By Michael J. Jacobson, Peter Reimann (auth.), Michael J. Jacobson, Peter Reimann (eds.)
ISBN-10: 0387882782
ISBN-13: 9780387882789
Few issues are as sure as societal changes—and the urgent desire for educators to organize scholars with the data and methods of pondering helpful for the demanding situations in a altering global. within the forward-thinking pages of Designs for studying Environments of the Future, foreign groups of researchers current rising advancements and findings in studying sciences and applied sciences on the infrastructure, curricular, and school room levels.
Focusing on principles approximately designing cutting edge environments for studying in parts resembling biology, engineering, genetics, arithmetic, and desktop technology, the ebook surveys quite a number studying applied sciences being explored round the world—a spectrum as different as electronic media, computing device modeling, and 3D digital worlds—and addresses demanding situations coming up from their layout and use. The editors’ holistic standpoint frames those techniques as not just discrete applied sciences yet as versatile studying environments that foster scholar engagement, participation, and collaboration. individuals describe probabilities for instructing and studying in those and different state of the art areas:
- Working with hypermodels and model-based reasoning
- Using visible representations in educating summary innovations
- Designing recommendations for studying in digital worlds
- Supporting net-based collaborative groups
- Integrating cutting edge studying applied sciences into faculties
- Developing own studying groups
Designs for studying Environments of the Future will improve the paintings of a variety of execs, together with researchers and graduate scholars within the studying and cognitive sciences, and educators within the actual and social sciences.
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Additional resources for Designs for Learning Environments of the Future: International Perspectives from the Learning Sciences
Example text
Secondly, there is an important distinction to be made in how representations relate to the phenomena they purport to describe. We are not arguing that aggregate equational representations are intrinsically ill suited for learning engineering or science as there are many cases in which equational representations are fruitful for For more on design for learning versus design for use see, for example, Soloway, Guzdial, & Hay, 1994. 1 2 MaterialSim: A Constructionist Agent-Based Modeling Approach to Engineering 23 learning.
141), which relates the fraction of dispersed particles (f), the mean radius of the particles (r), and the maximum particle size after grain growth (Dmax): P= 3f 3fγ 2γ 3 f γ 4r · π rγ = ⇒ = ⇒ Dmax = 2r D 2r 3f 2π r 2 However, the NetLogo algorithm is not based on this formula, or its geometrical approximations. Before running his experiments, Bob was asked if the model could actually match the “official” equation, since they departed from very different ideas, and he was skeptical. 8). 8’s lower plot).
Goldstone and Wilensky (2008) have called for such a restructuration of science curricula using common transdisciplinary “patterns” such as energy minimization, positive feedback, and simulated annealing. , 2004; Levy, Novak, & Wilensky, 2006), probability, and statistics (Abrahamson & Wilensky, 2005; Wilensky, 1995), robotics 2 MaterialSim: A Constructionist Agent-Based Modeling Approach to Engineering 25 (Berland & Wilensky, 2004, 2005), physics (Sengupta & Wilensky, 2008; Wilensky, 1993; 1999a; 2003), evolution, population dynamics, and mathematics (Centola, Wilensky, & McKenzie, 2000; Wilensky, Hazzard, & Longenecker, 2000; Wolfram, 2002).
Designs for Learning Environments of the Future: International Perspectives from the Learning Sciences by Michael J. Jacobson, Peter Reimann (auth.), Michael J. Jacobson, Peter Reimann (eds.)
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