226 research outputs found

    Ways of thinking in STEM-based problem solving

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    This article proposes an interconnected framework, Ways of thinking in STEM-based Problem Solving, which addresses cognitive processes that facilitate learning, problem solving, and interdisciplinary concept development. The framework comprises critical thinking, incorporating critical mathematical modelling and philosophical inquiry, systems thinking, and design-based thinking, which collectively contribute to adaptive and innovative thinking. It is argued that the pinnacle of this framework is learning innovation, involving the generation of powerful disciplinary knowledge and thinking processes that can be applied to subsequent problem challenges. Consideration is first given to STEM-based problem solving with a focus on mathematics. Mathematical and STEM-based problems are viewed here as goal-directed, multifaceted experiences that (1) demand core, facilitative ways of thinking, (2) require the development of productive and adaptive ways to navigate complexity, (3) enable multiple approaches and practices, (4) recruit interdisciplinary solution processes, and (5) facilitate the growth of learning innovation. The nature, role, and contributions of each way of thinking in STEM-based problem solving and learning are then explored, with their interactions highlighted. Examples from classroom-based research are presented, together with teaching implications

    Multidisciplinary Modelling in a Sixth-Grade Tsunami Investigation

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    This study investigated sixth-grade students’ development of multidisciplinary models involving the integration of mathematics, science, and statistics, together with shared STEM practices. Mathematics and science featured a reciprocal relationship within the real-world context of tsunami inundation. Experimenting with water tubs comprising “landmasses” and “shore slopes” of varying angles, students explored how varying the slope of the shoreline affects the inundation distance. Given that organising and structuring data are essential to the creation of models as systems of representation, students’ modelling revealed a basic understanding of key statistical concepts and processes, including variation and covariation, and an ability to identify trends both within and across data sets. Students were able to apply their learning in recommending ways of minimising the impact of a real-world tsunami, demonstrating how such an investigation can facilitate understanding of natural phenomena. The overall findings indicate how elementary-grade students can successfully engage in independent multidisciplinary modelling within integrated STEM investigations and develop important conceptual understandings in the process

    Fifth-grade Students’ Quantitative Modeling in a STEM Investigation

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    Fifth-grade students applied quantitative reasoning in exploring the flow times of three simulated lavas of different viscosities down the slope of a hand-made volcano. After modeling the lava flow times for 6 km down the volcano slope, students used their quantitative models to predict the evacuation times for villagers living 10 km down. Reported are how students structured and represented their data in model creation, how they applied their knowledge of viscosity in identifying variation and covariation displayed in their models, and how they applied quantitative reasoning in making predictions from their models. Students’ quantitative models included graph forms not formally taught at their grade level, including ordered case value, stacked bar, and line graphs. Models comprising ordered case value and line graphs appeared to facilitate students’ detection and interpretation of covariation between lava viscosity and flow time. Although there was some difficulty in explicating a global view of covariation, students could identify the variation in the viscosity and time separately. Linking their knowledge of viscosity with lava flow times suggested at least an implicit understanding of covariation, and illustrated a reciprocal relationship between mathematics and science. In making predictions about evacuation times, students applied both quantitative interpretation and quantitative literacy (Mayes, 2019), together with their understanding of viscosity and their contextual knowledge of volcanoes. Students’ diverse applications of quantitative reasoning were not anticipated, especially since they were not given any particular directions. In expressing the certainty of their predictions, students referred to viscosity and lava flow rates, the dimensions of the volcano, and environmental factors

    Interdisciplinary mathematical modeling

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    This chapter draws on the interdisciplinary nature of mathematical modeling in addressing fifth-grade students’ models for selecting a suitable site for the capital of Queensland, Australia, in 1859. Given a range of data on possible sites, students applied their knowledge of mathematics, history, society, and the environment in creating their models. They generated their own means of problem solution and developed mathematical ideas and processes that extended beyond their existing program. Children’s responses revealed a range of models displaying various levels of sophistication, from the use of marking and tallying systems, through to assigning positive and negative scores, and finally, operationalizing the variables by ranking, rating, or categorizing. There was also evidence of students identifying relationships between variables in their decision-making and their awareness of not extending beyond the data given in working the problem

    Mathematical modelling – A key to citizenship education

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    The pandemic has demonstrated more than ever that citizens around the world need to understand how mathematics contributes to understanding global challenges and ways of overcoming them. People need to understand that predictions are based on models that make use of assumptions and the best inputs available. They also need to learn to critically evaluate reports based on the outcomes of models to make effective decisions and deal with the inherent uncertainty in an appropriate way. These capabilities make it clear that mathematical modelling is a key element of citizenship education. Given this fundamental role of modelling, we take a closer look at its definition, its history, its connection to other teaching approaches, as well as the competences students need to carry through modelling processes and the competences teachers need for teaching modelling

    STEM and Integration

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    This entry first examines perspectives on STEM education (science, technology, engineering, and mathematics) and approaches to integrating the STEM disciplines. The second part explores STEM education and integration within preservice and in-service teacher education.Promoting students’ learning in STEM across the school years is receiving escalating global attention. It is frequently stressed that “STEM jobs are the jobs of the future,” with STEM skills increasingly in demand not only within, but also beyond, specific STEM occupations. With STEM competencies seen as essential to promoting innovation, productivity, and overall economic growth, a focus on STEM education is regarded as critical in many nations

    Exploring Mathematical Modeling with Young Learners

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    This book conceptualizes the nature of mathematical modeling in the early grades from both teaching and learning perspectives. Mathematical modeling provides a unique opportunity to engage elementary students in the creative process of mathematizing their world.A diverse community of internationally known researchers and practitioners share studies that advance the field with respect to the following themes: The Nature of Mathematical Modeling in the Early Grades Content Knowledge and Pedagogy for Mathematical Modeling Student Experiences as Modelers Teacher Education and Professional Development in ModelingExperts in the field provide commentaries that extend and connect ideas presented across chapters. This book is an invaluable resource in illustrating what all young children can achieve with mathematical modeling and how we can support teachers and families in this important work

    Design learning in STEM education

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    Design and design thinking have become more prominent in recent years, with design now being applied to a range of fields as diverse as business and medicine. Design thinking is an important component of complex problem solving, entailing the processes of problem identification, brainstorming solution ideas, generating prototypes, and testing and refining outcomes. Design plays a central role in technology and engineering education, where a basic understanding of how design impacts on the world and ultimately shapes our personal lives is considered essential. This chapter focuses on design learning in K-12 STEM education, specifically integrated STEM-based problem solving, through the lenses of learning about and learning through design

    Teaching and learning through mathematical problem posing:Commentary

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    The collection of articles in this special issue highlights the importance of problem posing from both teachers’ and students’ perspectives. Considerable research has been conducted across several decades on students’ problem posing, as the authors of the articles indicate. In contrast, studies of teachers’ knowledge of their students’ thinking as they engage in mathematical problem-posing and their capabilities in posing problems have been limited. Because research on teachers’ problem posing has not been prolific, the current articles raise more issues and questions than they can answer. I explore some of these issues and the challenges they present.</p

    Computational Thinking Is More about Thinking than Computing

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    Computational thinking is widely recognized as important, not only to those interested in computer science and mathematics but also to every student in the twenty-first century. However, the concept of computational thinking is arguably complex; the term itself can easily lead to direct connection with computing or computer in a restricted sense. In this editorial, we build on existing research about computational thinking to discuss it as a multi-faceted theoretical nature. We further present computational thinking, as a model of thinking, that is important not only in computer science and mathematics, but also in other disciplines of STEM and integrated STEM education broadly
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