| 000 | 03183nam a22004935i 4500 | ||
|---|---|---|---|
| 001 | 978-981-97-7858-4 | ||
| 003 | DE-He213 | ||
| 005 | 20251030114924.0 | ||
| 007 | cr nn 008mamaa | ||
| 008 | 250113s2024 si | s |||| 0|eng d | ||
| 020 |
_a9789819778584 _9978-981-97-7858-4 |
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| 024 | 7 |
_a10.1007/978-981-97-7858-4 _2doi |
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| 050 | 4 | _aLB1028.3 | |
| 072 | 7 |
_aJNV _2bicssc |
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_aEDU000000 _2bisacsh |
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| 245 | 1 | 0 |
_aText Mining in Educational Research _h[electronic resource] : _bTopic Modeling and Latent Dirichlet Allocation / _cedited by Myint Swe Khine. |
| 250 | _a1st ed. 2024. | ||
| 264 | 1 |
_aSingapore : _bSpringer Nature Singapore : _bImprint: Springer, _c2024. |
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| 300 |
_aIX, 151 p. 38 illus., 26 illus. in color. _bonline resource. |
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| 336 |
_atext _btxt _2rdacontent |
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| 337 |
_acomputer _bc _2rdamedia |
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| 338 |
_aonline resource _bcr _2rdacarrier |
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_atext file _bPDF _2rda |
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| 505 | 0 | _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. | |
| 520 | _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. . | ||
| 650 | 0 |
_aEducational technology. _957244 |
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| 650 | 0 |
_aTeachers _xTraining of. _958400 |
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| 650 | 1 | 4 |
_aDigital Education and Educational Technology. _968557 |
| 650 | 2 | 4 | _aTeaching and Teacher Education. |
| 700 | 1 |
_aKhine, Myint Swe. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt _971697 |
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| 710 | 2 | _aSpringerLink (Online service) | |
| 773 | 0 | _tSpringer Nature eBook | |
| 776 | 0 | 8 |
_iPrinted edition: _z9789819778577 |
| 776 | 0 | 8 |
_iPrinted edition: _z9789819778591 |
| 776 | 0 | 8 |
_iPrinted edition: _z9789819778607 |
| 856 | 4 | 0 | _uhttps://doi.org/10.1007/978-981-97-7858-4 |
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