國立高雄大學圖資館 |

Language: English

Back

Text mining in educational researchtopic modeling and Latent Dirichlet Allocation /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Text mining in educational researchedited by Myint Swe Khine.
Reminder of title:	topic modeling and Latent Dirichlet Allocation /
other author:	Khine, Myint Swe.
Published:	Singapore :Springer Nature Singapore :2024.
Description:	ix, 151 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	EducationResearch
Online resource:	https://doi.org/10.1007/978-981-97-7858-4
ISBN:	9789819778584$q(electronic bk.)

Text mining in educational researchtopic modeling and Latent Dirichlet Allocation /
Text mining in educational researchtopic modeling and Latent Dirichlet Allocation /[electronic resource] :edited by Myint Swe Khine. - Singapore :Springer Nature Singapore :2024. - ix, 151 p. :ill. (some col.), digital ;24 cm.

Using the Structural Topic Model to Explore Learner Satisfaction with LMOOCs -- Text Mining Applications in Educational Research -- The Advent of Topic Noise Models -- Formalizing the Social Aspects of Topic Modeling: Focus on the Social Positioning of Researchers.

This edited book consolidates and documents recent research on topic modeling in text mining using Latent Dirichlet Allocation (LDA). Written by leading experts in topic modeling, it covers a wide range of areas, such as theory building, systematic research, and innovative applications. This book offers a thorough exploration of the latest advancements in topic modeling. From identifying issues in unstructured text data to categorizing documents and extracting valuable insights, the book provides practical use of LDA as a powerful tool in qualitative and quantitative research. The chapters discuss the rapidly evolving landscape of topic modeling algorithms and offer tangible examples and applications of LDA in educational research, showcasing its real-world impact. This book dives into the heart of educational research and uncovers the transformative potential of Latent Dirichlet Allocation in shaping the future of topic modeling. This book is a valuable resource, highlighting exemplary works and rapid advances in the field. It appeals to students, researchers, and practitioners interested in text mining.

ISBN: 9789819778584$q(electronic bk.)

Standard No.: 10.1007/978-981-97-7858-4doiSubjects--Topical Terms:

264735
Education
--Research

LC Class. No.: LB1028

Dewey Class. No.: 370.721

Text mining in educational researchtopic modeling and Latent Dirichlet Allocation /
LDR:02397nmm a2200325 a 4500 001 673991
003 DE-He213
005 20250113115326.0
006 m o d
007 cr nn 008maaau
008 250501s2024 si s 0 eng d
020 $a 9789819778584$q(electronic bk.)
020 $a 9789819778577$q(paper)
024 7 $a 10.1007/978-981-97-7858-4 $2 doi
035 $a 978-981-97-7858-4
040 $a GP $c GP
041 0 $a eng
050 4 $a LB1028
072 7 $a JNV $2 bicssc
072 7 $a EDU000000 $2 bisacsh
072 7 $a JNV $2 thema
082 0 4 $a 370.721 $2 23
090 $a LB1028 $b .T355 2024
245 0 0 $a Text mining in educational research $h [electronic resource] : $b topic modeling and Latent Dirichlet Allocation / $c edited by Myint Swe Khine.
260 $a Singapore : $b Springer Nature Singapore : $b Imprint: Springer, $c 2024.
300 $a ix, 151 p. : $b ill. (some col.), digital ; $c 24 cm.
505 0 $a Using the Structural Topic Model to Explore Learner Satisfaction with LMOOCs -- Text Mining Applications in Educational Research -- The Advent of Topic Noise Models -- Formalizing the Social Aspects of Topic Modeling: Focus on the Social Positioning of Researchers.
520 $a This edited book consolidates and documents recent research on topic modeling in text mining using Latent Dirichlet Allocation (LDA). Written by leading experts in topic modeling, it covers a wide range of areas, such as theory building, systematic research, and innovative applications. This book offers a thorough exploration of the latest advancements in topic modeling. From identifying issues in unstructured text data to categorizing documents and extracting valuable insights, the book provides practical use of LDA as a powerful tool in qualitative and quantitative research. The chapters discuss the rapidly evolving landscape of topic modeling algorithms and offer tangible examples and applications of LDA in educational research, showcasing its real-world impact. This book dives into the heart of educational research and uncovers the transformative potential of Latent Dirichlet Allocation in shaping the future of topic modeling. This book is a valuable resource, highlighting exemplary works and rapid advances in the field. It appeals to students, researchers, and practitioners interested in text mining.
650 0 $a Education $x Research $x Methodology. $3 264735
650 0 $a Text data mining. $3 892173
650 1 4 $a Digital Education and Educational Technology. $3 913216
650 2 4 $a Teaching and Teacher Education. $3 338703
700 1 $a Khine, Myint Swe. $3 260970
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/978-981-97-7858-4
950 $a Education (SpringerNature-41171)