Download Designs for Learning Environments of the Future: by Michael J. Jacobson, Peter Reimann (auth.), Michael J. PDF

By Michael J. Jacobson, Peter Reimann (auth.), Michael J. Jacobson, Peter Reimann (eds.)

Few issues are as convinced as societal changes—and the urgent want for educators to organize scholars with the data and methods of considering 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 lecture room levels.

Focusing on principles approximately designing leading edge environments for studying in components corresponding to biology, engineering, genetics, arithmetic, and computing device technology, the booklet surveys more than a few studying applied sciences being explored round the world—a spectrum as diversified as electronic media, laptop modeling, and 3D digital worlds—and addresses demanding situations coming up from their layout and use. The editors’ holistic point of view frames those ideas as not just discrete applied sciences yet as versatile studying environments that foster scholar engagement, participation, and collaboration. members describe chances for educating 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 concepts for studying in digital worlds
  • Supporting net-based collaborative groups
  • Integrating cutting edge studying applied sciences into colleges
  • Developing own studying groups


Designs for studying Environments of the Future will improve the paintings of quite a lot 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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Even without formally mathematizing the time dependency of grain growth, she understood the reason for the triple point to be considered a “low-mobility” point in a microstructure. The central atom has two atoms (out of six) of each of the surrounding grains as neighbors, so the switch probability is the same (1/3), and there is no preferred growth direction. ” Rather than being told, Liz arrived at this conclusion on her own, by drawing microstructures, changing variables, and observing the dynamics of the 2 MaterialSim: A Constructionist Agent-Based Modeling Approach to Engineering 41 simulation.

At times, the small grain would grow, but most of the times it would shrink. Some of the students wanted to slow down the simulation and use the “zoom” tool to see the process in more detail, which meant they could only see the microlevel phenomenon (atoms flipping to different orientations). By zooming out again, they could observe the emergent behavior: curved surfaces disappearing as the Laplace–Young equation would predict. Thus, there is a qualitative difference between traditional learning tools and agentbased modeling: not only are students observing an expected outcome, but also they are able to see the process unfolding at various levels.

For example, to ask all turtles to check for neighbors (within a one-patch4 radius) and move backwards 10 units in case there are at least four neighbors around, we would use the following command: ask turtles [if (count neighbors in-radius 1) > 4 [back 10]] Such simple agent rules, however, may give rise to complex emergent aggregate phenomena, many of which are congruent with their traditional macroscopic formula-based descriptions. In addition to the modeling language itself, NetLogo includes a graphical user interface with advanced visualization features, such as multiple topologies and 3D representations.

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