Applying Machine Learning in Science Education Research
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 ad...
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| Ձևաչափ: | Online |
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| Լեզու: | անգլերեն |
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Springer Nature
2025
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| Խորագրեր: | |
| Առցանց հասանելիություն: | ONIX_20250313_9783031742279_12 |
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Չկան պիտակներ, Եղեք առաջինը, ով նշում է այս գրառումը!
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| _version_ | 1869527774685822976 |
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| collection | Directory of Open Access Books |
| description | 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. |
| format | Online |
| id | doab-20.500.12854ir-157445 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | Springer Nature |
| publisherStr | Springer Nature |
| record_format | ojs |
| spelling | doab-20.500.12854ir-1574452025-07-30T09:00:00Z Applying Machine Learning in Science Education Research Wulff, Peter Kubsch, Marcus Krist, Christina Machine learning Natural language processing science education research supervised and unsupervised learning Probabilistic modeling artificial intelligence in Science education Machine learning models Human-machine interactions Pattern recognition computational grounded theory reinforcement learning deep neural networks multimodal learning transfer learning qualitative and quantitative research methods computer-aided tutoring human-machine interaction big data complex systems theory thema EDItEUR::J Society and Social Sciences::JN Education::JNU Teaching of a specific subject thema EDItEUR::P Mathematics and Science::PD Science: general issues thema EDItEUR::J Society and Social Sciences::JN Education::JNZ Study and learning skills: general thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQM Machine learning thema EDItEUR::J Society and Social Sciences::JN Education::JNU Teaching of a specific subject thema EDItEUR::P Mathematics and Science::PD Science: general issues thema EDItEUR::J Society and Social Sciences::JN Education::JNZ Study and learning skills: general thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQM Machine learning 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. 2025-03-16T00:40:49Z 2025-03-16T00:40:49Z 2025-03-13T10:08:43Z 2025 book ONIX_20250313_9783031742279_12 https://library.oapen.org/handle/20.500.12657/99864 9783031742279 9783031742262 https://directory.doabooks.org/handle/20.500.12854/157445 eng Springer Texts in Education open access image/jpeg n/a https://library.oapen.org/bitstream/20.500.12657/99864/1/9783031742279.pdf Springer Nature Springer Nature Switzerland 10.1007/978-3-031-74227-9 10.1007/978-3-031-74227-9 9fa3421d-f917-4153-b9ab-fc337c396b5a 9783031742279 9783031742262 Springer Nature Switzerland 369 Cham open access |
| spellingShingle | Machine learning Natural language processing science education research supervised and unsupervised learning Probabilistic modeling artificial intelligence in Science education Machine learning models Human-machine interactions Pattern recognition computational grounded theory reinforcement learning deep neural networks multimodal learning transfer learning qualitative and quantitative research methods computer-aided tutoring human-machine interaction big data complex systems theory thema EDItEUR::J Society and Social Sciences::JN Education::JNU Teaching of a specific subject thema EDItEUR::P Mathematics and Science::PD Science: general issues thema EDItEUR::J Society and Social Sciences::JN Education::JNZ Study and learning skills: general thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQM Machine learning thema EDItEUR::J Society and Social Sciences::JN Education::JNU Teaching of a specific subject thema EDItEUR::P Mathematics and Science::PD Science: general issues thema EDItEUR::J Society and Social Sciences::JN Education::JNZ Study and learning skills: general thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQM Machine learning Applying Machine Learning in Science Education Research |
| title | Applying Machine Learning in Science Education Research |
| title_full | Applying Machine Learning in Science Education Research |
| title_fullStr | Applying Machine Learning in Science Education Research |
| title_full_unstemmed | Applying Machine Learning in Science Education Research |
| title_short | Applying Machine Learning in Science Education Research |
| title_sort | applying machine learning in science education research |
| topic | Machine learning Natural language processing science education research supervised and unsupervised learning Probabilistic modeling artificial intelligence in Science education Machine learning models Human-machine interactions Pattern recognition computational grounded theory reinforcement learning deep neural networks multimodal learning transfer learning qualitative and quantitative research methods computer-aided tutoring human-machine interaction big data complex systems theory thema EDItEUR::J Society and Social Sciences::JN Education::JNU Teaching of a specific subject thema EDItEUR::P Mathematics and Science::PD Science: general issues thema EDItEUR::J Society and Social Sciences::JN Education::JNZ Study and learning skills: general thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQM Machine learning thema EDItEUR::J Society and Social Sciences::JN Education::JNU Teaching of a specific subject thema EDItEUR::P Mathematics and Science::PD Science: general issues thema EDItEUR::J Society and Social Sciences::JN Education::JNZ Study and learning skills: general thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQM Machine learning |
| topic_facet | Machine learning Natural language processing science education research supervised and unsupervised learning Probabilistic modeling artificial intelligence in Science education Machine learning models Human-machine interactions Pattern recognition computational grounded theory reinforcement learning deep neural networks multimodal learning transfer learning qualitative and quantitative research methods computer-aided tutoring human-machine interaction big data complex systems theory thema EDItEUR::J Society and Social Sciences::JN Education::JNU Teaching of a specific subject thema EDItEUR::P Mathematics and Science::PD Science: general issues thema EDItEUR::J Society and Social Sciences::JN Education::JNZ Study and learning skills: general thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQM Machine learning thema EDItEUR::J Society and Social Sciences::JN Education::JNU Teaching of a specific subject thema EDItEUR::P Mathematics and Science::PD Science: general issues thema EDItEUR::J Society and Social Sciences::JN Education::JNZ Study and learning skills: general thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQM Machine learning |
| url | ONIX_20250313_9783031742279_12 |