Literary Insights

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Here is a summary of the introduction:

The author recalls nearly being attacked by a monkey as a student in India, which could have derailed his education. He struggles with coursework but passes his summer course.
He now leads MIT’s Open Learning initiative to extend educational opportunities. Many past reformers have tried to open education but faced challenges realizing visions.
While newer technologies are promising, past predictions of major changes from correspondence courses, films, radio, TV, and computers did not entirely transform schools. Schools remain much the same as 150+ years ago.
The author wonders what truly enables change, reflecting on his past struggle and how the physical school setting was naturally easy for a monkey but not for him as a student. His journey led him to questions about learning and new approaches to education.

The author argues that while schools are often compared to factories that mold students into similar graduates, a better analogy is that of an educational winnower. Just like a winnower separates grains from chaff, the education system eliminates diversity and deviance through a process of attrition and culling.

The system winsnows in how it tests, teaches and selects who gets access to education based on factors like location, age, family support, gender, race, caste, cost and more. This vast attrition sacrifices human potential and cuts short many educational journeys. Standardized testing, rote learning, college admissions criteria, and rising tuition costs all contribute to homogenizing the outcome while filtering out differences. The author suggests this winnowing process is far more responsible for similar-looking graduates than any molding in schools.

Selective colleges offer better financial aid and resources than non-selective schools but turn away most applicants due to limited seats. There is a need to make top schools more inclusive.
Intelligence testing has historically been used to sort and track students, based on the flawed assumptions that intelligence is fixed and inherited. In reality, intelligence is malleable and influenced by environmental factors.
While standardized tests like the SAT can help with college admissions, they also have significant limitations and biases. IQ and aptitude are impossible to directly measure, and tests favor those of higher socioeconomic status.
Factors like education, environment, stress, and stereotype threats can significantly impact test performance in ways unrelated to a student’s abilities. The predictive power of IQ scores is limited as IQ changes over time based on experiences.
While achievement tests have replaced aptitude tests, they still suffer from many of the same issues in how they are used to select and filter students. True ability is more complex than any single test can capture.

The passage discusses how stereotype threat may negatively impact girls’ performance on tests by occupying their cognitive resources at a crucial moment. It suggests that stereotype threat acted as an “asymmetric distraction” during the test, diverting girls’ mental focus away from the test questions precisely when they needed to concentrate the most. This could undermine their performance even if they were otherwise fully capable. So stereotype threat may erect unwarranted cognitive barriers that differentially impact certain groups on high-stakes standardized tests.

The passage describes a mechanical engineering lab class at MIT where students are working on small robotics projects using circuit boards and batteries.
The professor warns the students about the dangers of lithium polymer batteries, explaining they contain enough energy to severely injure someone if they were to rapidly discharge. He calculates one battery contains equivalent energy to lifting a Honda Civic 10 meters.
The students have simple robot platforms called “Mini-Mes” to learn foundational mechanical engineering principles like coding microcontrollers to control motors. The goal is for them to eventually build autonomous robots.
Through this project-based learning, students are meant to gain skills in robotics, design thinking, problem-solving challenges as projects progress, and confidence in their own engineering abilities.
However, some students come into the class with more extensive engineering experience than others from activities like robotics competitions in high school. This creates a disparity in the “learning divide” between students of varying backgrounds and prior knowledge.

So in summary, the passage describes an MIT engineering lab using robotics projects to teach principles hands-on, but notes some students have a head start due to differing educational experiences before college.

Here is a summary of the key points about MIT’s Course 2.007 (Mechanical Engineering) robotics competitions:

Course 2.007 is known for its robotics competition at the end of each spring semester, where students design and build robots to compete against each other. Winning comes with lifelong bragging rights.
Some students come into the course with experience from robotics competitions or activities like BattleBots, giving them an initial advantage. However, the course aims to get all students engaged in building practical robotics skills.
The course emphasizes learning through hands-on application rather than just memorization. Students must immediately apply concepts from lectures to their robot designs and compete against each other. This motivating nature inspires deep learning.
Over the semester, students progress from building simple “Mini-Me” robots to fully designed competition robots. Homework includes both written problems and physical challenges to complete with their robots.
The competition aspect and desire to outperform peers drives students to spend extensive extra time in the lab, gaining valuable engineering skills beyond what’s required just for their grade.
The course MIT’s Course 2.007 provides a hands-on, project-based learning experience that is very effective but also extremely expensive to offer at a large scale due to costs of equipment, facilities, and personnel.
Educators have long dreamed of replicating high-quality education through machines or technology to scale it up, going back to early 20th century psychologists like Thorndike who envisioned interactive books controlling the learning process. In the 1950s, B.F. Skinner built “teaching machines” that aimed to personalize learning but proved overly simplistic and boring for students.
Around the same time, two prominent early psychologists differed in their views - Edward Thorndike took a reductionist approach focusing on basic behavioral elements, while John Dewey advocated a more holistic, contextualized model. Thorndike’s views went on to shape the standardized model of 20th century mass education more than Dewey’s.
Dewey helped found a “laboratory school” applying experimental techniques with a curriculum tailored to students’ interests, though the conception of “interests” was still flawed in ethnocentric ways common at the time. The chapter explores the debate between their differing scientific approaches and visions of education.
John Dewey and Edward Thorndike were both education psychologists in the early 1900s who were influenced by William James but had very different approaches.
Dewey opened the Laboratory School at the University of Chicago in 1896 to test his progressive theories about learning through hands-on experiences and social activities rather than rote memorization. The school focused on integrated, interdisciplinary learning.
Thorndike favored a reductionist experimental approach, breaking down learning into its smallest components. He sought to isolate the simple mechanisms of learning through controlled experiments.
Despite some early similarities, Dewey and Thorndike’s views proved irreconcilable. Dewey saw learning as a holistic, social process while Thorndike focused on isolating individual mechanisms through stripping things down experimentally.
Dewey left the Laboratory School after 8 years in 1904 and it failed to spread his progressive model widely. His ideas ran counter to the times which favored Thorndike’s more reductionist and controlled experimental approach to understanding learning.
William James conducted an experimental study on memory where he found that memorizing one long poem did not improve his ability to memorize another poem later, contradicting the view that memory was an innate “faculty.” This intrigued young Edward Thorndike.
Thorndike studied psychology under James at Harvard and decided to pursue a PhD in the field. He began conducting experiments on learning with young chicks in James’s home, observing that experience in mazes improved their performance.
Thorndike continued his animal experiments at Columbia, using cats in puzzle boxes. He quantified their learning over time and proposed his theory of trial-and-error learning and the “Law of Effect.” This held that behaviors followed by satisfaction become stronger through repetition.
Thorndike’s work helped establish the behaviorist approach in psychology championed by John Watson. Behaviorism sought to study behavior objectively without reference to inner mental processes. B.F. Skinner further developed these ideas through his experiments with animal conditioning and innovative teaching machines.
Thorndike and behaviorism had a major influence on twentieth century psychology, education practices, and approaches to animal and even military training during World War II through programs like training pigeons to guide bombs. Their work established psychology as an experimental, quantitative science of observable behaviors.

Here are the key points from the passage:

Skinner drew lessons from Thorndike’s work on operant conditioning and applied it across species, including his teaching machines project which aimed to provide immediate feedback like with his pigeons experiments.
Thorndike’s Law of Effect provided the conceptual underpinning for the widespread push for standardization in schools led by administrative progressives in the early 20th century who sought to impose order on a rapidly changing society.
Thorndike’s views that learning could be measured and replicated at scale supported standardizing curricula, school administration, and ultimately students themselves through sorting. This diverged from Dewey’s holistic approach.
One casualty was the traditional classical curriculum. Courses were refocused on direct applications to students’ futures rather than general intellectual development. Teachers had a diminished role in the new hierarchical system led by administrators.
Efficiency and quantification became priorities, separating worthy from unworthy students. Thorndike embraced this sorting function rather than a focus on improving intelligence for all. Standardized testing was used to compare students accordingly.
Thorndike pioneered standardized testing and IQ testing in schools in the early 20th century. He believed intelligence was determined at birth and IQ tests could reliably measure it.
IQ testing became widely adopted in schools by the 1920s, with students tracked into college preparation or vocational programs based on their IQ scores. This essentially created self-fulfilling prophecies.
The adoption of IQ testing was criticized by John Dewey and others, who argued it reduced individuals to numbers and perpetuated the status quo.
IQ testing was influenced by eugenics beliefs that different races had vastly different intelligence levels that could not be changed by education. Thorndike and Lewis Terman, a key proponent of IQ testing, held racist views.
While quantification brought some benefits, IQ testing disproportionately weeded out marginalized groups and its assumptions have since been discredited. Its legacy still influences modern standardized testing and dual educational goals of teaching and standardization.
Moving forward, education needs approaches that acknowledge learning’s complexity, like Dewey’s “outside-in thinking,” as well as Thorndike’s “inside-out thinking” which breaks things down mechanistically. A balanced approach may enable more intentional, effective education.
The passage discusses how spaced repetition or spaced learning is more effective for memory retention than cramming all study into one session. Spacing out study sessions over days or weeks leads to remembering 34% more vocabulary words or performing twice as well on math problems.
Spacing benefits have been shown in many domains from language learning to surgery to motor skills. Surprisingly, even simple organisms like fruit flies and brainless sea slug: s called Aplysia benefit from spaced learning. This suggests spacing taps into fundamental memory mechanisms.
Early researcher Hermann Ebbinghaus found spacing improved retention in his nonsense syllable experiments in 1885. Thorndike later coined the terms short-term and long-term memory. However, spacing effects were not a major focus of early 20th century behaviorist psychology.
Santiago Ramón y Cajal predicted in 1894 that memory must involve the formation of novel connections (synapses) between neurons. This helped shape the path towards integrating psychological and physiological understanding of memory.
The passage describes Eric Kandel’s work using the sea slug: Aplysia californica to study the neural basis of memory.
In the 1960s, Kandel sought a simple model system to understand memory at the cellular level. He was drawn to Aplysia due to its large, identifiable neurons and simple neural circuits.
Kandel hoped to adapt classical conditioning paradigms to Aplysia to correlate changes in neural circuits with learning. This represented the goal of finding a biological mechanism for memory storage.
After a medical residency at Harvard, Kandel joined a French laboratory studying Aplysia. There, he began pioneering experiments using techniques like intracellular recording to trace the effects of classical conditioning on identified neurons and circuits in Aplysia.
Kandel’s work with Aplysia laid important groundwork for discovering how experiences can induce cellular changes involved in memory formation. His reductionist approach provided insights not possible with more complex nervous systems.
Eric Kandel studied the sea slug: Aplysia and its gill withdrawal reflex, which could be conditioned through classical and operant conditioning experiments.
He isolated clusters of Aplysia neurons and showed that simple forms of learning like habituation and sensitization correlated with weakening and strengthening of synapses between neurons.
Kandel then did experiments on living Aplysia and found that sensitization, habituation, and classical conditioning of the gill withdrawal reflex correlated precisely with changes in the synaptic strength of motor neurons that control the reflex.
This provided strong evidence that memory formation occurs through changes in synaptic strength between neurons due to conditioning experiences. It supported an associative theory of memory storage in the brain based on Thorndike’s law of effect.
Kandel’s findings helped resolve debates about whether the mind is a blank slate or pre-programmed in some ways. Experiences determine which neural pathways are used through synaptic strengthening and weakening.
His work established a cellular mechanism for associative learning and memory storage, though it did not fully explain how networks of neurons can represent complex ideas and memories.
Edward Thorndike in the early 20th century postulated laws of learning based on simple experiments with animals. His work inspired later psychologists like Edward Tolman and B.F. Skinner.
In the 1960s-70s, Eric Kandel conducted experiments on learning and memory in Aplysia sea slug: s. He was unique in using electrophysiology techniques to study learning mechanisms at the cellular level, inspired by behaviorist theories.
Kandel’s work helped uncover two mechanisms of synaptic plasticity - short-term potentiation based on neurotransmitter release, and long-term potentiation (LTP) requiring new protein synthesis.
LTP was discovered independently by Bliss and Lomo in 1973, showing strong, long-lasting changes in synaptic strength induced by high-frequency stimulation.
Later work suggested LTP involves anatomical changes to synapses like growth of new dendritic spines and receptors. It may underlie long-term memory formation.
Significantly, Kandel found spaced training protocols produced longer-lasting changes similar to the spacing effect in learning. This provided early evidence LTP mechanisms could explain the behavioral spacing effect.

So in summary, Kandel built on behaviorist theories to utilize electrophysiology and uncover cellular mechanisms of learning inspired by Thorndike, helping establish LTP as a potential physiological basis for memory formation and the spacing effect.

The passage describes the author’s training process at Schlumberger to prepare for work on an offshore oil rig. The training took place at a facility in Edinburgh that simulated conditions on a rig. Trainees were subjected to unpredictable schedules, with systems being “sabotaged” and requiring repairs at all hours to mirror real emergencies.

At first the schedule was frustrating, but it helped concepts click through repeated practice over time in relevant contexts. The timing of repairs helped with retention through spaced repetition and revisiting topics when knowledge started to fade. This spaced learning contrasted with the author’s cramming strategies in university, which didn’t support long-term remembering.

Research has since found spaced repetition promotes long-term potentiation in the brain by convincing neurons information is important. Interleaving different topics also allows connections to form between concepts. The author’s training method incorporated both spacing and interleaving, building confidence. By the end they felt fully prepared for challenges that may arise on the offshore rig.

The author was taught ropes, cables, pulleys, and weights as a trainee on an oil rig. On his first day, his boss told him to climb to the top of a tall platform and ask a worker for a “long weight.”
The author climbed up in the freezing rain and asked the worker, who said to stay put. The author then realized he had been sent up there as a prank, to wait for a long time rather than get a long weight. It was a lesson for him to learn.
Years later, the author looked back nostalgically on the training but also with concern about climate change. He wondered if such effective training could be applied more broadly.
The story then discusses an experiment where scientists used optogenetics to activate neurons involved in a specific memory in mice brains. They were able to reactivate a fear memory even after seemingly erasing it, suggesting memories may be encoded in neural connectivity patterns in addition to synaptic strength.
This challenged existing theories but did not prove the alternative explanation either. While groundbreaking, the exact mechanism remains unclear and debated among neuroscientists.
Memory research faces gaps in understanding across scales, from genetics and individual neurons up to brain systems and behavior.
The Picower Institute studies memory at the neuronal/cellular level using techniques like optogenetics. The McGovern Institute studies larger brain systems and circuits using imaging tools like fMRI.
There is a large gap in knowledge between these levels - we don’t understand which specific cells encode memories or how cell assemblies form memories. Bridging this gap requires characterizing entire neural circuits.
Efforts are being made to close these gaps in both directions. Some studies have identified key synapses involved in learning in fruit flies, but fully characterizing the circuit remains difficult. fMRI provides a way to study brain regions from the systems level and inform cellular research.
Cognitive science faces gaps across many disciplines from genetics to psychology. Understanding differences and interactions across these scales is challenging but important for a complete picture of memory and learning.

The passage discusses two levels of research into memory and learning - cognitive psychologists who study thought processes and brain researchers who study how neural circuits underlying surface phenomena.

It then focuses on two neuroscientists, John Gabrieli and Henry Molaison, and their research at MIT’s McGovern Brain Institute. Gabrieli used brain imaging to study memory mechanisms, complicating earlier simplistic views. Studies of amnesiac patient H.M. after surgery revealed multiple memory systems, including explicit memories like facts and implicit skills-based memories. This led to the distinction between declarative and non-declarative memory.

Gabrieli studied Molaison extensively, finding he could improve at certain tasks without remembering practicing them, showing different memory types rely on different brain regions. This split short-term and long-term memory into explicit conscious memories and implicit skill-based memories. Further research found more memory types and mechanisms, advancing understanding of how memory is organized in specialized brain systems rather than uniformly.

In the 1980s, researchers like Kanwisher and Gabrieli were early adopters of fMRI, which allowed more accurate study of brain activity than previous techniques.
Kanwisher discovered the fusiform face area, a region in the temporal lobe specialized for face recognition. She located it functionally based on differential response to faces vs other stimuli, rather than anatomical location.
This functional localization approach revealed many specialized perceptual regions, challenging the view that the brain is a general purpose device. Regions respond most to specific categories like faces, places, bodies.
Intriguingly, there is a visual word form area next to the fusiform face area that responds to letters, though reading is an recent evolutionary development. Its consistent location across individuals is puzzling given the lack of evolutionary history with reading.
Kanwisher’s research helped establish the modern view of the brain as composed of distinct specialized components, made possible by advances in fMRI localization of function-defined regions.

Here are the key points from the summary:

Dyslexia is a reading impairment caused by problems in a specific memory region called the “letterbox” that processes individual letters and letter combinations.
Brain imaging research in the 1990s-2000s helped map out the neural pathways and regions involved in reading, including parallel “deep” and “surface” routes.
The deep route connects letters directly to word meanings, while the surface route connects letters to sounds first before meanings.
In dyslexia, the more circuitous surface route is most affected, interfering with connecting letters to sounds, like pronouncing words without letters.
Neuroimaging research by Gabrieli in 2000 used diffusion tensor imaging to show dyslexia may involve atypical development of long pathways connecting distant language processing regions in the brain.
This helps explain cognitive difficulties experienced by those with dyslexia at the psychological level, like trouble sounding out made-up words. It points to anatomical abnormalities that interfere with reading development.
Diffusion tensor imaging allowed researchers to map white matter pathways in the brain. Studies found some pathways involved in reading appeared less organized/more disorganized in dyslexic brains.
One pathway called the left arcuate fasciculus is important for phonological awareness, a crucial part of reading. It connects the back and front of the reading network. Abnormalities in this pathway could explain dyslexia symptoms.
Gabrieli’s research also proposed dyslexia may be related to a general problem with synaptic plasticity, making reading uniquely difficult due to its high cognitive demands.
The brain’s “letterbox” region is consistently located because it must tap into existing shape-processing pathways. Some argue written languages evolved to meet these brain constraints.
Early interventions focusing on speech/auditory processing can help identify children at risk of dyslexia before reading begins. Alternate brain pathways may also develop to compensate.
Dyscalculia (math learning disorder) is now recognized and may affect up to 40% of those with dyslexia, but it remained hidden longer as math skills are less crucial.
Dyslexia and potentially dyscalculia challenge historical views of intelligence and standardized education systems not built to accommodate such learning disorders.
The passage discusses the concept of a “state of unreadiness” or lack of preparedness to learn introduced in the study identifying neural signatures of readiness to learn for memorizing places. This unreadiness could potentially disrupt education like dyslexia.
The author provides a personal anecdote of struggling to understand concepts in fluid dynamics in university due to an inability to find meaning or connection, like “tissue rejection.” He later gained understanding working on an oil rig.
George Loewenstein’s theory of curiosity as a psychological drive like hunger is discussed. Brief curiosity triggered by new information gaps is analyzed through fMRI studies, though complexity makes patterns difficult to pinpoint.
While readiness states can be identified, curiosity is more complex to study mechanistically due to the many ways the brain reacts to interesting information. The drive of curiosity and anticipation of reward may boost long-term memory storage like monetary rewards.

So in summary, it discusses the existence of an unreadiness to learn state counterpart to readiness, personal experience with this, and neuroscience research on the related topic of brief information-gap driven curiosity as a psychological drive.

Researchers studied curiosity and its relationship to memory formation in the brain. They found that being in a state of curiosity boosts long-term memory formation for all types of information, through dopamine facilitation of hippocampal long-term potentiation (LTP).
The hippocampus appears to determine if incoming information is worthy of curiosity. If so, it signals reward regions to return dopamine signals to the hippocampus, telling it to form long-lasting memories of that information.
Psychologist Jacqueline Gottlieb proposed the “learning progress theory” - curiosity is triggered when a new chunk of information causes you to change or reframe your prior knowledge. This helps explain curiosity in more open-ended real world situations where information gaps are not clearly defined.
Factors like posing questions, connecting curriculum to students’ lives, and promoting digestible “information gaps” can help trigger curiosity in classroom settings and promote more effective learning. The brain’s curiosity and memory systems should inform how education is designed and delivered.
Brandon is falling behind on his robotics project for his 2.007 course. His classmates are making good progress on their designs for the final Star Wars-themed competition.
The competition involves robots completing tasks like spinning thrusters on wooden replica X-wing starships. This requires overcoming challenges like high-torque flywheels. Approaches being tried include gearboxes and gripping devices.
Brandon’s plan is to retrieve stormtrooper toys from a display using pincer-like scissors actuated by fishing line. But he is struggling to get a firm enough grip on the magnetized stormtroopers.
When his professor Amos Winter evaluates students’ progress, Brandon overhears praise for another student’s simpler but effective design. Winter provides criticism of Brandon’s complex scissors design.
Other students are showing more advanced designs incorporating wheels, lifts, and precision-cut parts. Brandon remains unsure how to progress effectively on his robot given the challenges.
Z has discovered a way to make his robot thruster spin very fast using a powerful battery, potentially allowing him to score many more points than other competitors.
Brandon is struggling with the design of his robot which uses scissors. He met with his instructor Winter for advice but felt Winter was discouraging without directly criticizing his design.
Winter actually did have major doubts about Brandon’s scissor design but couldn’t directly say so, as that would undermine the goal of teaching students to apply their knowledge independently.
An education researcher and physics professor uses examples to show that students often fail to apply conceptual physics knowledge they have learned, keeping it “inert.” They incorrectly answer questions even on basic topics they studied.
Hands-on projects and competitions were introduced to try and activate inert knowledge, but the problem persisted. A balance is needed between direct instruction and independent application/discovery for students to truly learn how to think with their knowledge.
Jean Piaget’s theories of cognitive development revolutionized the field of psychology in the 1950s and influenced modern educational practices. He proposed that children develop through distinct stages as they actively construct their understanding of the world through experiences.
Piaget observed that children don’t make mistakes due to lack of ability, but due to naive understanding. He saw learning as an active, creative process of inventing personal “rules of thumb” about reality through experiences, rather than just passive memorization.
Knowledge is built up over time through assembling numerous individual “schemas” or mental representations into a hierarchical structure. Learning involves both building knowledge banks as well as problem-solving strategies.
Piaget’s theories inspired a more student-centered, “outside-in” approach to education that focuses on hands-on learning and discovery. However, some critics argue this approach can be resource-intensive and make student progress difficult to measure. The proper role of direct instruction vs open-ended learning is still debated today.
After the Soviet launch of Sputnik, the US sought to close the perceived “knowledge gap” between American and Soviet students. MIT physicist Jerrold Zacharias secured funding for efforts in this area.
Zacharias attributed the knowledge gap partly to the influence of behaviorism in US education. He hired psychologist Jerome Bruner, skeptical of behaviorism, to help develop new approaches.
At a 1959 conference, Bruner advocated applying cognitive science findings to education. His subsequent book promoted Piaget’s cognitive model over behaviorism and sold many copies.
This helped spark the “cognitive revolution” in education. Piaget’s theories fit well with work being done by cognitive scientists at Harvard and MIT in the late 1950s-early 1960s.
One important figure was Seymour Papert, who studied with Piaget and brought his constructivist ideas to MIT. Papert developed the Logo programming language to teach kids about coding in a hands-on way.
Papert’s former student Mitchel Resnick now leads constructivist efforts at MIT, seeing value in having students actively construct knowledge through building things physically and computationally. However, Logo faced challenges in widespread adoption.
Mitchel Resnick developed Scratch, a visual programming language that allows users to code by snapping together blocks rather than using text. This makes coding more intuitive and fun.
Scratch projects often involve animating characters and is popular with children. Over 30 million people have registered on the Scratch site.
Resnick aims to promote creative, collaborative learning through projects like Scratch. However, some education researchers criticize this “constructionist” approach.
John Sweller conducted studies showing students learned math better by studying worked examples rather than solving problems themselves. This challenged the idea that general problem-solving skills can be effectively taught.
The “cognitive load theory” proposed that solving complex problems overloads working memory, reducing capacity to actually learn material. This suggests direct instruction may be better than discovery learning in some cases.

So in summary, it discusses the development of Scratch and debates between advocates of constructionist, project-based learning versus those who argue direct instruction is sometimes preferable based on theories of cognitive load and working memory limitations.

Researchers have debated working memory capacity since the 1950s when it was proposed to be around 9 chunks of information. Later studies found the limit was closer to 4 chunks.
Mikael Lundqvist conducted computer modeling research in 2011 that proposed working memory representations are encoded in bursts of neural activity, rather than continuously like previous theories suggested.
Lundqvist’s research at MIT using monkeys with electrode arrays provided experimental evidence supporting the burst model. This helps explain why adding a new chunk causes an old one to be lost, analogous to writing messages in wet sand that need to be refreshed before erasing.
Lundqvist’s model provides a compelling explanation for inherent limits in working memory capacity. Factors like instruction design, stereotype threat, and certain disorders can reduce available working memory and negatively impact performance.
Teaching approximation strategies and overlearning facts can help reduce demands on working memory during problem solving. The model also suggests similarities between neural activity underlying thoughts and voluntary movement control.
While questions remain about free will and volition, Lundqvist’s research helps further scientific understanding of the mechanisms that underpin working memory and cognition.
The debate around inside-out vs outside-in approaches to education comes down to questions about how best to teach skills like problem-solving and how much control learners have over their cognition.
A 2009 book attempts to give both sides a fair hearing but ends up relying more on speculation than evidence. Like geopolitical disputes, the debate risks becoming needlessly fractious.
At the level of cognitive psychology, ideological differences inevitably emerge around questions of how and for whom education should benefit.
Multiple viewpoints are needed as no single approach is perfect. Educational practices should integrate diverse perspectives and update based on new evidence, taking a Bayesian approach.
While some skills like calculus require direct instruction, discovery learning is also important to develop skills like engineering. Both approaches have value and an optimal blend is needed.
The field must be pragmatic and not wait for a perfect solution, integrating multiple viewpoints and updating practices over time based on accumulating evidence. A Bayesian stance of open-mindedness is advocated.
The passage discusses the idea that certain theories of brain function and learning styles proposed based on neuroscience research led to problems when implemented in pedagogy.
Howard Gardner’s theory of multiple intelligences was misinterpreted and led to the idea of “learning styles,” which claims students have specialized learning needs depending on their brain type (visual, auditory, etc.). However, research shows matching teaching to purported learning styles does not improve outcomes.
The concept of left brain vs. right brain dominance as determining creativity vs. logic is also presented as a neuromyth with little evidence to support actual differences in brain function between individuals.
In the 1980s-90s, the “neuroeducation” movement argued children needed enriched sensory experiences early in life during a critical period of brain development to maximize potential. This led to questionable educational practices like playing classical music for young kids.
While brain science can potentially inform education, the passage argues theories need to be verified through cognitive psychology first before direct implementation, to avoid repeating mistakes of past “inside-out” thinking based directly on neuroscience. An integrated approach considering both brain and behavioral research is advocated.
Robert Bjork was a pioneering cognitive psychologist who studied memory and forgetting. He discovered that forgetting plays an important role in long-term learning and memory formation.
His early research showed that memories with high retrieval strength but low storage strength (things you can temporarily access but easily forget) and high storage strength but low retrieval strength (memories you can’t access in the moment but could recall with cues) both existed.
Bjork and his wife Elizabeth later developed a theory that storage and retrieval strengths interact with each other, not just that both are needed. Forgetting allows memories to be strengthened through retrieval at a later time.
Bjork conducted multiple studies manipulating ways to induce forgetting, such as interleaving information, changing environments, and spacing out study sessions. When subjects were later cued, the “forgotten-then-retrieved” memories were stronger and more accessible than those never forgotten.
Spacing and interleaving study likely boosts long-term retention partly through the mechanisms of forgetting and re-remembering. Directed forgetting, where subjects forgot info after being told to, was particularly intriguing to Bjork.

So in summary, Bjork was a pioneer in showing that forgetting plays an important and beneficial role in long-term learning and memory formation, contrary to initial assumptions. His work revealed interactions between storage and retrieval strengths.

During the 1970s and 1980s, Elizabeth Bjork had to take a background role in her research due to nepotism laws that penalized married couples where both spouses worked at a university.
Robert and Elizabeth found ways to collaborate and publish separately despite the informal rules against collaborating directly.
In 1978, they published a study examining how retrieving memories impacts later recall. They discovered that retrieving items boosts their future recall but depresses recall of competing items.
This unexpected finding laid the groundwork for their new theory of disuse, which directly challenged Thorndike’s theory that forgetting is due to decay over time.
In their seminal 1992 paper, they put forward the idea that forgetting serves an adaptive purpose by weakening associations to irrelevant information. Retrieving memories increases their “storage strength” and longevity.
Their theory flipped the script by proposing that memory capacity is essentially limitless and forgetting helps optimize retrieval of relevant information by pruning irrelevant associations. This established effortful retrieval as important for long-term learning.
The theory reconciled ideas of unlimited memory storage with finite retrieval capacity and helped explain why it is easier to relearn old information compared to new information.

The passage discusses how the theory of effortful retrieval and desirable difficulties has been applied in education. It notes how interleaving different tasks, like practicing different types of serves in badminton, leads to better long-term retention than massed practice of a single task. Pretesting, or taking practice tests, is highlighted as an effective technique that requires effortful retrieval but is resisted by students. The passage then questions why effective learning strategies like effortful retrieval feel difficult, noting the brain rewards other cognitively demanding activities. It discusses how research into metacognition, or how we think about our own thinking, helped address this question and fit with the theory of disuse. Metacognition proved a slippery concept to analyze but studies showed people can be biased in judging their own knowledge, such as overestimating what they will remember. This research into how we monitor our own learning supported and influenced the work of Robert Bjork and other educators.

Schools like FIU Law School traditionally tried to improve bar exam passage rates through “silver bullet” approaches like focusing resources on particular classes, but this did not work.
Louis Schulze, head of academic support at FIU Law, decided to implement a program teaching students how to effectively learn and retain large amounts of information using techniques from cognitive science research, known as “desirable difficulties.”
This includes spacing out study sessions rather than cramming, self-testing, and interleaving different subject material. Schulze showed students research on forgetting curves to convince them of these methods.
The program spans all six semesters of law school, starting with an optional intro course and becoming mandatory for the bottom 20% of students in later semesters.
Since starting the program in 2015, FIU Law has risen from 9th to 1st in Florida bar passage rates, and maintained a top 15 ranking nationwide.
The program particularly helps students at risk of failing, boosting their pass rates from the 50-60% range to the high 70s-low 80s, keeping them in law school and the profession. Schulze credits these students for moving the needle on FIU’s results.
Robert and Elizabeth Bjork have done research showing that incorporating “desirable difficulties” into learning, like retrieval practice and spacing out study sessions, can significantly improve student outcomes.
At Florida International University Law, changing routines to include these techniques produced remarkable results, with attrition decreasing by over 50%.
Their research challenges both the assumption that learning styles come naturally, and an overreliance on worked examples without enough productive struggle. Getting students to generate their own responses is most powerful.
While inspiring, their story also shows that many students are likely failing unnecessarily due to flaws in traditional education approaches. We now know ways to prevent this wasted potential.
Applying cognitive science findings to education faces challenges of scale, flexibility, and accommodating individual differences. New institutions may be needed to fully realize cognitively-optimzed learning.
However, places like MIT and FIU Law have shown meaningful changes are possible even within traditional structures, by incorporating techniques like retrieval practice, spaced learning and active generation of responses.
TEAL (Technology Enabled Active Learning) was a new approach to physics education pioneered at MIT by Bob Belcher in the late 1990s/early 2000s. It aimed to replace traditional lectures with active, collaborative learning activities.
TEAL classrooms featured round tables instead of rows of seats, allowing for group work. Lessons incorporated hands-on demonstrations, interactive simulations, and peer instruction techniques.
The transition to TEAL was challenging and faced resistance from some faculty used to traditional lectures and from skeptical students. Administrators provided support that helped the program evolve over several years.
Research studies found that TEAL students significantly outperformed those in traditional lectures on conceptual tests, with less failure rates. Women especially benefited from the collaborative environment compared to traditional physics courses.
By addressing shortcomings over time and providing data on learning gains, TEAL won over critics and established itself as the new model for introductory physics education at MIT. It aimed to make physics more accessible and reduce barriers that had historically disadvantaged some groups.
Andrew Bell arrived in Madras (now Chennai), India in 1787 intending to stay temporarily but ended up staying 10 years.
At this time, India was under growing British colonial rule after the British defeated the French. Many Indian women had children with colonial soldiers who then died or left, leaving the women and children.
The colonial government considered these children to be military orphans even if the mothers were still alive, and aimed to provide housing, feeding and education for them.
This challenge of caring for these “orphans” became Bell’s primary concern during his time in Madras. He devised a new system of mass education focused on knowledge rather than time, with the children teaching each other.
This early mass education system anticipated modern approaches like flipped learning but was ultimately discarded. Bell’s ideas later influenced the development of the Madras System of education which spread globally.
Andrew Bell developed a system of education called the Madras System or Monitorial System while serving as superintendent of the Male Orphan Asylum in Madras (now Chennai), India.
He observed Indian children using mutual instruction methods, with older students teaching younger ones. This inspired him to implement elements of mutual instruction at the asylum.
Bell had students write letters in wet sand trays, which engaged the students and made teaching easier. He had older students teach younger ones using this method.
Bell organized students into classes of varying levels. Students could advance through the classes at their own pace by demonstrating mastery. Those who struggled could be demoted temporarily.
Within each class, Bell paired students as tutors and pupils. Tutors received additional instruction to help their pupils keep up with the class. This exalted the tutors and enabled struggling students to not fall behind.
Bell published pamphlets spreading word of this Monitorial System, which focused on student-led, self-paced learning with older students teaching younger ones to aid the overworked teachers.
Andrew Bell developed an innovative new system of education at an asylum school in Madras, India in the late 18th century. Key aspects included peer teaching, short lessons, hands-on learning, and a rewards-based system instead of punishment. This reduced costs significantly.
When Bell returned to Britain, his system began spreading widely through schools set up by organizations like the National Society. It was also exported globally.
However, Bell’s rival Joseph Lancaster also developed a similar “monitorial” system. Lancaster focused on non-denominational Christian instruction, which appealed more in the US. Lancaster found champions like DeWitt Clinton who helped establish many schools in Philadelphia and New York.
The new American republic saw mass education as crucial to maintaining representative democracy against forces of anarchy and monarchy. Lancaster’s low-cost approach was very attractive. However, even supporters recognized the schools still struggled with disobedience and other issues beneath the surface.
Joseph Lancaster experimented with creative punishments to discipline students, including tying them up or putting them in baskets suspended from the ceiling. This alienated allies and contributed to his frequent job changes.
Monitorial schools spread worldwide in the early 1800s but started declining by the 1850s as self-contained classrooms taught by trained teachers became the norm.
In the US, Lancaster’s “pauper schools” took on a stigma as only suitable for other people’s children. Advocates like Horace Mann promoted professionally-run public schools to produce model citizens.
The monitorial system was not practical long-term as it relied on students teaching each other for free, which was not stable or scalable. Competing paid teaching jobs also undercut the model.
While it spread widely due to its low cost, the monitorial system ultimately failed as a “technological” solution that could not meet the demands of a developing modern education system on a long-term, sustainable basis. By the 1850s it had been replaced almost everywhere.
42 Silicon Valley is a free coding academy located near Silicon Valley that was established in 2015 by French billionaire Xavier Niel.
It has an untraditional approach - it does not require test scores, essays, or transcripts for admission. Instead, it accepts all applicants into a 28-day trial period called a “piscine” where students distinguish themselves solely through performance.
The school is self-contained and isolates students from the outside world. Students live and spend most of their time on campus.
The entire curriculum is arranged in sequential stages like a video game. Students must complete coding projects to advance through the stages, with the goal of reaching stage 21. No student has ever completed all stages.
Despite its unconventional approach, 42 Silicon Valley students have found jobs at major tech companies in the area. The stage-based structure may contribute to this success by producing dogged, insightful workers, though it is difficult and expensive for most schools to replicate.
The passage describes Claire Wang, a gifted young student who excelled in academics from a young age and grew bored with traditional schooling.
She discovered the field of memory sports and trained intensively in techniques like the method of loci. This allowed her to memorize things like decks of cards and perform feats of memory.
At age 13, she competed in and finished third at the USA Memory Championship held at MIT, memorizing large amounts of information quickly.
After this, local journalists were interested in her story as a young competitor. She discussed feeling limited by traditional schooling where everyone learns the same things at the same pace.
Looking to the next school year, Claire said she would be attending Ad Astra, a secretive school co-founded by Elon Musk and housed at the SpaceX headquarters. The passage provides details about the unique SpaceX facility and campus.

In summary, the passage profiles gifted young student Claire Wang and her success in memory competitions, as well as her plans to attend the innovative Ad Astra school in an effort to further challenge herself intellectually.

Neuralink and other companies led by Musk aim to develop brain-computer interfaces.
Ad Astra is a school located near Musk’s SpaceX buildings that focuses on simulations as a form of project-based learning.
Students are constantly involved in elaborate simulations designed by co-founder Josh Dahn that can last hours or months.
Students also manage their own virtual currency called Astras through developing business concepts and holding economic simulations.
The school aims to prepare students to be thoughtful future leaders by modeling an advanced capitalist world and assigning leadership roles in simulations.
Through complex simulations, Ad Astra gives students experience applying knowledge to real-world problems in a way that is more engaging than traditional schooling.
The case study focuses on a student named Claire who thrives in the personalized, simulation-based learning environment after struggling in traditional schools.

So in summary, it describes Musk’s involvement in brain-computer interface companies and provides an overview of the simulation-based educational approach used at Ad Astra, a school located near one of Musk’s companies.

Dahn is the founder of Ad Astra, an experimental school located within the campus of Elon University that was uniquely well-resourced and given freedom to try new approaches. Dahn says he wouldn’t have been able to create such a school anywhere else.
While standalone experimental schools can be effective, their approaches are difficult to scale up in a way that maintains quality without huge resources. They also operate outside the norms of the traditional education system.
AltSchool took an “outside-in” approach to research by recording students in classrooms and using machine learning to identify effective practices. However, recording students raises privacy concerns.
AltSchool’s founder, Max Ventilla, believes most edtech has historically oversimplified problems in a way that only addresses parts of the learning cycle. It aims to support the full experiential learning cycle through personalized lesson playlists and tracking student progress.
Ventilla describes his daughter leading her own parent-teacher conference using AltSchool’s software to discuss her goals, progress, reflections, and standardized test results in core subjects.

So in summary, it discusses the challenges of scaling unique school models and how AltSchool aimed to support the full learning process through personalized lessons and tracking student data.

The passage discusses the philosophy and approach of AltSchool, a now-defunct education startup founded by Dario Amodei that aimed to reinvent K-12 schooling using technology and a personalized learning model.
AltSchool emphasized developing students’ sense of self and enjoyment of learning over rote skills acquisition. However, the founder acknowledged the importance of subjects like math and making sure students understand basic concepts.
While AltSchool originally operated laboratory schools, it eventually shifted focus solely to an educational software platform. This pivot highlighted the challenges of applying a tech-based “outside-in” model to traditional schools.
The passage then contrasts AltSchool’s approach with Montessori education, as exemplified by Wildflower Montessori schools. Montessori focuses on hands-on learning through carefully designed educational materials, self-directed activity, and developing sensitivity to the needs of young learners.
Research suggests Montessori education may help close achievement gaps and lead to better long-term academic and behavioral outcomes compared to traditional schooling, especially for preschool-aged children. However, its holistic model can be difficult to study and scale systematically.
Montessori literacy instruction is highly phonetic, with students learning letter sounds before names. Materials help develop fine motor skills for writing.
Students write letters while saying their sounds, then put letters together to decode words phonetically (e.g. “cat”).
Not all Montessori schools adhere closely to Maria Montessori’s methods, so quality can vary.
Wildflower Montessori was founded in Cambridge, MA due to lack of high-quality Montessori schools. It aims to spread organically through “mitosis” - splitting existing schools when demand grows.
Wildflower schools focus on cultural inclusivity and serving diverse backgrounds. Individualized learning helps close achievement gaps.
Small class sizes and lack of administration keep costs lower than traditional schools, around $12-15k per student.
Wildflower uses teacher records and surveillance technology in some schools to collect additional student data for research purposes. The value and ethics of this approach are debated.
Overall Wildflower aims to expand access to high-quality, low-cost Montessori education on a large scale through decentralized, self-replicating schools.

This passage discusses different approaches to personalized education - inside-out vs outside-in. Inside-out approaches aim to automate the teaching process by breaking down learning into constituent parts and building a system from the ground up. Outside-in approaches keep teachers central and focus on experiential learning through projects and discovery.

42 is discussed as an example of an outside-in approach that has few teachers but uses peer-to-peer learning. Students teach each other through projects and assessment. This appears to work through social learning and growing the pool of institutional knowledge.

Both inside-out and outside-in approaches now show promise for scaling personalized education. Inside-out uses data analytics and AI to meet each student’s needs without teachers. Outside-in schools are also experimenting with technology to scale human-centered models.

Overall, the passage argues that both approaches have potential benefits and risks. Inside-out risks oversimplifying learning but may facilitate reform. Outside-in models face challenges with scale but respect each student’s complexity. More research is still needed to progress personalized education for all students.

MIT made a strategic bet on developing online, open educational resources and personalized learning approaches (“inside-out education”). This included founding MITx to offer MOOCs.
One early student who benefited was Battushig Myanganbayar, a high schooler in Mongolia. He took MIT’s inaugural MOOC on circuits and electronics in 2011 and excelled, getting a perfect score. This opened up opportunities for him.
MOOCs showed potential to reach learners who otherwise lacked access to education due to geography, costs, or other barriers. However, simply providing online courses was not enough - other supports were also needed.
The edtech sector grew rapidly but also encountered problems as some companies developed highly personalized instructional systems without fully understanding learning. There were concerns about potential overconfidence and hype.
MIT remained committed to developing a technologically advanced, personalized inside-out education system but recognized it had not been fully achieved yet and faced external pressures. The roots of MIT’s efforts stretched back to the late 1990s with initiatives like creating the first online computer science course with tutorials and the founding of OpenCourseWare.
The author reminisces about how his learning experience improved after leaving his job in the oil industry. With spacing, interleaving and retrieval practice from his training, fundamental concepts became easier to recall and apply to new learning.
He went back to school and eventually got a job teaching at MIT. He noticed Course 2.007 helped students learn engineering concepts better through hands-on projects compared to traditional lectures. Students were motivated by competing robotics projects rather than just grades.
The author and colleague David Brock came up with the idea of stripped-down RFID technology using inexpensive passive tags with IDs and data stored online. This led to the founding of the MIT AUTO-ID lab to develop and commercialize the technology.
The author took on a huge amount of new learning to understand the electrical, manufacturing, logistics and business aspects needed to implement the RFID standard. Despite the difficulty, he was now able to integrate new knowledge effectively through strategies like collaborating with others.
RFID technology is now widely used globally thanks to their efforts, including for supply chain management, retail, toll payments and other applications. This underscores how the author’s improved learning abilities enabled an impactful career change and innovations.
MIT originally planned to establish new campuses around the world but changed course after Stanford launched online courses on Coursera and Udacity.
Anuj Agarwal, head of MIT’s computer science lab, suggested creating a “virtual MIT for the whole world” through online courses. This proposal gained support.
MIT announced MITx in December 2011 with the goal of launching its first course in February 2012. Agarwal took on leading this first circuits course himself on a tight timeline.
The course development involved top experts building problem sets, labs, simulations, and other tools. Agarwal also created new lecture videos using a tablet.
Uniquely, Agarwal taught the online course concurrently with an on-campus version to provide real-world validation. This “flipped” the classroom experience.
The first MITx course had tens of thousands of students and proved successful. Around this time, MIT partnered with Harvard to create edX, a joint nonprofit online learning venture leveraging the MITx platform. This would soon host courses from many universities.
MOOCs initially received a lot of hype and optimism in the early 2010s, with predictions that they could help educate billions of learners worldwide. However, expectations began to fall short within a few years.
Enrollment growth stalled and completion rates remained low. Socioeconomic data also showed MOOCs disproportionately benefited more affluent regions and populations, rather than reaching the world’s poorer populations.
While MOOCs continued adding students, critics argued they were “dead” by 2017, failing to live up to early promises. However, providers like edX were still growing enrollment numbers.
By the mid-2010s, the broader online edtech sector boomed with over 15,000 startups pursuing various approaches like personalized learning, blended models, and virtual schools.
Machine learning algorithms also began playing a bigger role, allowing more individualized paths through content. Companies like IBM applied these techniques to offerings like Watson to improve question answering abilities.

So in summary, while MOOCs faced challenges meeting initial goals, online education continued growing more diverse with new approaches, and machine learning began powering more personalized learning experiences.

The passage discusses the rise of artificial intelligence and machine learning in education. It focuses on IBM Watson and how Watson was adapted for education through products like Watson Classroom and Watson Tutor. These aim to map a student’s knowledge and provide personalized learning by tracking their progress on topics.

However, the passage notes several potential issues with AI/ML-powered education. Algorithms may break topics down too finely without integrating knowledge properly. They also risk “cognitive harms” like overfocusing on short-term results from massed learning. Bias is also a concern, as seen with an essay-grading system that assigned lower scores to African American students’ essays.

The passage argues that while personalized learning tracks what students know, it may lack other important factors like motivation and curiosity. It’s also possible important nutrients for learning could be missing. Overall, machine learning shows promise but also risks in education that need to be addressed and monitored as these systems develop.

Edtech is being used in poor, under-resourced school districts in Mississippi to replace teachers due to a severe teacher shortage, especially in the rural Delta region. Technologies like online courseware platforms are being used without teachers to guide students.
This misuse of edtech is having negative educational outcomes. Students struggle without teachers to answer questions. It encourages narrow test preparation over broad learning. Facilitators focus on discipline rather than instruction.
Research shows students generally perform better with a combination of online and in-person instruction compared to fully online. Personalized online programs can be effective with one-on-one support.
While edtech has potential to improve access and learning, its effectiveness depends greatly on proper implementation and social/institutional context. It risks benefiting wealthier schools and harming under-resourced ones if not applied judiciously. The technologies may work well with teachers but fail students in real-world situations of dire need without support.
For edtech to fulfill its promise, human involvement and local conditions must be carefully considered to avoid negative unintended consequences, especially for disadvantaged communities. More research is still needed on different models.
Educational technologies today risk poisoning future opportunities if they harm students, as seen with Andrew Bell’s early monitoring schools that focused on discipline over learning and reinforced associations of schools with poverty.
Widespread use of online credit recovery courses makes assessing quality difficult but appears common, with 90% of US districts engaging in some form of it in 2011.
Continuously collecting granular student data from online learning raises privacy and sorting concerns. Over-surveillance risks selecting for a specific personality type rather than improving learning, and prevents students from taking risks without permanent penalty.
However, machine learning also shows potential to create more personalized, culturally-aware education beyond what’s possible today, as seen in Fox Harrell’s work exploring cognitive metaphors.
Cutting-edge edtech may develop outside rigid institutions, through leapfrog adoption in developing areas or nontraditional programs, as the “educational winnower” resists challenges to its methods of sorting students. Improving learning alone is not enough; the system’s focus on designating students as wheat or chaff must also be addressed.
The passage revisits Hebb’s model of memory which was first introduced in chapter 2. It discusses how memories can fade over time without continued stimulation of the synapses involved.
It suggests that the reader may have previously forgotten Hebb’s name since it was not revisited. Competing names or concepts could have crowded it out of memory.
The passage presents an opportunity to relearn about Hebb’s model of memory in order to take advantage of spaced practice and repetition to better cement it in long-term memory.
It encourages the reader to try remembering Hebb’s name this time to see if repeated exposure through spaced practice results in stronger memory retention.

So in summary, it discusses why revisiting concepts from earlier in the book is important for long-term memory formation based on Hebbian principles of synaptic plasticity and spaced repetition. It uses Hebb’s name and model of memory as an example to test this idea.

The passage discusses MIT’s strategy of offering “MicroMasters” credentials for online learning as part of graduate degree programs. Key points:

MIT created MicroMasters programs by taking existing online MITx courses and packaging them as a credential worth certifying on its own.
This credential could then serve as admission criteria and credit toward a subsequent on-campus master’s degree, combining online and in-person learning.
The first MicroMasters was for Supply Chain Management, led by Yossi Sheffi. It aimed to broaden applicants and improve diversity in the master’s program.
Two students, Paulina Gisbrecht and Srideepti Kidambi, enrolled in the MicroMasters from Germany and India respectively. They found it flexible and rigorous preparation for career advancement.
The MicroMasters proved an effective “conversation starter” for job interviews and Kidambi was hired by Rent the Runway based partly on her MicroMasters progress.

So in summary, MIT pioneered the MicroMasters approach to package online courses as stackable credentials for graduate education and careers.

Two women, Gisbrecht and Kidambi, enrolled in MIT’s supply chain MicroMasters program while working full-time jobs. They found the courses intellectually stimulating.
Gisbrecht did well in the first two courses but struggled on the final exam for the third course. Kidambi’s boss was supportive of her studies.
Kidambi used concepts from one course to develop a new distribution strategy for her company, Rent the Runway, which expanded their operations.
After 16 months, both women earned the MicroMasters credential. They then applied to the on-campus master’s program.
Out of 300,000 registrations for supply chain courses, 1,800 earned the MicroMasters, and 15% of those applied to the on-campus program. Applicants from the online program tend to be high achievers.
The MicroMasters program allows for a wider and more meritocratic selection of students compared to traditional admissions. Blended students from the online program often outperform traditionally admitted students in the on-campus program.
The author discusses how MIT’s Supply Chain MicroMasters program has expanded beyond just providing credit towards an MIT master’s degree. 21 other universities now also accept credit from the online coursework.
MicroMasters programs aim to make high-quality education available to more people worldwide by separating out the “stuff you learn” (online courses) from complex problem solving activities done on campus.
This model rejects the idea that exclusivity and only accepting a limited number of students is inherent to higher education. Students can demonstrate their competence through online coursework alone.
However, complex problem solving and hands-on learning (“Manus”) remain important for truly mastering a subject and being able to apply knowledge. This is best done through on-campus programs, which necessarily have limited capacity.
The author argues hands-on learning is crucial for creating learners who can make real-world impact. He provides examples of engineers like Woodie Flowers who benefited tremendously from hands-on problem solving experiences.

So in summary, the MicroMasters model expands access to core course content while still valuing hands-on, collaborative learning experiences, though those remain limited to on-campus programs for now.

Richard built an impressive robot called Tornado that could autonomously and manually spin the upper and lower thrusters very fast, scoring 937.5 points in the first round, the highest so far.
Most other robots fell to thruster-spinning robots, which proved the winning strategy this year.
Brandon and his roommate Josh each built paired thruster spinners and worked together, with one controlling while the other assisted. They advanced through the first rounds.
Amy Fang’s beloved robot Dodocopter fell in the first round when it got stuck in the middle trench.
Brandon and Josh gave themselves two chances to advance by working on each other’s robots, but Josh was eliminated when his robot also got stuck in the trench.
Brandon advanced to the round of 16 against James Li, who also had partner assistance. Brandon scored higher by spinning his thruster very fast, while Li’s robot struggled with alignment issues.

So in summary, it outlines the key robots and results from the initial rounds of the robot combat competition, with Richard and his Tornado robot standing out as the highest scoring in the first round.

The passage describes a robot combat competition held at MIT between students in Course 2.007. It focuses on two competitors in particular, Brandon and Z.
Z devised a strategy of using a smaller “sabotage” robot to interfere with opponents while his larger robot spun thrusters. This proved tricky to implement but crowds enjoyed the idea. Z made it to the semifinals.
Brandon faced a close match against a skilled opponent from BattleBots but was able to eke out a 5-point win thanks to a loophole allowing stormtrooper placement.
The crowds were energized by Brandon and Z’s displays of confidence and mastery, just as spectators enjoy seeing in professional competitions.
The competition demonstrates how hands-on learning and competitions at MIT help students develop “rational self-esteem” in their abilities and see the real-world applicability of their skills, above and beyond just theoretical knowledge. This fluidity and precision is what draws crowds to admire technical mastery.
Course 2.007 held robotic competitions where students designed robots to complete tasks like attaching to an X-wing starfighter. In the semifinals, groups from different lab sections competed against each other.
One group, led by Z and Brandon from an alternative lab section, had gained more confidence and skills in robot design through hands-on learning approaches. However, in the semifinals the favorites Richard and Tom advanced to the finals instead.
In the consolation match between the remaining semifinalists Z and Brandon, both were determined to win. During the match, Z deployed multiple coordinated robots while Brandon tried to reach the top of the X-wing. The summary was cut off before describing the outcome.
The passage discusses how hands-on, scaffolded learning can nurture students’ “creative ego” or self-confidence compared to debates around directly teaching certain skills. It also references the value of alternative learning pathways that combine online courses, bootcamps, and apprenticeships over traditional degree programs.

The passage describes an intense robot combat competition between two students, Brandon and Z. Their final battle ends in a tie, and a lab instructor uses homemade scales to determine the winner. Brandon’s robots are slightly heavier, making him the victor.

The passage then draws parallels between educational changes throughout history in response to major economic transformations. The Erie Canal in the early 1800s opened new national markets and disrupted local economies, leading to demands for mass public education. Similarly, the Industrial Revolution in the late 1800s automated many jobs and created large corporations, again changing economic structures and mobility.

Today, forces like globalization, automation, and rapid technological change are disrupting job markets and income mobility once more. This echoes earlier periods and demands an education system that can both keep up with changes while teaching timeless skills like problem-solving, critical thinking, and developing one’s talents. With advances in cognitive science and new educational tools, now may be an opportunity to avoid past mistakes and shape schooling to better support learning processes and meet shifting needs. An effective approach may integrate both conceptual knowledge and hands-on applications in a flexible, accessible framework.

Here is a summary of the key points from the acknowledgments section:

The authors thank William Rosen, their late co-author, for initially intending to write this book on learning together.
They thank their editor Yaniv Soha and agent Eric Lupfer for their guidance and support throughout the writing and editing process.
They acknowledge support from MIT, including David Shrier, Lisa Schwalli, and Laura White who helped manage the project.
The book was made possible by vision and support from MIT’s Office of Open Learning and people leading innovative educational projects there.
Within and beyond MIT, many luminaries provided insights and feedback that helped shape the book, including numerous professors, researchers, educators, and thinkers in the learning sciences field.
Outside help and support came from friends, family members, and others who read drafts, engaged in discussions, or provided places to write and stay during the process.
In conclusion, they thank all who contributed in any way, acknowledging it was likely not possible to list everyone individually. The book was very much a community effort.
John Dewey was a leading American philosopher and educator in the early 20th century who argued that learning should be an active, experiential process rather than passive absorption of facts. He founded the Laboratory School at the University of Chicago to implement his progressive pedagogical ideas.
However, Dewey’s ideas were criticized by those who thought they downplayed systematic acquisition of knowledge. There was also debate between scientific and child-centered approaches to education.
Edward Thorndike laid the foundations of educational psychology and brought a data-driven, empiricist approach. He pioneered testing and measurement of learning outcomes. Though his ideas differed from Dewey’s, the two respected each other’s work.
By the early 1900s, there were competing philosophies between progressive educators inspired by Dewey and a new scientific/psychological perspective championed by Thorndike and his followers. This set the stage for ongoing debates around student-centered vs standardized testing approaches. Both camp had valid viewpoints but also limitations.
Dewey’s Lab School had an impact but his ideas were difficult to implement at scale. Thorndike’s empiricist, measurement-focused perspective gained influence in the developing field of educational psychology and shaped the structure of modern schooling in America.
Edward Thorndike developed the theory of connectionism, which posited that learning occurs through the forming of associations between stimuli and responses. He advocated for learning through practice and repetition.
Thorndike’s Law of Effect stated that responses accompanied by satisfaction are more likely to be repeated, while those resulting in discomfort are less likely to be repeated. This influenced the use of rewards and punishments in education.
His experiments on animal learning were influential in establishing psychology as a scientific discipline. However, his views on intelligence testing and the inheritability of traits were controversial and promoted by eugenicists.
Thorndike applied principles of connectionsm to education, advocating for breaking learning down into discrete elements and testing students frequently with standardized exams. This influenced the adoption of scientific management approaches in schools.
Thornkike’s theories led to a focus on measurable outcomes and quantifying school performance, which some argue had unintended consequences like ability tracking that disadvantaged marginalized groups. Others argue it helped improve efficiency in schools.
His ideas remained influential for decades and shaped the development of educational psychology as a field focused on applying experimental methods to improve teaching methods. However, his theories have also received criticism for being too rigid and behaviorist in orientation.
Eric Kandel achieved important discoveries about classical conditioning and memory in Aplysia at the neuronal level through his work in the 1960s-70s. This included discoveries about the role of serotonin and cyclic AMP in sensitization and synaptic plasticity.
Kandel’s team helped establish long-term potentiation (LTP) as a candidate mechanism for memory formation by showing it can last days in Aplysia. Later work showed LTP in the hippocampus could last months.
Optogenetics techniques since the 2000s have allowed researchers to activate specific neural ensembles (“engrams”) involved in memory formation and recall fear memories in mice. Studies in the 2010s provided evidence these engram cells are crucial for systems memory consolidation.
Memory was once thought to reside in individual neurons but is now understood to involve networks and interaction between brain regions like the hippocampus and neocortex. The precise role of different brain regions continues to be explored using tools like fMRI.
Seminal cases like patient H.M. showed memory involves multiple systems beyond just the hippocampus. Studies of implicit memory in amnesiacs provided evidence of dissociable memory systems in the brain.
While early models pointed to a single “engram,” newer evidence suggests memory is distributed across networks and dynamic, involving synaptic remodeling over time. The nature of the physiological trace of memory remains an active area of research.
The cognitive revolution in the 1950s-60s brought cognitive sciences like linguistics, psychology and computer science into education. Figures like Piaget, Chomsky and Seymour Papert influenced thinking around how learning occurs.
Papert pioneered constructionsim theory which viewed learning as an active building process through interacting with computational objects like the programming language LOGO. This influenced the development of Scratch.
In the late 1980s, cognitive load theory emerged from the work of John Sweller arguing that problem solving places a high “cognitive load” on working memory. Worked examples reduce this load and aid learning.
Studies found worked examples helped learning in many domains like math, statistics, geometry and programming. Sweller argued educators should make greater use of worked examples instead of heavy problem solving approaches.
The cognitive revolution and emergence of cognitive sciences like cognitive load theory transformed understandings of teaching and learning. This influenced movements towards greater use of modeling, worked examples and active, hands-on constructionist approaches in education. Figures like Papert and Sweller played influential roles in these paradigm shifts.

Here is a summary of the key points from the article:

The article discusses George Miller’s seminal work in the 1950s establishing the concept of working memory capacity being limited to 7 ±2 items. Subsequent research has refined this understanding and suggested the limit may be closer to 4 items.
Working memory capacity can be influenced by various factors such as gender, race, socioeconomic status, and cognitive/neurological disorders like schizophrenia.
Strategies like chunking information can help maximize working memory utilization. Retrieval and spacing out practice over time also benefits learning by making retrieval more effortful.
The article discusses pioneers in research on learning strategies like Robert Bjork who demonstrated the “spacing effect” where spaced practice leads to better retention than massed practice. His work supported the idea that forgetting and learning are in a symbiotic relationship.
Contextual interference theory holds that varied or interleaved practice is more effective for learning than blocked practice due to increased difficulty of retrieval and error correction.
Pretesting or retrieval practice before studying helps potentiate future learning by priming memory structures and increasing encoding effectiveness.
The concept of metacognition, or thinking about thinking, emerged from research in the 1960s and is now recognized as important for self-regulated learning. Monitoring one’s own knowledge and comprehension aids performance.
Andrew Bell and Joseph Lancaster developed new monitorial school systems in the early 19th century that allowed one teacher to instruct large numbers of students efficiently. Bell developed his system at a school in Madras, India.
Their monitorial systems used older students as “monitors” to help instruct younger students. This reduced costs significantly compared to traditional one-teacher schools.
Bell and Lancaster promoted their systems enthusiastically in Britain and abroad. Their schools spread widely in the early 19th century as advocates saw them as a affordable way to expand access to basic education.
However, critics argued the monitorial systems were not as efficient or effective as claimed. Students received less individual attention, and teaching quality could be inconsistent. The systems also faced challenges in maintaining discipline among large numbers of students.
By the mid-19th century, the monitorial systems began to decline as critics’ views gained traction. Traditional small primary schools with a single teacher became more common again. However, the earlier expansion of basic schooling laid important foundations for public education systems.

Here is a summary of index.asp:

It discusses the history and origins of mastery learning as a concept that aims for students to fully understand topics before moving on, rather than being assessed on a curve. Key figures discussed include Benjamin Bloom.
It covers research showing benefits of mastery learning, such as Montessori students outperforming others. Studies found long-term benefits from Montessori education that equalized outcomes.
The rise of massive open online courses (MOOCs) is explored, as well as their limitations in reaching narrow audiences. Blended learning models combining online and in-person are discussed as more effective.
Advancements in educational technology are summarized, like early pioneers Khan Academy and adoption of tools like IBM’s Watson platform. Concerns around AI in education are also noted.
The use of online learning to address teacher shortages is covered through a case study of Mississippi. Broader trends in digital learning expansion globally are also mentioned.
Overall it provides context on the evolution of different educational models and technologies as well as debates around their impact and limitations. A wide range of topics are discussed relating to modernizing and scaling educational approaches.

Here is a summary of the sources provided:

The sources discuss two concurrent experiments on massive open online courses (MOOCs) in 2013. The first source, from Inside Higher Ed, discusses how the education technology company Udacity put one of its online courses “on pause” due to setbacks. The second source, from the New York Times, discusses how major online courses were being rethought and reconsidered after facing various setbacks.

The sources provide an example of how massive open online courses were being experimentally implemented in 2013 but were beginning to face challenges that led companies and institutions to re-evaluate and revise their approaches to this new model of online education. The concurrent reporting in these two sources from July and December 2013 help document the early challenges and revisions occurring for MOOCs during their initial widespread experimentation and rollout.

Here is a summary of the sources provided:

Thorndike, E. L. (1913). The Psychology of Learning. This work discusses educational psychology.
Thorndike, E. L. (1942). Human Nature and the Social Order. This book discusses human nature and its relationship to social structure.
Thorndike, E. L. (1901). Notes on Child Study. This work focuses on observations of child development.
Thorndike, E. L. (2013). The Principles of Teaching: Based on Psychology. As the title suggests, this examines principles of teaching grounded in psychology.
Tobias, S. & Duffy, T. M. (2009). Constructivist Instruction: Success or Failure?. This source evaluates the effectiveness of constructivist instructional methods.
Toffler, A. (1994). Future Shock. This book examines the psychological and sociological effects of rapid technological and cultural change.
Tschurenev, J. (2019). Empire, Civil Society, and the Beginnings of Colonial Education in India. As the title implies, this discusses the origins of education under colonial rule in India.
Additional sources examine topics like metaphor, urban education systems, public school reform efforts, embodied cognition, language development, olfactory perception, and contributions to educational psychology as a field.

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Grasp The Science Transforming How We Learn - Sanjay Sarma & Luke Yoquinto