02872nam a22004095i 4500001001800000003000900018005001700027007001500044008004100059020001800100024003500118050001300153072001600166072002300182072001500205245014300220250001800363264007200381300006400453336002600517337002600543338003600569347002400605505027000629520112900899650002802028650002702056650005002083650003602133700007902169710003402248773002602282776003602308776003602344776003602380856004602416978-981-97-7858-4DE-He21320251030114924.0cr nn 008mamaa250113s2024    si |    s    |||| 0|eng d  a97898197785847 a10.1007/978-981-97-7858-42doi 4aLB1028.3 7aJNV2bicssc 7aEDU0000002bisacsh 7aJNV2thema10aText Mining in Educational Researchh[electronic resource] :bTopic Modeling and Latent Dirichlet Allocation /cedited by Myint Swe Khine.  a1st ed. 2024. 1aSingapore :bSpringer Nature Singapore :bImprint: Springer,c2024.  aIX, 151 p. 38 illus., 26 illus. in color.bonline resource.  atextbtxt2rdacontent  acomputerbc2rdamedia  aonline resourcebcr2rdacarrier  atext filebPDF2rda0 aUsing 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.  aThis 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. . 0aEducational technology. 0aTeachersxTraining of.14aDigital Education and Educational Technology.24aTeaching and Teacher Education.1 aKhine, Myint Swe.eeditor.4edt4http://id.loc.gov/vocabulary/relators/edt2 aSpringerLink (Online service)0 tSpringer Nature eBook08iPrinted edition:z978981977857708iPrinted edition:z978981977859108iPrinted edition:z978981977860740uhttps://doi.org/10.1007/978-981-97-7858-4