- ホーム
- > 洋書
- > ドイツ書
- > Social Sciences, Jurisprudence & Economy
- > Education Science / Pedagogic
- > adult education
Full 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.
Contents
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.
