Why Don't Students Like School?

Brain Designed to Avoid Thinking

The human brain is not primarily designed for thinking. Rather, it is optimized to avoid the effortful and unreliable process of thinking whenever possible. Our brains are wired to rely on memory and automatic responses to handle most everyday tasks and problems.

This fundamental insight has important implications for education. Rather than constantly pushing students to "think harder," teachers should focus on creating learning environments that align with the brain's natural tendencies. This means providing opportunities for students to build up relevant knowledge and skills, so they can draw on memory and automation to handle many challenges, reserving their limited "thinking" capacity for more complex problems.

By understanding that the brain is not an endless wellspring of cognitive power, but rather a system designed to conserve mental effort, educators can design more effective and engaging learning experiences. The goal should be to harness the brain's strengths - its remarkable abilities in perception, memory, and pattern recognition - while minimizing the need for the brain's weaker capacity for effortful, unreliable thinking.

Here are some examples from the context that support the key insight that the human brain is designed to avoid thinking:

• The context states that "Thinking is slow and unreliable" and that "Thinking is not only effortful, as Ford noted, it's also slow and unreliable." This suggests the brain is not well-suited for constant thinking.

• It notes that "Your brain serves many purposes, and thinking is not the one it serves best." The brain is better designed for other functions like seeing and moving, rather than effortful thinking.

• The context compares human thinking abilities to computers, stating that "Five dollars will get you a calculator that can perform simple calculations faster and more accurately than any human can" and "With fifty dollars you can buy chess software that can defeat more than 99 percent of the world's population." This highlights how the brain is not optimized for certain types of thinking tasks.

• It provides the example of learning to drive a car, where initially "I didn't even listen to the radio while I drove, for fear of being distracted." But with practice, "the process of driving became automatic, and now I don't need to think about those small-scale bits of driving any more than I need to think about how to walk." This shows how the brain tries to automate tasks to avoid constant thinking.

The key point is that the human brain, contrary to common perceptions, is not primarily designed for effortful thinking, but rather tries to automate and avoid thinking wherever possible. The brain is better suited for other functions like perception and movement.

Knowledge as a Foundation for Critical Thinking

Factual knowledge is the foundation for critical thinking. Without a solid base of information, students cannot effectively engage in higher-order cognitive skills like reasoning, problem-solving, and analysis.

Trying to develop critical thinking without first building knowledge is misguided. Thinking well requires having something to think about - facts, concepts, and information stored in long-term memory. These knowledge stores enable the cognitive processes that teachers value most, like evaluating evidence and drawing insightful conclusions.

The human mind does not work like a calculator, where thinking skills can be applied to any data. Rather, critical thinking is intimately tied to the specific background knowledge a person possesses. Improving students' ability to think critically must go hand-in-hand with expanding their factual knowledge. Focusing solely on skill development without ensuring adequate knowledge acquisition is a flawed approach.

Here are examples from the context that support the key insight that knowledge is a crucial foundation for critical thinking:

• The context states that "critical thinking is not a set of procedures that can be practiced and perfected while divorced from background knowledge." It gives the example of a teacher asking her 4th grade class about living in a rainforest - the students could only give shallow responses until they had built up more background knowledge on the topic.

• The context emphasizes that "shallow knowledge is better than no knowledge" - even a basic understanding can aid comprehension and critical thinking, even if deep knowledge is ideal.

• The context highlights how "reading exposes children to more facts and a broader vocabulary than virtually any other activity" and that "people who read for pleasure enjoy cognitive benefits throughout their lifetime." This supports the idea that building knowledge through reading is key.

• The context notes that "before a child meets her first teacher, she may be quite far behind the next child in terms of how easy it is going to be for her to learn" due to differences in home environments and prior knowledge acquisition. This underscores the importance of building that foundational knowledge early on.

• The context contrasts the "calculator" model of thinking, where procedures can be applied to any data, with the reality that "critical thinking processes are tied to background knowledge." This directly supports the key insight.

Cognitive Limitations Shape Learning

Cognitive limitations shape learning. The human mind has a limited working memory, which constrains our ability to process and retain information. Effective teaching must account for these limitations.

By understanding the mind's natural workings, educators can design learning experiences that strategically manage cognitive constraints. For example, automating basic skills through practice frees up working memory for higher-order thinking. Providing relevant background knowledge also reduces the cognitive load on students.

Tailoring instruction to accommodate cognitive limitations maximizes the chances that students will learn and retain information effectively. This insight should guide teaching strategies across all subjects and grade levels. Leveraging the mind's natural capacities is key to fostering deep, lasting learning.

Here are examples from the context that support the key insight that cognitive limitations shape learning:

• The context describes how teaching is a "cognitive skill" that places demands on working memory, just like other complex cognitive tasks. It states that "formal experiments confirm this strong intuition; teaching is quite demanding of working memory."

• The context explains that "thinking is the putting together of information in new ways" and that this happens in the "staging ground of thought" - working memory. It notes that working memory has "limited space" and that we need to be mindful of this limitation when designing educational experiences.

• The example is given of a teacher who assigned students to draw pictures to represent plot elements in a book. The context states that this assignment led the students to focus more on drawing the pictures rather than thinking about the relationships between the plot elements. This illustrates how the design of an assignment can either leverage or fail to account for the limitations of working memory.

• The context emphasizes the importance of "factual knowledge" and "procedural knowledge" stored in long-term memory for effective teaching, as these provide the necessary building blocks that can be flexibly combined in working memory.

In summary, the context highlights how an understanding of cognitive limitations, such as the constraints of working memory, can and should shape effective teaching strategies that maximize student learning.

Impact of Expertise on Perception

Experts perceive information differently than novices. Experts have structured knowledge and experienced cognitive pathways that allow them to quickly identify the deep, functional relationships in a problem or situation. In contrast, novices tend to focus on surface-level details and struggle to see the underlying principles.

This insight is crucial for effective teaching. Rather than just exposing students to information, educators should focus on developing the cognitive pathways that allow students to think like experts. This means providing structured learning experiences that give students practice applying knowledge in meaningful ways, not just memorizing facts.

By understanding how expertise shapes perception, teachers can design instructional approaches that help students build the mental models and problem-solving skills of true experts. This shift from passive information delivery to active skill development is key to preparing students to think critically and tackle complex challenges.

Here are examples from the context that support the key insight about how expertise impacts perception:

• Experts group chess pieces based on function, not proximity: In an experiment, chess experts grouped chess pieces based on how they were strategically related, while novices grouped them based on their physical proximity on the board.

• Experts sort physics problems based on principles, not objects: Physics experts sorted physics problems based on the underlying physical principles needed to solve them, while novices sorted them based on the objects involved (e.g. springs, inclined planes).

• Experts define classroom problems before solving them: Expert teachers first seek to define and understand a classroom management problem, while novice teachers jump straight into trying to solve it.

• Experts make permanent changes, not just address the immediate issue: An expert teacher is more likely than a novice to make a permanent change, like rearranging seating assignments, to address the root cause of a classroom problem.

The key point is that experts perceive problems and situations at a deeper, more abstract level, focusing on the underlying functional relationships and principles, rather than just the surface features. This allows them to better understand the problem, generate appropriate solutions, and transfer their knowledge to new situations. In contrast, novices tend to focus on the specific details and objects involved.

The Power of Stories in Learning

Stories are psychologically privileged - the human mind is uniquely tuned to understand and remember them. This means stories are treated differently in memory compared to other types of information.

Organizing a lesson plan like a story can be an effective way to help students comprehend and remember the material. The key elements of a story structure are causality (events are causally related), conflict (a protagonist pursuing a goal faces obstacles), complications (subproblems that arise), and character (strong, interesting personas shown through action).

Using this story structure doesn't mean the teacher has to lecture or tell stories the whole time. The structure applies to how the teacher organizes the material for students to think about, not necessarily the teaching methods used. Structuring lessons this way can make the content more engaging and memorable for students.

Here are examples from the context that support the key insight about the power of stories in learning:

• The book states that stories are "psychologically privileged", meaning they are "treated differently in memory than other types of material." This suggests stories have a special cognitive advantage for comprehension and retention.

• The book outlines 4 key principles of story structure: causality (events are causally related), conflict (a protagonist pursuing a goal faces obstacles), complications (subproblems that arise), and character (strong, interesting characters shown through action).

• The book explains how this story structure helps communication, as the audience "knows the structure, which helps to interpret the action" and stories are "consistently rated as more interesting than other formats."

• The book provides the example of the Star Wars story, where Luke Skywalker's goal to deliver stolen plans is met with obstacles like needing transportation off his home planet, leading to complications like meeting Han Solo. This story structure engages the audience.

• The book contrasts a simple chronological telling of events versus one with causal connections, noting the latter is more engaging as the audience "must figure out that Luke intends to pretend Chewbacca is a prisoner" based on the story structure.

In summary, the context highlights how the psychological advantages of stories, with their causal, conflicting, complicated narratives centered on compelling characters, can be leveraged in teaching to enhance student engagement and learning.

Rejection of Learning Styles Theory

The book rejects the learning styles theory, which claims that students learn best when taught according to their individual cognitive styles (e.g. visual, auditory, kinesthetic). Instead, it argues for teaching methods that are universally engaging and effective, based on the principles of cognitive science.

The learning styles theory is popular in education, but lacks empirical support. Cognitive scientists have not been able to reliably identify distinct learning styles that consistently predict how a student will learn best. The theory also fails to show that students with different styles differ in ability on average.

In contrast, the book promotes teaching approaches grounded in cognitive science. These methods focus on ensuring students think about the meaning and structure of the material, rather than catering to presumed style preferences. The goal is to design lessons that maximize student engagement and learning for all students, not just those who fit a particular style.

The key insight is that effective teaching should be based on universal principles of how the mind works, not unproven theories about individual differences in cognitive style. By understanding and applying cognitive science, teachers can create learning experiences that benefit all students, regardless of their presumed learning preferences.

Here are examples from the context that support the key insight of rejecting learning styles theory:

• The context states that the visual-auditory-kinesthetic learning styles theory is "widely believed, even though psychologists know that the theory is not right." This directly contradicts the popular educational theory.

• The context explains that the evidence for learning styles theories is "mixed at best" and that "after decades of trying, psychologists have not been able to find" cognitive styles that meet the criteria of being stable, consequential, and distinct from abilities.

• The context provides the example of the "rod-and-frame test" and "embedded-figures test" which were thought to measure field dependence/independence as a cognitive style, but were found to actually measure an ability rather than a style.

• The context states that cognitive styles theories fail to show that "people with different styles do not, on average, differ in ability" which is a key criteria for a valid cognitive styles theory.

• The context argues that trying to make material "relevant to students' interests" is not an effective way to engage them, contradicting popular teaching methods based on learning styles.

In summary, the context provides multiple examples and explanations that directly challenge the effectiveness of teaching based on presumed learning styles, and instead promotes teaching methods grounded in cognitive science principles.

Quotes

Let's take a look at some key quotes from "Why Don't Students Like School?" that resonated with readers.

Memory is the residue of thought.

This quote means that what we remember is determined by what we focus our thoughts on. When we think about certain ideas or experiences, they leave a trace in our memory, making it easier for us to recall them later. Therefore, directing our attention to valuable information and concepts will improve our ability to retain and recall them.

Thinking is the hardest work there is, which is the probable reason why so few people engage in

The quote means that thinking is a challenging mental activity, which is likely why not many people put in the effort to do it. It suggests that thinking requires a significant amount of concentration and effort, and therefore, it's not something that many people are willing to do regularly.

People are naturally curious, but we are not naturally good thinkers; unless the cognitive conditions are right, we will avoid thinking.

The quote means that people usually have a strong desire to learn new things, but they may not naturally excel at deep, focused thinking. If the conditions aren't ideal, individuals tend to avoid engaging in such mentally demanding activities. This implies that creating the right environment and conditions is crucial to foster effective thinking and learning.

Chapter Notes

Introduction

• The Brain's Complexity: The human brain is an incredibly complex organ, with its three-pound mass of cells being the source of the greatest mysteries in the universe. Despite its complexity, we have learned more about how the mind works in the last 25 years than in the previous 2,500 years.

• The Gap between Research and Practice: While cognitive science has made significant advancements in understanding the mind, the application of this knowledge in the classroom has been limited. This gap is understandable, as cognitive scientists often isolate mental processes in the laboratory, whereas in the classroom, these processes interact in complex and unpredictable ways.

• The Limitations of Applying Laboratory Findings to the Classroom: An example of this is the finding that repetition helps learning. While this is true in the laboratory, applying it directly to the classroom can be counterproductive, as excessive repetition can lead to decreased motivation and a lack of learning.

• The Nine Principles of the Mind's Operation: The book "Why Don't Students Like School?" presents nine principles that are fundamental to the mind's operation and can be reliably applied to classroom situations. These principles are not meant to be surprising, but rather to have important implications for teaching.

• Viewing the Human Species as Bad at Thinking: One of the surprising implications of these principles is that it may be more useful to view the human species as bad at thinking, rather than as cognitively gifted.

• The Importance of Factual Knowledge: The book emphasizes the importance of factual knowledge, as authors often write only a fraction of what they mean, and students need to have a strong foundation of factual knowledge to understand and interpret this information.

• Harnessing the Ease of Learning: The book explores why people can easily remember the plot of "Star Wars" without trying, and how teachers can harness this ease of learning for their classrooms.

• Avoiding the Trap of Thinking Like Real Scientists: The book advises against trying to get students to think like real scientists, as this can be counterproductive and lead to a lack of understanding.

• Communicating the Truth about Intelligence: The book discusses the common misconception that intelligence is inherited from parents, and the importance of communicating the truth about this to students.

• The Two Goals of the Book: The book aims to tell readers how their students' minds work and to clarify how to use that knowledge to become better teachers.

Chapter 1 - Why Don’t Students Like School?

• The brain is not designed for thinking: The brain is designed to save you from having to think, as thinking is slow and unreliable. The brain is not naturally good at thinking.

• People enjoy mental work if it is successful: People like to solve problems, but not to work on unsolvable problems. If schoolwork is always just a bit too difficult for a student, they are unlikely to enjoy it.

• People are naturally curious, but not naturally good thinkers: Unless the cognitive conditions are right, people will avoid thinking. This is the cognitive principle that guides the chapter.

• Teachers should reconsider how they encourage students to think: To maximize the likelihood that students will get the pleasurable rush that comes from successful thought, teachers should reconsider how they encourage their students to think.

• Difficulty of schoolwork is a key factor in student enjoyment: If schoolwork is always just a bit too difficult for a student, it should be no surprise that they don't like school much. The difficulty of the work is a key factor in whether students enjoy school.

People Are Naturally Curious, but Curiosity Is Fragile

Here are the key takeaways from the chapter:

• Humans are not primarily designed for thinking: The chapter argues that the human brain is not primarily designed for thought, but rather for other functions like vision and movement, which it performs more efficiently and reliably than thinking.

• Thinking is slow, effortful, and uncertain: The chapter provides the example of the candle problem to illustrate that thinking is a slow, effortful, and uncertain process, unlike the immediate and reliable functioning of the visual system.

• We rely on memory rather than thinking: To avoid the difficulties of thinking, humans rely heavily on memory to guide their actions and decisions, using past experiences and strategies rather than reasoning from scratch.

• Repeated tasks become automatic: The chapter explains that with practice, thought-demanding tasks can become automatic, as the brain changes to perform them without conscious thought, as seen in the example of learning to drive a car.

• We have a natural curiosity to think, but the conditions must be right: Despite the difficulties of thinking, humans do have a natural curiosity to engage in certain types of thought, but this curiosity will only thrive if the conditions are favorable, otherwise, we tend to avoid thinking.

How Thinking Works

Here are the key takeaways from the chapter:

• People Enjoy Mental Activity: Even though the brain is not optimized for efficient thinking, people actually enjoy mental activity and intentionally seek out situations that demand thought, such as solving crossword puzzles, watching informative documentaries, or pursuing mentally challenging careers.

• Solving Problems Brings Pleasure: Successful problem-solving triggers a sense of satisfaction and fulfillment, which is believed to be linked to the brain's natural reward system and the release of dopamine, a neurotransmitter associated with pleasure.

• Difficulty Level Matters for Enjoyment: The pleasure derived from problem-solving is not solely based on the content of the problem, but rather on the difficulty level. Problems that are too easy or too difficult tend to be less enjoyable, as they do not provide the optimal level of challenge and sense of accomplishment.

• Curiosity is Fragile: While people are naturally curious and willing to explore new ideas and problems, their curiosity can quickly wane if the mental work required is perceived as too overwhelming or too trivial. This can explain why students may not enjoy school if the work is routinely too difficult or too easy for their abilities.

• Balancing Difficulty is Key: To maintain curiosity and engagement, it is important to provide problems or tasks that are at the right level of difficulty, where the mental work required is challenging enough to be rewarding, but not so difficult that it becomes frustrating or discouraging.

Implications for the Classroom

• Thinking Requires Four Key Factors: Successful thinking relies on four factors: (1) information from the environment, (2) facts in long-term memory, (3) procedures in long-term memory, and (4) the amount of space in working memory. If any one of these factors is inadequate, thinking will likely fail.

• Procedural Knowledge Aids Thinking: Procedural knowledge, which is your knowledge of the mental procedures necessary to execute tasks, acts as a "recipe" to accomplish a particular type of thought. Having the appropriate procedure stored in long-term memory helps a great deal when we're thinking.

• Factual Knowledge Supplements Thinking: Factual knowledge, such as the fact that 8 × 7 = 56, also helps thinking by providing information that can be combined in working memory to solve problems.

• Working Memory Capacity Limits Thinking: Working memory has limited space, so thinking becomes increasingly difficult as working memory gets crowded. Overloads of working memory can be caused by things like multistep instructions, lists of unconnected facts, and the application of a just-learned concept to new material.

• Framing Problems to Engage Students: When planning lessons, teachers should consider the key question that will engage students and make them want to know the answer, rather than just focusing on the information they want students to know. Framing the problem in the right way can pique students' interest and curiosity.

• Adjusting Problem Difficulty to Student Preparation: Teachers should assign work to individuals or groups of students that is appropriate to their current level of competence, rather than giving all students the same work, which may be too difficult for some and too easy for others.

• Changing the Pace to Maintain Attention: Changing topics, starting a new activity, or in some other way shifting gears can grab students' attention when they start to lose focus, giving the teacher a new chance to engage them.

Chapter 2 - How Can I Teach Students the Skills They Need When Standardized ...

Here are the key takeaways from the chapter:

• Factual knowledge must precede skill: The ability to analyze, synthesize, and think critically requires extensive factual knowledge. Trying to teach these skills without first building a foundation of knowledge is impossible.

• Facts should be taught in context: Merely memorizing disconnected facts is not effective. Facts should be taught in the context of skills and understanding, ideally beginning in preschool.

• Standardized tests often focus on fact recall: Many standardized tests emphasize the regurgitation of isolated facts rather than the demonstration of analytical and critical thinking skills.

• Thinking requires background knowledge: To comprehend new information, one needs relevant background knowledge. Simply looking up definitions or facts on the internet is not a sufficient substitute for having that knowledge stored in long-term memory.

• Thinking processes are tied to knowledge: Critical thinking skills are not generic, context-independent procedures. They are intimately linked to the specific background knowledge in a domain. Mastering critical thinking in one area does not automatically transfer to other areas.

• Knowledge and skills must be developed in parallel: Educators must ensure that students acquire relevant background knowledge at the same time they are practicing critical thinking skills. The two are interdependent and should not be treated as separate.

Background Knowledge Is Necessary for Cognitive Skills

Here are the key takeaways from the chapter:

• Background knowledge is essential for reading comprehension: Even if you understand the individual ideas (A and B) in a sentence, you may still need background knowledge to understand the relationship between them.

• Writers intentionally leave gaps in their writing: Writers cannot include all factual details, as that would make the prose too long and tedious. They assume the reader has the necessary background knowledge to fill in these gaps.

• Chunking increases capacity in working memory: When you have relevant background knowledge, you can "chunk" related pieces of information into a single meaningful unit, allowing you to hold more in your working memory.

• Background knowledge guides interpretation of ambiguous information: When a passage is vague or ambiguous, your background knowledge helps you interpret the meaning by providing relevant context.

• Knowledge, not just reading ability, determines comprehension: Studies show that a person's level of knowledge about a topic is a stronger predictor of their comprehension than their general reading ability.

• The "fourth-grade slump" may be due to the role of background knowledge: As reading tests shift from focusing on decoding to comprehension, students from privileged backgrounds who have more background knowledge have an advantage.

Factual Knowledge Improves Your Memory

Here are the key takeaways from the chapter:

• Memory Retrieval is the First Cognitive Process: When faced with a problem, people first search their memory for a solution, rather than engaging in logical reasoning. This is because memory retrieval is an easy and effective way to solve problems, especially if the solution has worked in the past.

• Background Knowledge is Necessary for Logical Reasoning: The processes of critical and logical thinking are not possible without relevant background knowledge. Without prior knowledge, people struggle to reason through problems, as demonstrated by the card problem in Figure 6.

• Background Knowledge Enables Chunking: Extensive background knowledge allows people to recognize familiar patterns or "chunks" in information, which frees up working memory to focus on higher-level reasoning and problem-solving. This is seen in the example of the expert cook who can quickly identify recipes from the ingredients in a pantry.

• Background Knowledge Underlies Thinking Strategies: Many of the thinking strategies we teach students, such as looking for anomalous results in science, require underlying background knowledge in order to be applied effectively. The strategies themselves are not sufficient without the relevant domain knowledge.

• Expertise Relies More on Memory than Reasoning: In domains like chess, the differences between the best players and others are more attributable to their extensive memory of game positions rather than superior reasoning abilities. This is why top chess players maintain their edge even in fast-paced "blitz" tournaments.

In summary, the chapter emphasizes that background knowledge is a fundamental prerequisite for the higher-order cognitive skills we aim to develop in students. Mere exposure to thinking strategies is not enough - students need a solid foundation of relevant knowledge in order to apply those strategies effectively.

Implications for the Classroom

Here are the key takeaways from the chapter:

• Background Knowledge Improves Memory: People with more background knowledge in a subject area remember more details about information they read on that topic, compared to people without that background knowledge. This is because background knowledge helps provide context and meaning, which aids memory.

• Background Knowledge Facilitates Learning: Studies have shown that "experts" who have previously learned a lot about a subject are able to learn new information on that subject more quickly and easily than "novices" who have little prior knowledge. Background knowledge provides a foundation for integrating and understanding new information.

• The "Rich Get Richer" Effect: People with more factual knowledge in their long-term memory tend to learn and retain new information at a faster rate than those with less knowledge. This creates an ever-widening gap in knowledge between the "knowledge-rich" and "knowledge-poor" over time.

• Importance of Factual Knowledge: Contrary to the views of some great thinkers, factual knowledge is crucial for enabling higher-order cognitive processes like problem-solving, decision-making, and creativity. Knowledge provides the foundation and context necessary for effectively deploying thinking skills.

• Evaluating Which Knowledge to Teach: When deciding what factual knowledge to teach students, the key question should be "What knowledge yields the greatest cognitive benefit?", not "What knowledge is most important?". This means focusing on teaching the core concepts and background information needed to comprehend the material students are expected to read and understand.

• Ensuring Adequate Background Knowledge: Teachers should assess whether students have the necessary background knowledge before expecting them to engage in critical thinking tasks. Providing some initial "shallow" knowledge is better than no knowledge at all, as it can still provide a foundation for deeper understanding.

• Promoting Reading: Reading, especially of books, magazines, and newspapers, is one of the most effective ways to expand students' factual knowledge and vocabulary. Encouraging age-appropriate, engaging reading material is crucial.

• Incidental Knowledge Acquisition: Factual knowledge can be acquired incidentally through exposure, such as in conversations, math problems, or vocabulary used in the classroom, not just through direct study and memorization.

• Starting Knowledge Acquisition Early: Children's early home environments play a major role in the factual knowledge they bring to school. Teachers must work to build the knowledge base of students who start behind their peers in this regard.

Chapter 3 - Why Do Students Remember Everything That’s on Television and Forget ...

• Memory is Selective: The memory system cannot store everything we experience, so it selectively stores information based on certain principles.

• Repeated Information is More Memorable: Things that are repeated again and again are more likely to be stored in memory, as the memory system assumes they will need to be recalled later.

• Emotional Experiences are Memorable: Experiences that evoke strong emotions are also more likely to be stored in memory, as the memory system prioritizes these potentially important events.

• Neutral but Important Information may be Forgotten: Important yet neutral information, such as most schoolwork, may be forgotten by the memory system if it is not repeatedly thought about.

• Memory is a Product of Thought: What we remember is not a product of what we want to remember or try to remember, but rather a product of what we actually think about and devote cognitive resources to.

• Teaching should Focus on Student Thought: When designing assignments, teachers should pay careful attention to what the assignment will actually make students think about, rather than what the teacher hopes they will think about, as that is what the students will ultimately remember.

What Good Teachers Have in Common

Here are the key takeaways from the chapter:

• Attention is necessary for learning: If students do not pay attention to information, they will not be able to learn or remember it.

• Emotion can enhance memory, but is not necessary: Emotional events tend to be better remembered, but emotion is not required for learning and memory. Even mundane information can be learned and remembered if students think about its meaning.

• Repetition alone is not sufficient for learning: Repeatedly encountering information, like seeing many pennies, does not guarantee that information will be learned and remembered. The way students think about the information is more important.

• Wanting to remember is not enough: Explicitly telling students that they will be tested on information does not improve their memory of that information. The key is how students think about the material, not their desire to remember it.

• Memory is the residue of thought: What students remember is determined by what they think about. If they focus on the meaning and relationships of the material, they are more likely to remember it. If they focus only on superficial details, they may not remember the deeper meaning.

• Teachers should design lessons to encourage thinking about meaning: Assignments and activities should prompt students to think about the meaning and relationships of the material, not just superficial details. This will help ensure the information is encoded into long-term memory.

• The specific aspect of meaning matters: There can be multiple aspects of meaning for the same material. Students need to think about the specific aspect of meaning that is most relevant for the learning goal.

The Power of Stories

Here are the key takeaways from the chapter:

• Trying to make the material relevant to students' interests does not work: The author argues that content is seldom the decisive factor in maintaining student interest. Even if the instructor tries to make the material relevant to the students' interests, it can lead to distractions and students thinking about unrelated topics.

• Effective teachers have two key qualities: (1) They are able to connect personally with students, and (2) they organize the material in a way that makes it interesting and easy to understand.

• Student evaluations of teachers focus on two key factors: (1) Whether the professor seems like a nice person, and (2) whether the class is well organized. Most of the items on student evaluation forms are redundant and boil down to these two factors.

• The emotional bond between students and teacher is crucial: The author states that the emotional bond between students and teacher, for better or worse, accounts for whether students learn. A brilliantly organized teacher who is seen as mean will not be effective, while a funny or gentle teacher with poorly organized lessons will also not be effective.

• Personality and presentation style are only half of good teaching: The author argues that while the teacher's personality and presentation style (e.g., being a comedian, den mother, storyteller, or showman) can generate goodwill and get students to pay attention, the second crucial component is organizing the ideas in a lesson plan in a coherent way so that students will understand and remember the material.

Putting Story Structure to Work

Here are the key takeaways from the chapter:

• Stories are "psychologically privileged": The human mind is particularly adept at understanding and remembering stories, treating them differently in memory compared to other types of material.

• Story structure has 4 key principles:

  • Causality: Events in a story are causally related to one another, not just chronological.
  • Conflict: The protagonist has a goal but faces obstacles or adversaries that prevent them from reaching it.
  • Complications: Subproblems or challenges that arise from the protagonist's main goal.
  • Character: Strong, interesting characters are shown through their actions rather than just told.

• Advantages of using a story structure:

  • Comprehension: The audience understands the structure, helping them interpret the action and make inferences.
  • Interest: Stories are consistently rated as more interesting than other formats, even with the same information.
  • Memory: Stories are easy to remember due to the need for medium-difficulty inferences and the causal structure.

• Optimal level of detail: Stories are most interesting when they leave some inferences for the audience to make, rather than providing too much exhaustive detail.

But What If There Is No Meaning?

Here are the key takeaways from the chapter:

• Structuring Lessons Like Stories: The author suggests structuring lessons using the four elements of story structure - causality, conflict, complications, and character. This doesn't mean the teacher has to do most of the talking, but rather organize the material in a way that encourages students to think about it.

• Considering Different Perspectives: When planning a lesson, the author suggests considering different perspectives, such as using Japan's point of view for a lesson on Pearl Harbor, rather than just the typical U.S. perspective. This can lead to new ways of organizing the material.

• Storytelling in Math and Science: The author demonstrates how the storytelling approach can be used even in math and science classes, such as when introducing the concept of a Z-score in a statistics class. The key is to establish the central conflict or question that the lesson is trying to address.

• Spending Time on the Conflict: The author emphasizes the importance of spending significant time (e.g. 10-15 minutes) setting up the central conflict or question of the lesson, similar to how screenwriters spend time establishing the characters and situation in a movie before introducing the main conflict.

• Focusing on Meaning, Not Just Answers: The author argues that teachers should focus on helping students understand the meaning and significance of the material, not just getting to the right answer. Establishing the central question or conflict is crucial for this.

• One Approach Among Many: The author clarifies that using a story structure is just one way to teach, and not the only way. The key is to get students thinking about the meaning of the material.

Implications for the Classroom

Here are the key takeaways from the chapter:

• Rote Memorization: Rote memorization refers to memorizing material that has little or no meaning to the learner. This is commonly required when entering a new field or domain of knowledge, such as memorizing the symbols for elements on the periodic table.

• Mnemonic Devices: Mnemonic devices are memory tricks that can help students memorize meaningless material. Examples include acronyms, first-letter methods, and setting information to music or rhythms. Mnemonics work by providing cues to aid recall.

• Lesson Plan Review: Teachers should review their lesson plans to anticipate what students are likely to actually think about, rather than just what the teacher hopes they will think about. This can reveal if students are unlikely to grasp the intended meaning.

• Attention Grabbers: Attention grabbing techniques at the start of a lesson can pique student interest, but teachers must ensure the attention grabber is clearly connected to the lesson's learning objectives, so students can transition from the attention grabber to thinking about the intended meaning.

• Discovery Learning: Discovery learning, where students explore and discover concepts on their own, can be effective when the environment provides prompt feedback on whether the student is thinking about the problem correctly. However, without such constraints, discovery learning risks students forming incorrect understandings.

• Designing for Meaning: Assignments should be designed so that thinking about the meaning of the material is unavoidable for students, rather than just telling them to remember facts or concepts.

• Appropriate Use of Mnemonics: While overreliance on mnemonics should be avoided, they can have a place in helping students memorize prerequisite knowledge (like letter-sound associations or vocabulary) that will enable them to then focus on understanding deeper meanings.

Chapter 4 - Why Is It So Hard for Students to Understand Abstract Ideas?

• Abstraction is the goal of schooling: The teacher wants students to be able to apply classroom learning in new contexts, including those outside of school.

• The mind prefers the concrete: The mind does not care for abstractions and instead prefers concrete examples to help understand new concepts.

• We understand new things in the context of things we already know: Most of what we know is concrete, making it difficult to comprehend and apply abstract ideas.

• Exposure to multiple examples of an abstraction helps understanding: The surest way to help students understand an abstraction is to expose them to many different versions of the abstraction, such as solving area calculation problems about various objects.

• New techniques can hurry the process of understanding abstractions: There are some promising new techniques that can help students understand abstract ideas more quickly.

Why Is Knowledge Shallow?

Here are the key takeaways from the chapter:

• Understanding new ideas is about relating them to old ideas: Students understand new ideas by relating them to things they already know. This is similar to how we understand an unfamiliar word by looking up its definition in terms of familiar words.

• Analogies and concrete examples aid understanding: Analogies and concrete examples help students understand new ideas by relating them to familiar concepts. Abstractions are harder to understand without these concrete connections.

• Familiarity of examples is key, not just concreteness: Concrete examples alone are not enough - the examples must also be familiar to the student. Unfamiliar concrete examples, like the Mohs scale or Rasch model, do not aid understanding.

• Understanding involves retrieving and manipulating relevant prior knowledge: To understand a new idea, the teacher must ensure the right prior knowledge is retrieved from the student's long-term memory and put into working memory. The student must then compare, combine or otherwise manipulate these relevant prior concepts.

• Understanding is a matter of degree, not all-or-nothing: Even when students "understand" a new concept, their level of comprehension can vary from shallow to deep. Repeated exposure and practice is often needed to develop deeper understanding.

• Transfer of understanding to new contexts is challenging: Students may struggle to recognize that they know how to solve a problem when it is presented in a new context, even if they recently solved a similar problem. Transferring understanding beyond the classroom is difficult.

Why Doesn’t Knowledge Transfer?

Here are the key takeaways from the chapter:

• Rote Knowledge vs. Shallow Knowledge: Rote knowledge refers to memorizing information without understanding, while shallow knowledge means having some understanding of the material but in a limited context.

• Searle's Chinese Room Thought Experiment: This thought experiment illustrates how a person can display intelligent behavior (responding to Chinese messages) without truly understanding the language, similar to how a computer can display sophisticated behavior without real comprehension.

• Characteristics of Deep Knowledge: Students with deep knowledge can apply their understanding in different contexts, discuss the topic in various ways, and predict how changes in one part of the system would affect the whole. Their knowledge is richly interconnected.

• Reasons for Shallow Knowledge: Students may develop shallow knowledge due to lack of attention, or because the target knowledge is too abstract and they need more concrete examples to fully grasp the underlying principles.

• The Importance of Examples: Using familiar examples like classroom or family rules can help students understand more abstract concepts like the need for rules in a community. However, students need to see the connection between the examples and the broader principle to develop deep knowledge.

• Difficulty of Attaining Deep Knowledge: Deep knowledge, which involves understanding both the abstraction and the examples and how they are related, is more challenging to obtain than shallow knowledge.

Implications for the Classroom

Here are the key takeaways from the chapter:

• Surface Structure vs. Deep Structure: The surface structure of a problem refers to the specific details and context, while the deep structure refers to the underlying principles and steps required to solve the problem. People tend to focus more on the surface structure, which makes it difficult to transfer knowledge to new problems with different surface structures but the same deep structure.

• Cognitive Interpretation: When people read or hear new information, their cognitive system tries to make sense of it by relating it to their existing background knowledge. This process of interpretation often leads them to focus on the surface structure rather than the deep structure.

• Difficulty in Identifying Deep Structure: The deep structure of a problem is not always obvious, and there can be multiple deep structures that could be applicable. In contrast, the surface structure is immediately apparent, which makes it easier for people to focus on.

• Importance of Providing Examples: Providing students with multiple examples of the same deep structure, and asking them to compare and contrast the examples, can help them recognize the deep structure and facilitate knowledge transfer.

• Emphasis on Deep Knowledge: Teachers should make it clear, both explicitly and implicitly through their teaching practices, that they expect students to understand the deep meaning and underlying principles, not just the surface-level facts.

• Difficulty of Tracing Implicit Knowledge Transfer: Even when people don't recognize the connection between a new problem and a previously solved one, they may still be drawing on their past experiences and knowledge to inform their approach. This type of implicit knowledge transfer is difficult to observe and study.

Chapter 5 - Is Drilling Worth It?

• Cognitive Bottleneck: The mind has a limited capacity to juggle multiple ideas simultaneously. This cognitive bottleneck is a key reason why practice is essential for developing proficiency in mental tasks.

• Automaticity: Low-level processes, such as hitting a ball in soccer or recalling math facts, must become automatic through practice, freeing up mental resources for higher-level concerns, like game strategy or complex problem-solving.

• Reasons for Practice: There are two main reasons to practice: (1) to gain a minimum level of competence in a skill, and (2) to improve and refine an existing skill, even after the basics have been mastered.

• Reinforcement of Basic Skills: Practicing skills, even after they have been mastered, helps reinforce the basic skills required for learning more advanced skills.

• Protection Against Forgetting: Continued practice helps protect against forgetting the skills and knowledge that have been acquired.

• Improved Transfer: Practicing skills, even after they have been mastered, can improve the transfer of those skills to new contexts and situations.

Practice Makes Memory Long Lasting

Here are the key takeaways from the chapter:

• Working Memory is the Site of Thinking: Working memory is where thinking occurs, as it allows us to combine information from the environment, long-term memory, and other sources in new ways. However, working memory has limited capacity, which acts as a fundamental bottleneck for human cognition.

• Chunking Expands Working Memory Capacity: One way to overcome the limited capacity of working memory is through chunking, which involves treating multiple separate pieces of information as a single unit. This allows more information to be stored in working memory at once.

• Automaticity Frees Up Working Memory: Another way to overcome working memory limitations is to make mental processes more efficient and automatic through practice. Automatic processes require little to no working memory capacity, freeing up space for other cognitive tasks.

• Automaticity Develops Through Repetition: The only way to develop automatic mental processes is through repeated practice of the target skill or knowledge. There are no shortcuts or workarounds - automaticity must be earned through extensive repetition.

• Automaticity Enables Higher-Level Thinking: Developing automatic retrieval of basic skills and knowledge, such as letter-sound associations or math facts, frees up working memory to focus on higher-level thinking and meaning-making. This is why practice is so crucial for advancing learning in academic subjects.

• Civilization Advances Through Automaticity: As philosopher Alfred North Whitehead noted, the progress of civilization depends on being able to perform important operations without conscious thought. Automaticity allows us to devote our limited working memory resources to more complex and innovative thinking.

Practice Improves Transfer

Here are the key takeaways from the chapter:

• Forgetting is Rapid: Even students who perform well in a course (e.g., get an A) forget much of the material within a few years, with retention dropping to 50% or less within 3 years.

• Continued Practice Prevents Forgetting: People who take more advanced math courses after an introductory algebra course are able to retain their algebra skills even decades later, whereas those who only take one algebra course forget it over time. This is because continued practice of the material prevents forgetting.

• Spaced Practice is More Effective than Cramming: Spacing out study sessions over time (e.g., 1 hour per day for 4 days) leads to better long-term retention compared to cramming all the study time into a single session right before a test. Cramming may lead to better performance on an immediate test, but the material is forgotten more quickly.

• Spaced Practice Requires Less Total Study Time: Because spaced practice leads to longer-lasting memory, students can get away with less total practice time compared to cramming, while still achieving better long-term retention of the material.

• Spaced Practice is Easier to Make Interesting: The fact that spaced practice is better for long-term retention makes it easier for teachers to make the practice activities interesting for students, since they don't have to cram as much practice into a short time period.

Implications for the Classroom

Here are the key takeaways from the chapter:

• Practice Increases Transfer: Practicing a particular type of problem or skill makes it more likely that you will recognize the underlying structure of a new problem, even if it is not identical to the problems you have practiced. The more practice you have with a deep structure, the more automatic it becomes to recognize it.

• Contextual Information Aids Understanding: As you read or listen, your mind automatically interprets the meaning of words and relationships based on the context, similar to how you understand that "eye" can refer to the center of a hurricane. This happens without conscious effort.

• Practicing Deep Structures: Just like practicing the meaning of individual words, practicing recognizing deep structures (e.g. permission rules) makes it more automatic to identify them in new situations. The first time you encounter a deep structure you may understand it, but practice is required for it to become automatic.

• What to Practice: The processes and skills that should be practiced extensively are the "building blocks" - the fundamental skills and knowledge that serve as prerequisites for more advanced work. Examples include retrieving number facts, letter sounds, or basic facts about elements.

• Spacing Out Practice: Spacing out practice over time, rather than massing it all together, has several benefits: it makes memory more enduring, reduces boredom, and requires students to think more carefully about how to apply their knowledge to new problems.

Chapter 6 - What’s the Secret to Getting Students to Think Like Real ...

Here are the key takeaways from the chapter:

• Cognition Early vs. Late in Training: The cognitive principle that guides this chapter is that cognition early in training is fundamentally different from cognition late in training. Experts do not think like experts-in-training when they started out - they thought like novices.

• Limitations of Typical Science and History Curricula: Typical science and history curricula focus on having students memorize facts, conduct predictable lab experiments, and read textbook summaries, rather than engaging in the actual thinking and problem-solving that scientists and historians do. This does not adequately prepare students to think like real scientists and historians.

• Expertise Requires Extensive Practice: Real scientists, mathematicians, and historians are experts who have worked in their fields for years, often 40+ hours per week. This extensive practice makes a qualitative, not just quantitative, difference in how they think compared to well-informed amateurs.

• Thinking Like an Expert is a Tall Order: Thinking like a true expert in a field, such as a historian, scientist, or mathematician, is a very challenging goal for students. It requires much more than just learning facts and procedures - it involves a fundamentally different way of organizing and applying knowledge.

• Importance of Authentic Experiences: While students should not be expected to immediately think like experts, providing them with opportunities to engage in authentic scientific, historical, or mathematical practices (e.g., analyzing primary sources, designing experiments with unknown outcomes) can be valuable, even if their thinking is still at a novice level.

What Is in an Expert’s Mental Toolbox?

Here are the key takeaways from the chapter:

• Experts have the ability to quickly identify and focus on the most important details: Experts are able to sift through large amounts of information and quickly identify the most relevant details, unlike novices who often struggle to separate the "wheat from the chaff".

• Experts are highly attuned to subtle cues that others miss: Experts, like Dr. House, are able to pick up on small, seemingly insignificant details that provide important clues, which others often overlook.

• Experts have extensive domain-specific knowledge, but also the ability to rapidly access and apply that knowledge: While experts and novices may have similar levels of knowledge, experts can more quickly and accurately retrieve and apply the relevant information from their memory.

• Experts make "graceful" mistakes: When experts make mistakes, their incorrect guesses or hypotheses are still relatively sensible and plausible, unlike the more haphazard mistakes made by novices.

• Experts demonstrate better "transfer" of knowledge to similar domains: Experts are better able to apply their skills and knowledge to new, but related, areas, whereas novices struggle to transfer their learning beyond the specific context in which it was acquired.

• Experts, like experienced teachers, are better able to notice and respond to important details: Experts, whether in medicine, teaching, or other fields, are more attuned to relevant cues and can more quickly generate appropriate responses, compared to novices.

How Can We Get Students to Think Like Experts?

Here are the key takeaways from the chapter:

• Working Memory Limitations: Working memory is the mental workspace where thought occurs, but it has a limited capacity. Experts use two strategies to overcome this limitation:

  • Background Knowledge: Experts have extensive, well-organized knowledge in their domain, which allows them to "chunk" information and reduce the load on working memory.
  • Automaticity: Experts have automated many routine procedures, freeing up working memory for higher-level thinking.

• Abstract vs. Surface-Level Thinking: Experts think in terms of the deep, functional structure of problems, whereas novices focus on surface-level features. This allows experts to:

  • Recognize underlying principles that connect seemingly different problems
  • Ignore irrelevant details and focus on the most important information
  • Achieve better transfer of knowledge to new situations

• Expert Self-Talk: Experts engage in a more sophisticated form of self-talk compared to novices. Experts use self-talk to:

  • Generate hypotheses about the nature of the problem
  • Evaluate their understanding and think through the implications of possible solutions
  • Draw inferences and test their own reasoning

• Expertise Development: Expertise is developed through a combination of:

  • Acquiring extensive, well-organized domain knowledge
  • Automating routine procedures through extensive practice
  • Engaging in self-reflection and self-testing to deepen understanding

These key takeaways highlight the cognitive mechanisms that distinguish experts from novices, and the strategies that can be used to develop expertise in a domain.

Implications for the Classroom

Here are the key takeaways from the chapter:

• Experts Think Differently than Novices: Experts see problems and situations in their field functionally rather than at the surface level. This allows them to focus on important details, produce consistent solutions, and transfer their knowledge to related fields.

• Expertise Requires Extensive Practice: Becoming an expert in a field takes a significant amount of time and practice, often around 10 years. This is because experts need to build up a large knowledge base and develop automatic cognitive processes.

• Students are Comprehenders, not Creators: Students are novices, not experts. Their goal should be to develop a deep understanding of existing knowledge, rather than to create new knowledge like experts do.

• Challenging Activities can be Motivating, but not Cognitively Beneficial: Asking students to engage in expert-level activities like designing experiments or analyzing historical documents may be motivating, but is unlikely to actually help them think like experts. These activities are beyond their current cognitive capabilities.

• Practice and Technique are Important, but not the Whole Story: While practice and technique are crucial for developing expertise, great experts also have an incredible capacity for sustained, focused work that sets them apart.

Chapter 7 - How Should I Adjust My Teaching for Different Types of Learners?

• Learning Styles Hypothesis: The hypothesis behind learning styles theory is that some students learn best visually (they have to see it to learn it), while others learn best auditorily (they have to hear it to learn it). Additionally, some students are linear thinkers, while others are holistic thinkers.

• Lack of Empirical Evidence: Despite extensive research over the past 50 years, there is no consistent evidence supporting the existence of distinct learning styles that would require tailoring instruction to each individual student's cognitive style.

• Cognitive Principle: The cognitive principle guiding this chapter is that "children are more alike than different in terms of how they think and learn." This does not mean that all children are exactly alike, but rather that there are no categorically different types of learners.

• Individualized Instruction: While teachers should interact with each student differently, just as they interact with friends differently, they should be aware that there is no scientific basis for the existence of distinct learning styles that would require drastically different teaching methods for different students.

• Burden on Teachers: Analyzing and catering to multiple learning styles in the same classroom would be an enormous burden on the teacher, as there is no evidence that such an approach would be more effective than a more generalized teaching method.

• Importance of Tailoring Instruction: The chapter acknowledges that struggling students might benefit from alternative teaching methods, but the lack of empirical support for learning styles theory suggests that a more generalized approach to instruction may be more effective.

Cognitive Styles

Here are the key takeaways from the chapter:

• Cognitive Abilities vs. Cognitive Styles: Cognitive abilities refer to a person's capacity or success in certain types of thought, such as mathematical ability. Cognitive styles, on the other hand, are biases or tendencies to think in a particular way, such as sequentially or holistically. Abilities reflect the level or quantity of what someone knows and can do, while styles reflect how someone prefers to think and learn.

• Differences in Abilities and Styles: While students' differences in abilities and styles may seem obvious and large, the chapter suggests that these differences are more alike than different overall. Abilities are considered better or worse, but styles are not - they are simply different approaches that may be more or less effective for particular problems.

• Using Strengths to Address Weaknesses: Teachers can leverage a student's strengths to help address their weaknesses. For example, using a student's talent in poetry (a strength) to help them better understand biology concepts (a weakness).

• Adapting Instruction to Learning Styles: Teachers can also adapt their instruction to better match a student's preferred learning style. For example, presenting material in a more holistic manner for a student who thinks holistically, rather than sequentially.

• Tradeoffs in Adapting Instruction: While these approaches of leveraging strengths and adapting instruction hold promise, the chapter acknowledges that they also imply more work for the teacher, as it may require changing instruction for each individual student. The chapter suggests that the research on cognitive abilities and styles will shed light on whether this extra work is worthwhile.

Visual, Auditory, And Kinesthetic Learners

Here are the key takeaways from the chapter:

• Cognitive Styles: Cognitive styles refer to the different ways people think, make decisions, and approach problems. Psychologists have proposed many distinctions between cognitive styles, such as impulsive vs. reflective, complex vs. simple, and concrete vs. abstract.

• Evaluating Cognitive Styles: To be considered a valid cognitive style, a distinction must meet three criteria: (1) it must be stable within an individual, (2) it must have meaningful consequences for how people think and learn, and (3) it must not simply measure ability rather than style.

• Field Dependence vs. Independence: One proposed cognitive style is field dependence vs. independence, which refers to whether people tend to see objects in terms of their relationships to other objects (field dependent) or as independent details (field independent). However, this distinction seems to measure ability rather than style, as field-independent people outperform field-dependent people on cognitive tasks.

• Lack of Validated Cognitive Style Theories: Despite decades of research, psychologists have not been able to identify a cognitive style theory that meets all three of the necessary criteria. This does not mean cognitive styles do not exist, but it suggests that the current theories and measures have not successfully captured these differences in thinking and learning.

• Visual, Auditory, and Kinesthetic (VAK) Learners: One specific cognitive style theory that will be examined more closely is the idea of VAK learners, who are said to prefer learning through visual, auditory, or kinesthetic modalities. The chapter suggests that this theory, like others, has not been conclusively validated by research.

Abilities and Multiple Intelligences

Here are the key takeaways from the chapter:

• Cognitive Styles vs. Abilities: The chapter distinguishes between cognitive styles (biases or tendencies to think or learn in a particular way) and cognitive abilities (how effectively one can perform a particular cognitive function).

• Visual, Auditory, and Kinesthetic Learners: The visual-auditory-kinesthetic (VAK) theory suggests that people have a preferred sensory modality (visual, auditory, or kinesthetic) for receiving and processing new information. However, the chapter argues that this theory is not well-supported by research.

• Storing Memories: People can store memories in terms of visual, auditory, and semantic (meaning-based) representations. Individuals vary in the vividness and effectiveness of their visual and auditory memories, but meaning-based memories are most important for academic learning.

• Lack of Evidence for VAK Theory: Numerous studies have found that matching instruction to a student's "preferred" sensory modality does not improve their learning. The chapter argues that the VAK theory is not supported by the evidence.

• Reasons for the Persistence of the VAK Theory: The chapter suggests several reasons why the VAK theory remains popular despite the lack of evidence: (1) it has become commonly accepted wisdom, (2) there are individual differences in visual and auditory memory abilities, and (3) the confirmation bias leads people to interpret ambiguous situations as confirming their existing beliefs.

• Broader Implications: The chapter states that the critiques of the VAK theory apply to other cognitive styles theories as well, and that the evidence for these theories is generally mixed.

Conclusions

• Mental Abilities vs. Mental Ability: The chapter argues that we should talk about "mental abilities" rather than a single "mental ability" or "intelligence". This is because people can excel at some mental tasks (e.g., math) while struggling with others (e.g., reading comprehension), suggesting that different mental processes support these activities.

• Gardner's Theory of Multiple Intelligences: The chapter discusses Howard Gardner's theory, which proposes that there are eight distinct "intelligences" (e.g., linguistic, logical-mathematical, musical, etc.), rather than a single general intelligence. This theory was influential in education, though the chapter suggests that Gardner's claims about the theory have been overstated or misinterpreted by some.

• Intelligences vs. Talents: The chapter explores the debate around whether Gardner's list represents "intelligences" or simply "talents/abilities". The author notes that while the distinction may be arbitrary, the term "intelligence" carries more prestige, which likely contributed to the theory's popularity.

• Teaching All Intelligences: The chapter discusses the claim that schools should teach all eight of Gardner's intelligences, which the author sees as a misinterpretation of the theory. The author argues that curricular decisions should be based on community values, with Gardner's theory providing guidance, rather than the idea that all intelligences must be equally emphasized.

• Leveraging Strengths: The chapter suggests that it may be sensible to try to engage students by appealing to their strengths (e.g., giving the science-oriented student memoirs of a physicist rather than poetry). However, the author cautions that this approach has limits and is not a revolutionary idea.

• Separateness of Abilities: The chapter argues against the idea that students can learn concepts by tapping into different intelligences (e.g., learning about commas through musical, naturalist, and kinesthetic activities). The author contends that different abilities are distinct and cannot be easily substituted for one another.

Implications for the Classroom

Here are the key takeaways from the chapter:

• Matching instruction to cognitive style is not supported by evidence: The idea that matching instruction to a student's preferred "cognitive style" will improve learning is not supported by scientific evidence. Despite much research, no reliable set of cognitive styles has been identified.

• Substituting cognitive strengths for weaknesses is not possible: The idea that students can use their cognitive strengths to compensate for weaknesses is also not supported. While students do differ in their cognitive abilities, this type of substitution does not work in practice.

• Differentiation should be based on the teacher's experience, not scientific categories: Since science has not identified reliable categories of students or matching instructional methods, teachers should differentiate instruction based on their own experience with each student, rather than trying to apply scientific theories.

• Applying "learning style" ideas to content, not students: While "learning style" theories are not useful for categorizing students, they can be applied productively to think about how to present different types of content (e.g. visual, auditory, kinesthetic).

• Varying instructional methods promotes attention: Changing the mental processes required of students (e.g. from deductive to creative thinking) can help re-focus their attention during a lesson.

• Not all students will be "smart in some way": The idea that "every student is intelligent in some way" is problematic. Some students may simply not have exceptional abilities in any area, and telling them otherwise is unlikely to be helpful.

• Intelligence does not determine a child's value: A child's worth is not defined by their intelligence or cognitive abilities. Even children with severe mental disabilities have inherent value.

Chapter 8 - How Can I Help Slow Learners?

Here are the key takeaways from the chapter:

• Intelligence is Malleable: The chapter contrasts the Western view of intelligence as a fixed attribute with the Eastern view of intelligence as malleable. It argues that intelligence can be changed through sustained hard work, rather than being solely determined by genetics.

• General Intelligence (g) and Specific Intelligences: The chapter explains the concept of general intelligence (g), which contributes to performance across different cognitive tasks. However, it also discusses how there are specific intelligences, such as verbal and mathematical intelligence, that are distinct but also influenced by g.

• Defining Intelligence: The chapter provides a definition of intelligence as the ability to understand complex ideas, use different forms of reasoning, overcome obstacles through thought, and learn from experiences. This definition focuses on cognitive abilities rather than other talents like music or athletics.

• Implications for Slow Learners: Given the understanding of intelligence as malleable and comprising both general and specific components, the chapter suggests that teachers should model the belief in malleable intelligence and provide support to help all students, including those who may be slower learners, get the most out of their education.

• Limitations of the Single Intelligence Model: The chapter critiques the simplistic view of a single, fixed intelligence that underlies all intellectual tasks. It explains how the data shows a more nuanced pattern of general intelligence (g) contributing to both verbal and mathematical intelligences, which are distinct but related.

How Beliefs About Intelligence Matter

Here are the key takeaways from the chapter:

• Genetics vs. Environment in Intelligence: The chapter presents two opposing views on the origins of intelligence - that it is primarily determined by genetics (nature) or by environmental factors and experience (nurture). The author argues that the truth likely lies somewhere in the middle, with both genetics and environment playing important roles.

• Twin Studies: Comparing the intelligence of identical twins (who share 100% of their genes) to fraternal twins (who share 50% of their genes) can help researchers understand the relative importance of genetics and environment. Siblings raised in the same household also provide insights.

• Heritability of Intelligence: Studies suggest that genetics account for around 50% of individual differences in intelligence, on average. This percentage increases as people age, which is the opposite of what one might expect.

• The Flynn Effect: IQ scores have shown substantial increases over the past several decades in many countries, a phenomenon known as the Flynn effect. This rapid change cannot be explained by genetic factors, indicating that environmental factors play a significant role in intelligence.

• Interaction of Genetics and Environment: The author proposes that genetics may have a relatively modest direct effect on intelligence, but can indirectly influence intelligence by steering individuals towards certain environments and experiences that then shape their cognitive development.

• Malleability of Intelligence: Since intelligence is not solely determined by genetics, the chapter suggests that it can be improved through interventions and environmental factors. This has important implications for how we approach educating students who may seem less intelligent.

Implications for the Classroom

Here are the key takeaways from the chapter:

• Beliefs about Intelligence: There are two main beliefs about intelligence - a "fixed" view where intelligence is seen as unchangeable, and a "malleable" view where intelligence is seen as something that can be developed through effort and learning.

• Felicia vs. Molly: Felicia, who believes in fixed intelligence, avoids challenging tasks to maintain the appearance of being smart, whereas Molly, who believes in malleable intelligence, embraces challenges as opportunities to learn and grow.

• Praise and Beliefs: The way children are praised (for ability vs. effort) can shape their beliefs about intelligence. Praising effort promotes a malleable view, while praising ability promotes a fixed view.

• Helping Slow Learners: Slow learners are not "dumb", they just need more time and support to develop their potential. Key strategies include:

  • Praising effort, not ability
  • Explicitly telling students that hard work pays off
  • Treating failure as a natural part of learning
  • Explicitly teaching study skills, as they cannot be taken for granted

• Catching Up is Challenging: Catching up for slow learners is difficult, as they have more ground to cover. It requires a sustained, long-term effort, similar to lifestyle changes required for successful dieting. Setting achievable interim goals is important.

Chapter 9 - What About My Mind?

• Teaching is a Cognitive Skill: Like any complex cognitive skill, teaching must be practiced and improved over time. The cognitive principles that apply to students' minds also apply to the minds of teachers.

• The Mind's Cognitive Apparatus: Effective thinking, including effective teaching, requires three key components: 1) Sufficient working memory capacity to manipulate information, 2) Relevant background knowledge stored in long-term memory, and 3) Familiarity with the mental procedures (or "cognitive skills") needed to process information.

• Working Memory: Working memory is the "staging ground of thought" where information from the environment and long-term memory is combined and manipulated. However, working memory has limited capacity, so teachers must be mindful of cognitive load.

• Long-Term Memory: Long-term memory stores both factual knowledge (e.g., content knowledge) and procedural knowledge (e.g., teaching strategies and techniques). Teachers need to have both types of knowledge readily available to draw upon.

• Cognitive Procedures: Teachers need to have well-practiced cognitive procedures or "mental skills" for tasks like lesson planning, explaining concepts, managing a classroom, and assessing student learning. These procedures can become automated with practice.

• Applying Cognitive Principles to Teaching: Just as students need sufficient working memory, relevant knowledge, and cognitive skills to learn effectively, teachers need these same cognitive resources to teach effectively. The principles of cognitive science apply equally to the minds of teachers and students.

The Importance of Practice

Here are the key takeaways from the chapter:

• Teaching Demands Working Memory: Teaching requires juggling multiple tasks simultaneously, and if teachers try to juggle too many things, some will be dropped. Formal experiments confirm that teaching is highly demanding of working memory.

• Factual Knowledge is Important for Teaching: Teachers need rich subject-matter knowledge, especially in middle and high school and in math. Additionally, pedagogical content knowledge (knowledge specific to teaching a subject) is also crucial for effective teaching.

• Procedural Knowledge is Crucial for Teaching: Teachers make extensive use of procedures stored in long-term memory, ranging from mundane tasks like passing out papers to more complex procedures like explaining mathematical concepts or handling student conflicts.

• Improving Teaching Requires Practice: Just like any other cognitive skill, the best way to improve teaching is through extended practice. Simply having a lot of experience, like driving for 30 years, does not necessarily lead to improvement without deliberate practice.

A Method for Getting and Giving Feedback

Here are the key takeaways from the chapter:

• Practice vs. Experience: Practice involves consciously trying to improve one's performance, whereas experience simply means engaging in an activity. For example, a driver with 30 years of experience may not be well-practiced if they did not actively try to improve their driving skills.

• Feedback is Crucial for Improvement: To improve, one needs feedback from knowledgeable people. This is because it is difficult to objectively assess one's own performance, and we often have a self-serving bias that makes us interpret our performance in a favorable way.

• Practice Involves More than Just the Target Task: Effective practice often involves activities that are not directly related to the target task but are done to improve it. For example, chess players study openings and analyze expert games, and athletes do strength and conditioning training.

• Peer Observation and Feedback: Working with a partner to observe each other's teaching and provide feedback is a valuable practice method. This allows the teacher to get an outside perspective and constructive criticism, which can be difficult to obtain on one's own.

• Importance of a Supportive Approach: When providing feedback to a peer, it is important to be supportive and focus on concrete behaviors, rather than making judgments or offering unsolicited advice. This helps the teacher being observed feel comfortable and open to the feedback.

• Respecting the Teacher's Goals: When observing a peer's teaching, it is important to respect the goals and focus that the teacher has set for the observation, rather than imposing one's own agenda.

Consciously Trying to Improve: Self-Management

• Videotaping Yourself: The purpose of videotaping yourself is to increase your awareness of what is happening in your classroom and gain a new perspective on your own actions and your students' actions. This allows you to identify areas for improvement.

• Making a Simple Plan: When making a plan for improvement, focus on implementing one change at a time, even if you have identified multiple areas for improvement. This keeps the process simple and manageable.

• Cognitive Principles: The approach outlined in the chapter is rooted in cognitive principles, such as the limitations of working memory, the importance of what we pay attention to for memory formation, and the differences in how experts and novices perceive the world.

• Observation and Background Knowledge: Careful observation of your own classroom, as well as other classrooms, can help you gain valuable background knowledge about your students, their interactions, and how they engage with the material you teach. This background knowledge is crucial for effective problem-solving.

• Changeability of Teaching: The chapter presents a hopeful view that teaching, like human intelligence, can be improved through sustained hard work. This suggests that teachers can develop and enhance their teaching skills over time.

Smaller Steps

Here are the key takeaways from the chapter:

• Planning for the Extra Work Required: Improving your teaching will require more conscious effort and be emotionally draining, so you may need extra support from your family and schedule more relaxation time.

• Allocating Additional Time for Improvement: Spending more time reviewing your teaching performance and planning changes will require finding that time, such as by scheduling it in your weekly routine.

• Prioritizing and Taking Smaller Steps: It's not realistic to try to improve everything at once, so focus on the most important areas and take concrete, manageable steps towards your goals.

• Keeping a Teaching Diary: Regularly writing down your teaching intentions, reflections on how lessons went, and patterns you notice can help you be more self-aware and identify areas for improvement.

• Starting a Discussion Group with Fellow Teachers: Meeting regularly with other teachers can provide social support and a forum to share problems and get ideas for solutions.

Key terms and concepts:

• "Autopilot" refers to teaching based on past habits rather than conscious effort to improve.
• "Self-reflection" is the process of critically examining one's own teaching practices and performance.

Conclusion

Here are the key takeaways from the chapter:

• Reading is a mental journey: Every piece of writing is a proposal to the reader to embark on a mental journey. The writer must keep in mind whether the reader is being adequately rewarded for their time and effort, as the ratio of effort to reward increases, the likelihood of the reader discontinuing the journey also increases.

• Teaching is an act of persuasion: Similar to writing, teaching involves guiding the thoughts of the student down a particular pathway or exploring new terrain. The teacher must persuade the student to continue the journey, not to lose heart when encountering obstacles, and to appreciate the beauty and awe of the scenery.

• Know your audience/students: To ensure that students follow the teacher, the teacher must keep them interested. To ensure their interest, the teacher must anticipate their reactions, which requires knowing the students' personality, tastes, biases, and background knowledge.

• Cognitive science principles: The book presents nine principles of the mind that are always applicable, based on a large body of data, have a sizable impact on student performance, and provide clear direction for teachers. These principles cover topics like encountering new problems, expertise, differences among students, and the need to practice teaching.

• Cognitive science is useful but not decisive: Cognitive science can help teachers balance conflicting concerns in the classroom and provide boundaries for educational practice, but it is not the whole story. Education involves emotional, social, and motivational elements that are outside the realm of cognitive science.

• Importance of using cognitive science in education: Applying the accumulated wisdom of cognitive science to education holds the promise of making better minds and better education, which is crucial given the importance of education in passing on the accumulated wisdom of generations.

Discussion

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