Discovery Based Learning In The Life Sciences
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Discovery-Based Learning in the Life Sciences
Author: Kathleen M. Susman
language: en
Publisher: John Wiley & Sons
Release Date: 2015-06-30
For nearly a decade, scientists, educators and policy makers have issued a call to college biology professors to transform undergraduate life sciences education. As a gateway science for many undergraduate students, biology courses are crucial to addressing many of the challenges we face, such as climate change, sustainable food supply and fresh water and emerging public health issues. While canned laboratories and cook-book approaches to college science education do teach students to operate equipment, make accurate measurements and work well with numbers, they do not teach students how to take a scientific approach to an area of interest about the natural world. Science is more than just techniques, measurements and facts; science is critical thinking and interpretation, which are essential to scientific research. Discovery-Based Learning in the Life Sciences presents a different way of organizing and developing biology teaching laboratories, to promote both deep learning and understanding of core concepts, while still teaching the creative process of science. In eight chapters, the text guides undergraduate instructors in creating their own discovery-based experiments. The first chapter introduces the text, delving into the necessity of science education reform. The chapters that follow address pedagogical goals and desired outcomes, incorporating discovery-based laboratory experiences, realistic constraints on such lab experiments, model scenarios, and alternate ways to enhance student understanding. The book concludes with a reflection on four imperatives in life science research-- climate, food, energy and health-- and how we can use these laboratory experiments to address them. Discovery-Based Learning in the Life Sciences is an invaluable guide for undergraduate instructors in the life sciences aiming to revamp their curriculum, inspire their students and prepare them for careers as educated global citizens.
Deep Learning for the Life Sciences
Deep learning has already achieved remarkable results in many fields. Now it’s making waves throughout the sciences broadly and the life sciences in particular. This practical book teaches developers and scientists how to use deep learning for genomics, chemistry, biophysics, microscopy, medical analysis, and other fields. Ideal for practicing developers and scientists ready to apply their skills to scientific applications such as biology, genetics, and drug discovery, this book introduces several deep network primitives. You’ll follow a case study on the problem of designing new therapeutics that ties together physics, chemistry, biology, and medicine—an example that represents one of science’s greatest challenges. Learn the basics of performing machine learning on molecular data Understand why deep learning is a powerful tool for genetics and genomics Apply deep learning to understand biophysical systems Get a brief introduction to machine learning with DeepChem Use deep learning to analyze microscopic images Analyze medical scans using deep learning techniques Learn about variational autoencoders and generative adversarial networks Interpret what your model is doing and how it’s working
The Neurological Basis of Learning, Development and Discovery
Author: Anton E. Lawson
language: en
Publisher: Springer Science & Business Media
Release Date: 2006-04-11
A goal of mine ever since becoming an educational researcher has been to help construct a sound theory to guide instructional practice. For far too long, educational practice has suffered because we have lacked firm instructional guidelines, which in my view should be based on sound psychological theory, which in turn should be based on sound neurological theory. In other words, teachers need to know how to teach and that "how-to-teach" should be based solidly on how people learn and how their brains function. As you will see in this book, my answer to the question of how people learn is that we all learn by spontaneously generating and testing ideas. Idea generating involves analogies and testing requires comparing predicted consequences with actual consequences. We learn this way because the brain is essentially an idea generating and testing machine. But there is more to it than this. The very process ofgenerating and testing ideas results not only in the construction of ideas that work (i. e. , the learning of useful declarative knowledge), but also in improved skill in learning (i. e. , the development of improved procedural knowledge).