Effective Learning in the Life Sciences

Effective Learning in the Life Sciences

Author: David J. Adams

Publisher: John Wiley & Sons

Published: 2011-10-17

Total Pages: 289

ISBN-13: 0470661569

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Effective Learning in the Life Sciences is intended to help ensure that each student achieves his or her true potential by learning how to solve problems creatively in laboratory, field or other workplace setting. Each chapter describes state of the art approaches to learning and teaching and will include case studies, worked examples and a section that lists additional online and other resources. All of the chapters are written from the perspective both of students and academics and emphasize and embrace effective scientific method throughout. This title also draws on experience from a major project conducted by the Centre for Bioscience, with a wide range of collaborators, designed to identify and implement creative teaching in bioscience laboratories and field settings. With a strong emphasis on students thinking for themselves and actively learning about their chosen subject Effective Learning in the Life Sciences provides an invaluable guide to making the university experience as effective as possible.


Active Learning in College Science

Active Learning in College Science

Author: Joel J. Mintzes

Publisher: Springer Nature

Published: 2020-02-23

Total Pages: 989

ISBN-13: 303033600X

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This book explores evidence-based practice in college science teaching. It is grounded in disciplinary education research by practicing scientists who have chosen to take Wieman’s (2014) challenge seriously, and to investigate claims about the efficacy of alternative strategies in college science teaching. In editing this book, we have chosen to showcase outstanding cases of exemplary practice supported by solid evidence, and to include practitioners who offer models of teaching and learning that meet the high standards of the scientific disciplines. Our intention is to let these distinguished scientists speak for themselves and to offer authentic guidance to those who seek models of excellence. Our primary audience consists of the thousands of dedicated faculty and graduate students who teach undergraduate science at community and technical colleges, 4-year liberal arts institutions, comprehensive regional campuses, and flagship research universities. In keeping with Wieman’s challenge, our primary focus has been on identifying classroom practices that encourage and support meaningful learning and conceptual understanding in the natural sciences. The content is structured as follows: after an Introduction based on Constructivist Learning Theory (Section I), the practices we explore are Eliciting Ideas and Encouraging Reflection (Section II); Using Clickers to Engage Students (Section III); Supporting Peer Interaction through Small Group Activities (Section IV); Restructuring Curriculum and Instruction (Section V); Rethinking the Physical Environment (Section VI); Enhancing Understanding with Technology (Section VII), and Assessing Understanding (Section VIII). The book’s final section (IX) is devoted to Professional Issues facing college and university faculty who choose to adopt active learning in their courses. The common feature underlying all of the strategies described in this book is their emphasis on actively engaging students who seek to make sense of natural objects and events. Many of the strategies we highlight emerge from a constructivist view of learning that has gained widespread acceptance in recent years. In this view, learners make sense of the world by forging connections between new ideas and those that are part of their existing knowledge base. For most students, that knowledge base is riddled with a host of naïve notions, misconceptions and alternative conceptions they have acquired throughout their lives. To a considerable extent, the job of the teacher is to coax out these ideas; to help students understand how their ideas differ from the scientifically accepted view; to assist as students restructure and reconcile their newly acquired knowledge; and to provide opportunities for students to evaluate what they have learned and apply it in novel circumstances. Clearly, this prescription demands far more than most college and university scientists have been prepared for.


Visible Learning for Science, Grades K-12

Visible Learning for Science, Grades K-12

Author: John Almarode

Publisher: Corwin Press

Published: 2018-02-15

Total Pages: 131

ISBN-13: 1506394191

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In the best science classrooms, teachers see learning through the eyes of their students, and students view themselves as explorers. But with so many instructional approaches to choose from—inquiry, laboratory, project-based learning, discovery learning—which is most effective for student success? In Visible Learning for Science, the authors reveal that it’s not which strategy, but when, and plot a vital K-12 framework for choosing the right approach at the right time, depending on where students are within the three phases of learning: surface, deep, and transfer. Synthesizing state-of-the-art science instruction and assessment with over fifteen years of John Hattie’s cornerstone educational research, this framework for maximum learning spans the range of topics in the life and physical sciences. Employing classroom examples from all grade levels, the authors empower teachers to plan, develop, and implement high-impact instruction for each phase of the learning cycle: Surface learning: when, through precise approaches, students explore science concepts and skills that give way to a deeper exploration of scientific inquiry. Deep learning: when students engage with data and evidence to uncover relationships between concepts—students think metacognitively, and use knowledge to plan, investigate, and articulate generalizations about scientific connections. Transfer learning: when students apply knowledge of scientific principles, processes, and relationships to novel contexts, and are able to discern and innovate to solve complex problems. Visible Learning for Science opens the door to maximum-impact science teaching, so that students demonstrate more than a year’s worth of learning for a year spent in school.


Problem-based Learning in the Life Science Classroom, K-12

Problem-based Learning in the Life Science Classroom, K-12

Author: Tom J. McConnell

Publisher:

Published: 2016

Total Pages: 0

ISBN-13: 9781941316207

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Problem-Based Learning in the Life Science Classroom, K- 12 offers a great new way to ignite your creativity. Authors Tom McConnell, Joyce Parker, and Janet Eberhardt show you how to engage students with scenarios that represent real-world science in all its messy, thought-provoking glory. The scenarios prompt K- 12 learners to immerse themselves in analyzing problems, asking questions, posing hypotheses, finding needed information, and then constructing a proposed solution. In addition to complete lesson plans supporting the Next Generation Science Standards, the book offers extensive examples, instructions, and tips. The lessons cover four categories: life cycles, ecology, genetics, and cellular metabolism. But Problem-Based Learning in the Life Science Classroom, K- 12 doesn' t just explain why, how, and when to implement problem-based learning (PBL). It also provides you with what many think is the trickiest part of the approach: rich, authentic problems. The authors facilitated the National Science Foundation- funded PBL Project for Teachers and used the problems in their own science teaching, so you can be confident that the problems and the approach are teacher tested and approved.


Make It Stick

Make It Stick

Author: Peter C. Brown

Publisher: Harvard University Press

Published: 2014-04-14

Total Pages: 330

ISBN-13: 0674729013

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To most of us, learning something "the hard way" implies wasted time and effort. Good teaching, we believe, should be creatively tailored to the different learning styles of students and should use strategies that make learning easier. Make It Stick turns fashionable ideas like these on their head. Drawing on recent discoveries in cognitive psychology and other disciplines, the authors offer concrete techniques for becoming more productive learners. Memory plays a central role in our ability to carry out complex cognitive tasks, such as applying knowledge to problems never before encountered and drawing inferences from facts already known. New insights into how memory is encoded, consolidated, and later retrieved have led to a better understanding of how we learn. Grappling with the impediments that make learning challenging leads both to more complex mastery and better retention of what was learned. Many common study habits and practice routines turn out to be counterproductive. Underlining and highlighting, rereading, cramming, and single-minded repetition of new skills create the illusion of mastery, but gains fade quickly. More complex and durable learning come from self-testing, introducing certain difficulties in practice, waiting to re-study new material until a little forgetting has set in, and interleaving the practice of one skill or topic with another. Speaking most urgently to students, teachers, trainers, and athletes, Make It Stick will appeal to all those interested in the challenge of lifelong learning and self-improvement.


Deep Learning for the Life Sciences

Deep Learning for the Life Sciences

Author: Bharath Ramsundar

Publisher: O'Reilly Media

Published: 2019-04-10

Total Pages: 236

ISBN-13: 1492039802

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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


A New Biology for the 21st Century

A New Biology for the 21st Century

Author: National Research Council

Publisher: National Academies Press

Published: 2009-11-20

Total Pages: 113

ISBN-13: 0309147867

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Now more than ever, biology has the potential to contribute practical solutions to many of the major challenges confronting the United States and the world. A New Biology for the 21st Century recommends that a "New Biology" approach-one that depends on greater integration within biology, and closer collaboration with physical, computational, and earth scientists, mathematicians and engineers-be used to find solutions to four key societal needs: sustainable food production, ecosystem restoration, optimized biofuel production, and improvement in human health. The approach calls for a coordinated effort to leverage resources across the federal, private, and academic sectors to help meet challenges and improve the return on life science research in general.


Enhancing Fieldwork Learning Using Mobile Technologies

Enhancing Fieldwork Learning Using Mobile Technologies

Author: Derek France

Publisher: Springer

Published: 2015-09-30

Total Pages: 168

ISBN-13: 3319209671

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This book shows how tablets (and smartphones) using a variety of selected ‘apps’, can enhance fieldwork and other out-of-classroom activities. The authors review imaginative uses of tablets from their own project and as well as examples from other colleagues. To help readers keep abreast of new technology and innovative ways to use it, the book is supported by a web site and a social media community.


Machine Learning in Biotechnology and Life Sciences

Machine Learning in Biotechnology and Life Sciences

Author: Saleh Alkhalifa

Publisher: Packt Publishing Ltd

Published: 2022-01-28

Total Pages: 408

ISBN-13: 1801815674

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Explore all the tools and templates needed for data scientists to drive success in their biotechnology careers with this comprehensive guide Key FeaturesLearn the applications of machine learning in biotechnology and life science sectorsDiscover exciting real-world applications of deep learning and natural language processingUnderstand the general process of deploying models to cloud platforms such as AWS and GCPBook Description The booming fields of biotechnology and life sciences have seen drastic changes over the last few years. With competition growing in every corner, companies around the globe are looking to data-driven methods such as machine learning to optimize processes and reduce costs. This book helps lab scientists, engineers, and managers to develop a data scientist's mindset by taking a hands-on approach to learning about the applications of machine learning to increase productivity and efficiency in no time. You'll start with a crash course in Python, SQL, and data science to develop and tune sophisticated models from scratch to automate processes and make predictions in the biotechnology and life sciences domain. As you advance, the book covers a number of advanced techniques in machine learning, deep learning, and natural language processing using real-world data. By the end of this machine learning book, you'll be able to build and deploy your own machine learning models to automate processes and make predictions using AWS and GCP. What you will learnGet started with Python programming and Structured Query Language (SQL)Develop a machine learning predictive model from scratch using PythonFine-tune deep learning models to optimize their performance for various tasksFind out how to deploy, evaluate, and monitor a model in the cloudUnderstand how to apply advanced techniques to real-world dataDiscover how to use key deep learning methods such as LSTMs and transformersWho this book is for This book is for data scientists and scientific professionals looking to transcend to the biotechnology domain. Scientific professionals who are already established within the pharmaceutical and biotechnology sectors will find this book useful. A basic understanding of Python programming and beginner-level background in data science conjunction is needed to get the most out of this book.