This book explores cultivating a scientific mindset for critical thinking and informed decision-making. It guides educators in shifting to student-centered, evidence-based STEM instruction, aligning with modern educational frameworks in the Age of Inference.
In an age where we are inundated with information, the ability to discern verifiable information to make proper decisions and solve problems is ever more critical. Modern science, which espouses a systematic approach to making 'inferences,' requires a certain mindset that allows for a degree of comfort with uncertainty.
This book offers inspirations and ideas for cultivating the proper mindset for the studying, teaching, and practicing of science that will be useful for those new to as well as familiar with the field. Although a paradigm shift from traditional instruction is suggested in the National Framework for K-12 science, this volume is intended to help educators develop a personal mental framework in which to transition from a teacher-centered, didactical approach to a student-centered, evidence-guided curriculum.
While the topics of the book derive from currently published literature on STEM education as they relate to the National Framework for K-12 Science and the Three-Dimensional science instruction embedded in the Next Generation Science Standards, this book also examines these topics in the context of a new societal age posited as the 'Age of Inference' and addresses how to make sense of the ever-increasing deluge of information that we are experiencing by having a scientific and properly discerning mindset.
Recenzijas
This volume takes on one of the thorniest existential problems of our time, the contradiction between the exponentially growing amount of information that individuals have access to, and the diminished capacity of those individuals to understand it. Its chapters provide the reader with an introduction to the relationship between knowledge, science, and inference; needed new approaches to learning science in our new data rich world; and a discussion of what we can and must do to reduce or eliminate the growing gap between the inference have's and have nots. It is not too much to say that how we resolve the issues outlined in this volume will determine the future of our species on this planet."" Joseph L. Graves Jr., Professor of Biological Sciences North Carolina A&T State University, Fellow, American Association for the Advancement of Science: Biological Sciences, Author of: The Emperor's New Clothes: Biological Theories of Race at the Millennium
""Big data is not enough for addressing dangers to the environment or tackling threats to democracy; we need the ability to draw sound inferences from the data. Cultivating a scientific mindset requires fundamental changes to the way we teach and learn. This important and well -written volume shows how."" Ashok Goel, Professor of Computer Science and Human Centered Computing, Georgia Institute of Technology. Editor of AI Magazine Founding Editor of AAAI's Interactive AI Magazine
""If you are a science teacher concerned about the implications of information overload, analysis paralysis, and intellectual complacency on our health, economic future, and democracy, then I recommend this book."" Michael Svec, Professor for Physics and Astronomy Education, Furman University, Fulbright Scholar to Czech Republic
Foreword; Miranda Feliciano Tyson.
Acknowledgments and Dedication.
Section I. Introduction: The Nature Of Knowledge, Science, And Inference.
Chapter
1. The Age of Inference; John R. McConnell, Sarah B. Dugger, and
Philip C. Short.
Chapter
2. Nature of the Scientific Enterprise; Harvey Henson.
Chapter
3. The Nature of Science Education: Relativity of Theory; Philip C.
Short.
Chapter
4. Mathematics: Language, Modeling, and Comparison Assisting
Inference; Mary Barone Martin, Tammy Jones, Dovie Kimmins, and Teresa
Schmidt.
Chapter
5. Statistics: Developing Impactful Teaching and Learning; Mary
Barone Martin, Tammy Jones, and Teresa Schmidt.
Chapter
6. Statistics: Assessing Success in the Inference Process; Mary
Barone Martin, Tammy Jones, and Teresa Schmidt.
Section II. Application: Framework For Science In A Data-rich World.
Chapter
7. Children's Literature Resources to Support Authentic Science
Practices and Environmental Decision-Making: The Conservation Tales Series;
Tom J. McConnell and Barbara Giorgio-Booher.
Chapter
8. Supporting Statistical Literacies in the Context of a Data
Visualization Project With Elementary Students; Lynn Hodge, Joy Bertling, and
Shande King.
Chapter
9. Green Literacy K-5: Nurturing a Scientific Mindset; Jennifer
Cullerton Johnson and Mary K. Gove.
Chapter
10. Complex Multimodal Text Sets to Support Science Literacy; William
Romine, Amy Lannin, Torrey Palmer, Delinda van Garderen, Rachel Juergensen,
Cassandra M. Smith, and William Folk.
Chapter
11. Power Up or Put Away? Using Mobile Phones for Authentic Student
Investigations; Kristin T. Rearden and Blanche O'Bannon.
Chapter
12. Self-Regulated Learning Theory to Build Scientific Mindsets for
Diversity in STEM; Giuseppina Mattietti and Erin E. Peters-Burton.
Chapter
13. A Case for the Model-Based Reasoning Classroom; Arthur Beauchamp
and Cynthia Passmore.
Chapter
14. Cultivating a Scientific Mindset Through Inquiry-Based Science
Learning Utilizing Nasa Resources for Educators Bridging the Disconnect
Between How We Do and Teach Science: Cultivating a Scientific Mindset in an
Era of Data-Driven Education; Jana Bouwma-Gearhart.
Chapter
15. The Scientific Method Card Game: Applications for Any Educational
Context; Kallina M. Dunkle.
Chapter
16. Toward a Design-Centered Scientific Mindset: Closing the
Opportunity Gaps via 3D Design and Printing in STEM Teacher Education;
Lingguo Bu, Harvey Henson, and Euginia Nyirenda.
Section III. Conclusion: The Future For Inferences And Actions.
Chapter
17. Developing Critical Literacy Skills Among Diverse Learners During
the Age of Inference; Laveria F. Hutchison.
Chapter
18. Content and Pedagogical Knowledge for Teaching Confidence
Intervals in a Post p < 0.05 World; Jennifer J. Kaplan and Kristen E.
Roland.
Chapter
19. The Multidimensional Learning Goals for Making Inferences With
Data; Ryan Seth Jones, Anna Strimaitis Grinath, and Fonya Scott.
Chapter
20. Expectations and Disciplinary Blends; L. Jeneva Clark, R.
Alexander Bentley, Nicholas N. Nagle, and Vasileios Maroulas.
Chapter
21. Machine Learning: A New Lens for Integrating Computational
Thinking and Science in the High School Classroom; Michael Daley, Zhen Bai,
Raffaella Borasi, and Dave Miller.
Chapter
22. Becoming a Postmodern STEM Teacher Leader in the Age of
Inference; Rebekah Hammack.
Chapter
23. Seeking Homeostasis in a Heteroscedastic World: A Sense of the
Stakes in the Age of Inference; Philip C. Short, Donna F. Short, and John R.
McConnell.
References.
About the Editors.
About the Contributors.
Philip C. Short, Austin Peay State University
Harvey Henson, Southern Illinois University
John R. McConnell, Austin Peay State University
