The books and other resources below come in rough order of introductory-to-advanced within each section. I’m not saying I agree with every single word of every one of them, but they are great resources to learn from.
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Learning and Instruction
Make It Stick: The Science of Successful Learning by Peter Brown, Henry Roediger, and Mark McDaniel. A readable introduction to some of the most fundamental findings in learning science.
Peak: Secrets from the New Science of Expertise by Anders Ericsson and Robert Pool. A readable introduction to the idea of deliberate practice and how we might improve our practice even when learning on our own.
The ABCs of How We Learn: 26 Scientifically Proven Approaches, How They Work, and When to Use Them by Daniel Schwartz, Jessica Tsang, and Kristen Blair. A particularly useful collection of short essays on different approaches in learning and instruction that excels in contextualizing and explaining each approach.
How Learning Works: 7 Research-Based Principles for Smart Teaching by Susan Ambrose et al. It can be a little tough to read at times, but contains lots of good advice structured around specific problems that teachers regularly encounter.
Retrieval Practice by Pooja Agarwal. An excellent website for teachers on incorporating more science-based practices into your teaching. Agarwal is a researcher who focuses on applying psychological research to the classroom and has collaborated with teachers – notably Patrice Bain – to do so effectively.
The Carl Wieman Science Education Initiative (CWSEI). Carl Wieman is a nobel-prize winning physicist who works extremely hard to improve undergraduate programs in math and science. This website is the result of his first major project, at the University of British Columbia, to reform how science was taught there. Lots of accessible and actionable information if you are an undergraduate science teacher and want to up your game. If you’re interested in the process of reform, you might also consider reading his book Improving How Universities Teach Science.
The Cambridge Handbook of Expertise and Expert Performance edited by Anders Ericsson et al. An academic compendium of the research on expertise. More focused on the nature of expertise and expertise development than instructional practices.
The Psychology of Learning and Motivation. This book series solicits articles from the most knowledgeable people in the field on their specialties. Each author or group of authors writes comprehensive chapters on a single topic. Probably the best resource for quickly understanding the state of the field for a particular topic. Among my favorite chapters are:
- Marsh, E. J., Cantor, A. D., & M. Brashier, N. (2016). Believing that Humans Swallow Spiders in Their Sleep. In Psychology of Learning and Motivation (Vol. 64, pp. 93–132). Elsevier. https://doi.org/10.1016/bs.plm.2015.09.003.
- Rittle-Johnson, B., & Star, J. R. (2011). The Power of Comparison in Learning and Instruction: Learning Outcomes Supported by Different Types of Comparisons. In Psychology of Learning and Motivation (Vol. 55, pp. 199–225). Elsevier. https://doi.org/10.1016/B978-0-12-387691-1.00007-7.
- Shah, P., Michal, A., Ibrahim, A., Rhodes, R., & Rodriguez, F. (2017). What Makes Everyday Scientific Reasoning So Challenging? Psychology of Learning and Motivation. http://www.sciencedirect.com/science/article/pii/S0079742116300214.
- Delaney, P. F., Verkoeijen, P. P. J. L., & Spirgel, A. (2010). Spacing and Testing Effects. In Psychology of Learning and Motivation (Vol. 53, pp. 63–147). Elsevier. https://doi.org/10.1016/S0079-7421(10)53003-2.
- Roediger III, H. L., Putnam, A. L., & Smith, M. A. (2011). Ten Benefits of Testing and Their Applications to Educational Practice. In Psychology of Learning and Motivation (Vol. 55, pp. 1–36). Elsevier. https://doi.org/10.1016/B978-0-12-387691-1.00001-6.
Tacit and Explicit Knowledge by Harry Collins. Challenging read, but a good book. I made a video summarizing the main points, as I see them.
Scientific Reasoning and the History of Science
The Hunt for Vulcan: … and How Albert Einstein Destroyed a Planet, Discovered Relativity, and Deciphered the Universe by Thomas Levenson. A great story about the search for a planet that would explain Mercury’s strange orbit in Newtonian terms.
Are We Smart Enough to Know How Smart Animals Are? by Frans de Waal. This is a book about the history of research on animal intelligence, but it’s also a book about cultures of scientific research and the interaction between research methods and research concepts.
The Science Wars: The Battle over Knowledge and Reality by Steven Goldman. A history, of sorts, of conflicting ideas about what scientific knowledge is. If you prefer the material in a lecture format, check out his Teaching Company lecture series “The Science Wars: What Scientists Know and How They Know It”. His other lectures there are fantastic, too.
Everything is Obvious*: How Common Sense Fails Us by Duncan Watts. An applied mathematician turned social scientist, Duncan Watts always brings his A game. This is a book about the challenges of social science, but also about how intuitions and research findings interact.
The Social Construction of What? by Ian Hacking. If you are at all familiar with the “Science Wars” of the 90s or struggle to reconcile realist philosophical positions with social constructivist ones, this book is a boon. Also look up Ian Hacking and the Styles of Scientific Reasoning.
Thing Knowledge by Davis Baird. This is a tremendously interesting book about the different roles that objects play in scientific reasoning – as measurement devices, as models, as demonstrations.
The Invention of Science: A New History of the Scientific Revolution by David Wootton. Just a great overview of science in Europe during the 1600s. I need to re-read this.
Reasoning and Decision-Making
Thinking, Fast and Slow by Daniel Kahnemann. Kahnemann synthesizes 50 years of research on decision-making. Excellent, readable.
How We Reason by Philip Johnson-Laird. Probably the single best book on human reasoning that I know of. He synthesizes a wealth of studies to advocate for his take on mental models in reasoning and how they work. Another book that I frequently return to.
Learning Causality in a Complex World: Understandings of Consequence by Tina Grotzer. A more academic book, but worth reading, especially if you’re in science education.
Understanding and Using Scientific Evidence by Richard Gott and Sandra Duggan. Gott, Duggan, and their colleagues have done a ton of work in analyzing scientific reasoning. Their website also has a lot of solid information.
What Intelligence Tests Miss: The Psychology of Rational Thought by Keith Stanovich. A pointed critique on whether intelligence tests really measure what matters in problem solving and decision-making.
Research and Policy-making
The Tyranny of Metrics by Jerry Z. Muller. An excellent overview of the various dangers of using metrics to incentivize behavior. I wish every policy-maker read this.
Bad Science: Quacks, Hacks, and Big Pharma Flacks by Ben Goldacre. Goldacre has been fighting against nonsense science and bad science journalism for decades at this point. This book in particular emphasizes how science words and concepts are manipulated by various companies for profit. His blog also has tons of great material.
Measuring Up: What Educational Testing Really Tells Us by Daniel Koretz. A patient history of the growth of standardized testing in the States and discussion of how the persistent misinterpretations of standardized test scores warp public policy and our educational system as a whole.
Cadillac Desert: The American West and its Disappearing Water by Marc Reisner. A book my wife and I simply dubbed, “The Depressing Water Book”. A blistering dissection of the stupidity and avarice of water policy in the States.
Statistics as Principled Argument by Robert P. Abelson. This is the best introductory book I know of for understanding statistics conceptually. Contains plenty of memorable examples.
Regression and Other Stories by Andrew Gelman, Jennifer Hill, and Aki Vehtari. If I could have only one statistics textbook, this is what I would pick. Clear writing, sensible advice — I learn from it often. You can also check out Andrew Gelman’s blog if you want to spend days wading down the rabbit hole of statistics debates. There’s many, many lessons there on interpreting statistical evidence.
Write It Up and How to Write a Lot by Paul Silvia. These are both practical guides on writing in academia — particularly in social science. Excellent books that will improve your academic writing regardless of discipline.
On Writing Well by William Zinsser. One of the classic books on non-fiction writing. Highly recommended.
Artificial Intelligence: A Guide for Thinking Humans by Melanie Mitchell. One of the clearest discussions of modern forms of AI, what they can do, and what they can’t do, and why.
Artifictional Intelligence by Harry Collins. An extended discussion of the limitations of current models of AI (including large language models, “deep learning” methods, GPT-4, etc.).