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.

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.

This handbook gathers in one volume the major research and scholarship related to multicultural science education that has developed since the field was named and established by Atwater in 1993. Culture is defined in this handbook as an integrated pattern of shared values, beliefs, languages, worldviews, behaviors, artifacts, knowledge, and social and political relationships of a group of people in a particular place or time that the people use to understand or make meaning of their world, each other, and other groups of people and to transmit these to succeeding generations. The research studies include both different kinds of qualitative and quantitative studies. The chapters in this volume reflect differing ideas about culture and its impact on science learning and teaching in different K-14 contexts and policy issues. Research findings about groups that are underrepresented in STEM in the United States, and in other countries related to language issues and indigenous knowledge are included in this volume.

This book argues that modelling should be a component of all school curricula that aspire to provide ‘authentic science education for all’. The literature on modelling is reviewed and a ‘model of modelling’ is proposed. The conditions for the successful implementation of the ‘model of modelling’ in classrooms are explored and illustrated from practical experience. The roles of argumentation, visualisation, and analogical reasoning, in successful modelling-based teaching are reviewed. The contribution of such teaching to both the learning of key scientific concepts and an understanding of the nature of science are established. Approaches to the design of curricula that facilitate the progressive grasp of the knowledge and skills entailed in modelling are outlined. Recognising that the approach will both represent a substantial change from the ‘content-transmission’ approach to science teaching and be in accordance with current best-practice in science education, the design of suitable approaches to teacher education are discussed. Finally, the challenges that modelling-based education pose to science education researchers, advanced students of science education and curriculum design, teacher educators, public examiners, and textbook designers, are all outlined.

Understanding the complexity of the natural world and making sense of phenomena is one of the main goals of science and science education. When investigating complex phenomena, such as climate change or pandemic outbreaks, students are expected to engage in systems thinking by considering the boundaries of the investigated system, identifying the relevant components and their interactions, and exploring system attributes such as hierarchical organization, dynamicity, feedback loops, and emergence. Scientific models are tools that support students’ reasoning and understanding of complex systems, and students are expected to develop their modeling competence and to engage in the modeling process by constructing, testing, revising, and using models to explain and predict phenomena. Computational modeling tools, for example, provide students with the opportunity to explore big data, run simulations and investigate complex systems. Therefore, both systems thinking and modeling approaches are important for science education when investigating complex phenomena.

This open access volume is a collection of full papers based on the peer-reviewed presentations accepted for the European Researchers in Didactics of Biology, ERIDOB 2022 conference. ERIDOB aims to bring together researchers in didactics of Biology from Europe and around the world to share and discuss their research work and results. It is the only major international conference whose focus lies exclusively on biology education research, and all the papers are written by international researchers from across Europe (and beyond) which report on a range of contemporary biology education research projects. They are all entirely new papers describing new research in the field. Each paper has been peer-reviewed by experienced biology education researchers and the members of the ERIDOB Academic Committee. The selected papers are collated within the following categories of biology education: · Teaching Strategies and Learning Environments · Students’ Knowledge, Conceptions, Values, Attitudes and Motivation · Outdoor and Environmental Education · Biology Teachers’ Professional Development By providing a collection of new research findings from many countries, this book is a great resource for researchers and practitioners such as school, college and university biology teachers' around the world. It is useful for training biology teachers and therefore valuable to teacher training institutions.

This edited volume provides an in-depth exploration of a theoretical framework supporting Early Childhood Science Education research and teaching best practices. Particularly by presenting the concept of the Precursor Model from an epistemological, psychological, and didactical point of view at Early Childhood Science Education. The book examines and discusses the nature of Precursor Models and their use for early science teaching and learning. It scrutinizes different aspects of the construction of such models applied in early childhood education settings and contexts. Several empirical studies are presented within diverse scientific domains, as well as in international educational contexts. By providing a vary of examples of precursor models it makes this book a great companion for teachers aiming to teach children to understand and reason about topics such as: floating and sinking; shadow formation; water state changes; air; clouds and rain; electricity; inheritance and selection; as well as variation within populations. Finally, this volume supports the development of science education from an early age by using the original framework of a precursor model to mediate teaching and learning science at school during early childhood.