Towards A Framework For Representational Competence In Science Education
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Towards a Framework for Representational Competence in Science Education
This book covers the current state of thinking and what it means to have a framework of representational competence and how such theory can be used to shape our understanding of the use of representations in science education, assessment, and instruction. Currently, there is not a consensus in science education regarding representational competence as a unified theoretical framework. There are multiple theories of representational competence in the literature that use differing perspectives on what competence means and entails. Furthermore, dependent largely on the discipline, language discrepancies cause a potential barrier for merging ideas and pushing forward in this area. While a single unified theory may not be a realistic goal, there needs to be strides taken toward working as a unified research community to better investigate and interpret representational competence. An objective of this book is to initiate thinking about a representational competence theoretical framework across science educators, learning scientists, practitioners and scientists. As such, we have divided the chapters into three major themes to help push our thinking forward: presenting current thinking about representational competence in science education, assessing representational competence within learners, and using our understandings to structure instruction.
Teacher Education for Transformative Agency
Many teacher education programs globally are undergoing significant changes in response to government policy, imperatives driven by global competitiveness, as well as local conditions. This is particularly relevant in the South African context where teacher education seeks to navigate from the ravages of apartheid education towards addressing the developmental needs of the majority of its citizens. This book records and explores efforts by academic staff members within the Faculty of Education at Stellenbosch University, South Africa, responding to the demands of a new program in initial teacher education. It brings together diverse views seeking to present a coherent program in the Postgraduate Certificate in Education (PGCE). It examines how curriculum design unfolds across disciplines in the program, and crucially, the commonalities in the presentation of course material. Lecturers examine the purpose, structure and content of their teaching as they engage with putting democratic policy goals into practice in the core, as well as subject-specific modules of the program.
Towards a Competence-Based View on Models and Modeling in Science Education
Author: Annette Upmeier zu Belzen
language: en
Publisher: Springer Nature
Release Date: 2020-01-01
The book takes a closer look at the theoretical and empirical basis for a competence-based view of models and modeling in science learning and science education research. Current thinking about models and modeling is reflected. The focus lies on the development of modeling competence in science education, and on philosophical aspects, including perspectives on nature of science. The book explores, interprets, and discusses models and modeling from the perspective of different theoretical frameworks and empirical results. The extent to which these frameworks can be integrated into a competence-based approach for science education is discussed. In addition, the book provides practical guidance by outlining evidence-based approaches to diagnosing and promoting modeling competence. The aim is to convey a strong understanding of models and modeling for professions such as teacher educators, science education researchers, teachers, and scientists. Different methods for the diagnosis and assessment of modeling competence are presented and discussed with regard to their potential and limitations. The book provides evidence-based ideas about how teachers can be supported in teaching with models and modeling implementing a competence-based approach and, thus, how students can develop their modeling competence. Based on the findings, research challenges for the future are identified.