Applying Machine Learning In Science Education Research
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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 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.
Handbook of Research on Science Learning Progressions
Gathering contributions from leading scholars around the world, this handbook offers a comprehensive resource on the most recent advances in research surrounding the theories, methodologies, and applications of science learning progressions. Researchers and educators have used learning progressions to guide the design and alignment of curriculum, instruction, and assessment, and to help students learn scientific knowledge and practices in a coherent and connected way across multiple years. This handbook lays out the development and current state of research in this field across four sections: learning progression theories and methodologies; learning progressions to promote student learning; teachers’ learning and use of learning progressions; and new technology in learning progression research. Featuring internationally-recognized experts in learning progression research as well as up-and-coming voices, the Handbook of Research on Science Learning Progressions offers a defining new resource for researchers, teachers and teacher educators, and curriculum and assessment developers in science education.
Machine Learning in Educational Sciences
This comprehensive volume investigates the untapped potential of machine learning in educational settings. It examines the profound impact machine learning can have on reshaping educational research. Each chapter delves into specific applications and advancements, sheds light on theory-building, and multidisciplinary research, and identifies areas for further development. It encompasses various topics, such as machine-based learning in psychological assessment. It also highlights the power of machine learning in analyzing large-scale international assessment data and utilizing natural language processing for science education. With contributions from leading scholars in the field, this book provides a comprehensive, evidence-based framework for leveraging machine-learning approaches to enhance educational outcomes. The book offers valuable insights and recommendations that could help shape the future of educational sciences.