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Applying Machine Learning in Science Education Research
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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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Keywords
- artificial intelligence in Science education
- big data
- complex systems theory
- computational grounded theory
- computer-aided tutoring
- deep neural networks
- human-machine interaction
- Human-machine interactions
- Machine learning
- machine learning models
- Multimodal learning
- Natural Language Processing
- pattern recognition
- Probabilistic modeling
- qualitative and quantitative research methods
- reinforcement learning
- science education research
- supervised and unsupervised learning
- thema EDItEUR::J Society and Social Sciences::JN Education::JNU Teaching of a specific subject
- thema EDItEUR::J Society and Social Sciences::JN Education::JNZ Study and learning skills: general
- thema EDItEUR::P Mathematics and Science::PD Science: general issues
- thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQM Machine learning
- transfer learning
Links
DOI: 10.1007/978-3-031-74227-9Editions