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The roadmap of all my learning, statistics, and biology articles.

We all want to learn. But some popular ideas of “how learning works” are downright wrong, so the best approaches are overlooked.

Interpreting statistics is an important skill. But in my experience as a TA for intro stats, I’ve seen how some concepts are easily confused.

And as a neuroscience major, I’ve been taught a breadth of valuable biology topics. I notice they haven’t necessarily translated well in popular science, though.

I’m here to clear things up. Here’s the rundown:

To learn, a learner needs to store a well-developed web of relevant knowledge in long-term memory.

• How we process new information is shaped by the prior contents of our long-term memory. The more you know, the more you can know. (“Unlimited Long-Term Memory, Limited Working Memory: How This Affects Learning”)
• When students are frustrated with school, it’s usually because they don’t have the right knowledge base for the new content to stick. If they can’t get the satisfaction of success, it feels pointless. Learning doesn’t have to be “relevant” to be interesting; just by changing the expected mental payoff, it can go from frustrating to fun. (“We All Like to Learn — Why Don’t We Like Learning for School?”)
• Direct instruction eases the load on working memory so new information can more easily settle into long-term memory. This isn’t just “passive” learning. It doesn’t stifle “critical thinking.” In fact, a solid knowledge base is what you need for critical thinking, and it’s hard to build that if you have to figure everything out yourself. (“Passive Learning is Underrated”)
• Combine direct instruction methods with a rich knowledge-based curriculum, and you have what’s known as traditional education. This is opposed to progressive education, which emphasizes student-centered discovery and creativity. The names may be misleading, because this disagreement isn’t political — the same approach can be supported for different political reasons! (“‘Traditional’ vs. ‘Progressive’ Education: Time for New Labels?”)
• “Traditional” education gets a bad rap. It calls for memorizing isolated facts, critics say. It’s just non-interactive lecturing. It’s outdated, failing to prepare learners for the future. Here’s why I disagree. (“5 Myths About ‘Traditional’ Knowledge-Centered Education”)
• Critical thinking shortcuts, such as identifying logical fallacies, don’t really help thinkers figure out what’s true. An informed viewpoint requires specific knowledge — and traditional school subjects do help with this. (“Teaching Logical Fallacies Isn’t Teaching How to Think”)
• Why are “academic subjects” taught, rather than more transferable life skills? Because skills like socializing and problem-solving are biologically primary, thus learned naturally on the individual’s own time frame, whereas more recent developments in knowledge are biologically secondary, so they need to be explicitly passed on. (“Why Some Skills Can’t Be Taught in Schools: They’re Biologically Primary”)
• Well-designed multiple choice questions are a good tool to assess knowledge; other measures are often confounded. Do such questions promote “closed-ended” thinking? Too bad, because reality is filled with closed-ended questions. People aren’t going to come up with “creative” ideas if they can’t understand the constraints they’re working with — and if they can’t apply it in a multiple choice question, they likely don’t understand. Your thinking can’t be shaped by the knowledge you don’t have. (“What Makes a Good Multiple Choice Question?”)
• Is the future of education “personalized”? Maybe. It sure would help, when different learners enter with such different levels of prior knowledge. But instructor-tailored and student “choice”-driven methods have consistently been found ineffective. New educational technology can eventually fill the gap. (“Differentiated Instruction: Not a Solution to Student Struggle (Yet?)”)
• Just because an approach claims to be “brain-based” doesn’t mean it’s more scientifically supported. Many neuroscience findings have been simplified or mistranslated to the public. And even when interpreted right, seeing what’s going in the brain doesn’t tell us which interventions would work on the behavioral level. (“Beware the Claims of ‘Brain-Based’ Learning Programs”)

To interpret statistics appropriately, be aware of what statistical vocabulary means and what you can (and can’t) infer from a figure.

• Some everyday words have different meanings in statistical contexts — such as association, bias, error, independent, random, and spread. (“Everyday Words That Mean Something Else in Statistics”)
• Many intro stats students get bias and lurking variables get confused. With bias, you can’t necessarily generalize a statistic to the population; with lurking variables, you can’t necessarily infer a cause-and-effect relationship. (“Bias vs. Lurking Variables — What’s the Difference?”)
• Common ways statistical reasoning goes wrong? Measures of the wrong thing, wrong assumption of significance (of whether a “difference” makes a real difference), and wrong comparisons of proportions. (“3 Common Pitfalls in Amateur Statistical Reasoning”)

Taking scientific narratives at face value without understanding the underlying science leads to rigid misinterpretations.

• Many are familiar with the terms “dominant” and “recessive,” but aren’t sure exactly what makes something dominant or recessive, or how traits may be the result of more complex interactions. (“What are ‘Dominant’ and ‘Recessive’ Traits? The Rundown of Heredity”)
• When you unpack the highfalutin neurotransmitter terminology from popular neuroscience, you’ll find that it’s often just familiar psychology. The vocabulary gives an air of scientific credibility, but when we’re talking about behavioral inputs and behavioral outputs, it’s redundant to describe the chemical in-between. Describing the neurochemical processes associated with them often tells us nothing new or interesting about these constructs in practical matters of life. (“The Problem with Pop Neuroscience”)
• Vaccine skeptics aren’t ignorant of what major health organizations say. They have other concerns that they feel are being ignored and trivialized — that’s what needs to be addressed. (“What You Need to Stop Doing to Vaccine Skeptics”)
• Legitimate experts don’t want to waste their time explaining why they disagree with alternative COVID-19 theories that they can see as obviously ridiculous. But they don’t seem so ridiculous to everyone. I think these deserve to be seriously addressed. Censoring them just makes it look as if science has something to hide. The criticism of germ theory in The Contagion Myth is interesting, but ultimately, with its gaps in causal reasoning, I can’t conclude contagion is a myth. (“The Contagion Myth: A Review”)

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