Educational data science: essentials, approaches, and tendencies = proactive education based on empirical big data evidence /
LDR:03173nmm a2200337 a 4500 001 2317037
003 DE-He213
005 20230429073307.0
006 m d
007 cr nn 008maaau
008 230902s2023 si s 0 eng d
020 $a 9789819900268 $q (electronic bk.)
020 $a 9789819900251 $q (paper)
024 7 $a 10.1007/978-981-99-0026-8 $2 doi
035 $a 978-981-99-0026-8
040 $a GP $c GP
041 0 $a eng
050 4 $a LB1028.43
072 7 $a UN $2 bicssc
072 7 $a COM031000 $2 bisacsh
072 7 $a UN $2 thema
082 0 4 $a 370.28557 $2 23
090 $a LB1028.43 $b .E24 2023
245 0 0 $a Educational data science: essentials, approaches, and tendencies $h [electronic resource] : $b proactive education based on empirical big data evidence / $c edited by Alejandro Pena-Ayala.
260 $a Singapore : $b Springer Nature Singapore : $b Imprint: Springer, $c 2023.
300 $a xiii, 291 p. : $b ill., digital ; $c 24 cm.
490 1 $a Big data management, $x 2522-0187
505 0 $a 1. Engaging in Student-Centered Educational Data Science through Learning Engineering -- 2. A review of clustering models in educational data science towards fairness-aware learning -- 3. Educational Data Science: Is an "Umbrella Term" or an Emergent Domain? -- 4. Educational Data Science Approach for End-to-End Quality Assurance Process for Building Credit-Worthy Online Courses -- 5. Understanding the Effect of Cohesion in Academic Writing Clarity Using Education Data Science -- 6. Sequential pattern mining in educational data: the application context, potential, strengths, and limitations -- 7. Sync Ratio and Cluster Heat Map for Visualizing Student Engagement.
520 $a This book describes theoretical elements, practical approaches, and specialized tools that systematically organize, characterize, and analyze big data gathered from educational affairs and settings. Moreover, the book shows several inference criteria to leverage and produce descriptive, explanatory, and predictive closures to study and understand education phenomena at in classroom and online environments. This is why diverse researchers and scholars contribute with valuable chapters to ground with well--sounded theoretical and methodological constructs in the novel field of Educational Data Science (EDS), which examines academic big data repositories, as well as to introduces systematic reviews, reveals valuable insights, and promotes its application to extend its practice. EDS as a transdisciplinary field relies on statistics, probability, machine learning, data mining, and analytics, in addition to biological, psychological, and neurological knowledge about learning science. With this in mind, the book is devoted to those that are in charge of educational management, educators, pedagogues, academics, computer technologists, researchers, and postgraduate students, who pursue to acquire a conceptual, formal, and practical landscape of how to deploy EDS to build proactive, real- time, and reactive applications that personalize education, enhance teaching, and improve learning!
650 0 $a Education $x Data processing. $3 524970
650 0 $a Big data. $3 2045508
650 1 4 $a Data Science. $3 3538937
650 2 4 $a Data Mining and Knowledge Discovery. $3 898250
650 2 4 $a Big Data. $3 3134868
700 1 $a Pena-Ayala, Alejandro. $3 2056836
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
830 0 $a Big data management. $3 3500827
856 4 0 $u https://doi.org/10.1007/978-981-99-0026-8
950 $a Computer Science (SpringerNature-11645)