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Applying machine learning in science...

Wulff, Peter.

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Applying machine learning in science education research = when, how, and why? /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Applying machine learning in science education research/ edited by Peter Wulff, Marcus Kubsch, Christina Krist.
Reminder of title:	when, how, and why? /
other author:	Wulff, Peter.
Published:	Cham :Springer Nature Switzerland : : 2025.,
Description:	xiii, 369 p. :ill. (some col.), digital ;24 cm.
[NT 15003449]:	Introduction -- Part I:Theoretical background -- Basics of machine learning -- Data in science education research -- Applying supervised ML -- Applying unsupervised ML -- Sequencing unsupervised and supervised ML -- Natural language processing and large language models -- Human-machine interactions in machine learning modeling: The role of theory -- Part II:Hands-on case studies -- Working with data getting started -- Automation Supervised Machine Learning -- Pattern Recognition - Unsupervised Machine Learning -- Automation and explainability: Supervised machine learning with text data -- Unsupervised ML with language data -- Unsupervised ML with text data -- Triangulating Computational and Qualitative Methods to Measure Scientific Uncertainty -- Part III:Future directions -- Risks and ethical considerations in the context of machine learning research in science education -- Future directions -- Conclusions.
Contained By:	Springer Nature eBook
Subject:	Science - Study and teaching -
Online resource:	https://doi.org/10.1007/978-3-031-74227-9
ISBN:	9783031742279

Applying machine learning in science education research = when, how, and why? /
Applying machine learning in science education researchwhen, how, and why? /[electronic resource] :edited by Peter Wulff, Marcus Kubsch, Christina Krist. - Cham :Springer Nature Switzerland :2025. - xiii, 369 p. :ill. (some col.), digital ;24 cm. - Springer texts in education,2366-7680. - Springer texts in education..

Introduction -- Part I:Theoretical background -- Basics of machine learning -- Data in science education research -- Applying supervised ML -- Applying unsupervised ML -- Sequencing unsupervised and supervised ML -- Natural language processing and large language models -- Human-machine interactions in machine learning modeling: The role of theory -- Part II:Hands-on case studies -- Working with data getting started -- Automation Supervised Machine Learning -- Pattern Recognition - Unsupervised Machine Learning -- Automation and explainability: Supervised machine learning with text data -- Unsupervised ML with language data -- Unsupervised ML with text data -- Triangulating Computational and Qualitative Methods to Measure Scientific Uncertainty -- Part III:Future directions -- Risks and ethical considerations in the context of machine learning research in science education -- Future directions -- Conclusions.

Open access.

This open access textbook offers science education researchers a hands-on guide for learning, critically examining, and integrating machine learning (ML) methods into their science education research projects. These methods power many artificial intelligence (AI)-based technologies and are widely adopted in science education research. ML can expand the methodological toolkit of science education researchers and provide novel opportunities to gain insights on science-related learning and teaching processes, however, applying ML poses novel challenges and is not suitable for every research context. The volume first introduces the theoretical underpinnings of ML methods and their connections to methodological commitments in science education research. It then presents exemplar case studies of ML uses in both formal and informal science education settings. These case studies include open-source data, executable programming code, and explanations of the methodological criteria and commitments guiding ML use in each case. The textbook concludes with a discussion of opportunities and potential future directions for ML in science education. This textbook is a valuable resource for science education lecturers, researchers, under-graduate, graduate and postgraduate students seeking new ways to apply ML in their work.

ISBN: 9783031742279

Standard No.: 10.1007/978-3-031-74227-9doiSubjects--Topical Terms:

525875
Science
--Study and teaching

LC Class. No.: Q181

Dewey Class. No.: 507.1

Applying machine learning in science education research = when, how, and why? /
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505 0 $a Introduction -- Part I:Theoretical background -- Basics of machine learning -- Data in science education research -- Applying supervised ML -- Applying unsupervised ML -- Sequencing unsupervised and supervised ML -- Natural language processing and large language models -- Human-machine interactions in machine learning modeling: The role of theory -- Part II:Hands-on case studies -- Working with data getting started -- Automation Supervised Machine Learning -- Pattern Recognition - Unsupervised Machine Learning -- Automation and explainability: Supervised machine learning with text data -- Unsupervised ML with language data -- Unsupervised ML with text data -- Triangulating Computational and Qualitative Methods to Measure Scientific Uncertainty -- Part III:Future directions -- Risks and ethical considerations in the context of machine learning research in science education -- Future directions -- Conclusions.
506 $a Open access.
520 $a This open access textbook offers science education researchers a hands-on guide for learning, critically examining, and integrating machine learning (ML) methods into their science education research projects. These methods power many artificial intelligence (AI)-based technologies and are widely adopted in science education research. ML can expand the methodological toolkit of science education researchers and provide novel opportunities to gain insights on science-related learning and teaching processes, however, applying ML poses novel challenges and is not suitable for every research context. The volume first introduces the theoretical underpinnings of ML methods and their connections to methodological commitments in science education research. It then presents exemplar case studies of ML uses in both formal and informal science education settings. These case studies include open-source data, executable programming code, and explanations of the methodological criteria and commitments guiding ML use in each case. The textbook concludes with a discussion of opportunities and potential future directions for ML in science education. This textbook is a valuable resource for science education lecturers, researchers, under-graduate, graduate and postgraduate students seeking new ways to apply ML in their work.
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700 1 $a Krist, Christina. $3 3785081
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