Semantic learning
This glossary entry is used to explain "I would never send my kids to school" (2017-2024) by Piotr Wozniak
Definition
Semantic learning is learning based on solid comprehension. Semantic learning builds knowledge that is well connected with the semantic network of prior knowledge. By definition, semantic learning leads to coherent memories. Such memories are usually characterized by high applicability and high stability. The effect of coherence on applicability is context dependent (e.g. coherent knowledge of history may not be applicable in the context of problem solving in physics).
Explanation
The easiness with which we remember is strongly related to how a piece knowledge is connected with the rest of the body of knowledge. To reference the system of semanatic connections, I use the term framework. In learning we will primarily encounter three classes of situations: (1) semantic learning in which the semantic framework is based on the meaning of knowledge, (2) mnemonic learning is memorization based on mnemonic frameworks erected artificially to support memory, and (3) asemantic learning in which the framework of connections is weak or non-existent (colloquially we would call that form of learning cramming).
Examples
In asemantic learning, we may be requested to remember a number: 647 983 894. This is a very challenging job for an unprepared mind. The disassociation of the number from prior knowledge is magnified by the fact that we are not even providing a meaningful anchor. An association of the number with someone's phone might provide such an anchor. For example, What is John's phone number? 647 983 894
In mnemonic learning, we may use a peg list or a memory palace to (1) separate the number into chunks, e.g. 6 47 98 38 94, (2) convert the chunks into a pictorial story, and (3) memorize the story that can easily be decoded back as a number. It is not likely a phone number can be remembered without an assist from mnemonic techniques. It may occasionally be remembered via frequent use (e.g. one's own number), or via natural mnemonic anchors that develop over decades of learning (esp. if memorizing numbers is frequently needed).
In semantic learning, we associate new knowledge with prior knowledge. If the associations are simple, the knowledge is particularly easy to remember. For example: What is the name of our galaxy? Milky way. All semantic learning is mnemonic in nature as the semantic framework can make the best kind of a mnemonic framework.
Evolution of learning in development
Little children can only learn using the natural semantic way. Things that are meaningful, simple, and useful will be stored in memory. Those early connections may even survive the period of childhood amnesia (e.g. recognition of a dog may precede the earliest recallable episode in life, e.g. an encounter with a vicious dog).
With the gradual buildup of knowledge, prior knowledge may provide rudimentary mnemonic framework that will help remember things with the use of prior knowledge without the need for a specific semantic connection. For example, we may remember that the Internet was born in 1969 because it was the same year that we went to the moon. The two events may seem unrelated, but the mind can automatically form the association.
One of the great harms of schooling is that tests and deadlines promote cramming. Children are coerced to employ asemantic learning as an emergency tool. Overtime, incessant conditioning results in weakening the habit of semantic learning. A schooled adult may approach learning in the old bad-school way, i.e. cram fast and voluminously. This has a disastrous impact on the quality of learning, comprehension, and the ability to reason about knowledge. This way, schooling undermines human intelligence!
Adult empathy
Adults struggle to empathize with young brains. It is no coincidence that early learning gravitates towards asemantic content. Letters of the alphabet, days of the week, names of months, multiplication table, etc. These are all asemantic concepts that get quickly filtered as hardest. This gives them the aura of primacy and primitiveness. They may be labelled as basic toolset. In reality, what a young brain needs is a rich semantic framework that establishes the need for, and the meaning of those primitive concepts. Only then learning may become semantic and produce desired results. A vast majority of adults, including good teachers, do not understand the need for semantic learning. This is the outcome of the conditioning by the Prussian system.
It is very easy for an adult to get a glimpse into a child's brain. Imagine a child who is to learn the names of the days of the week. This child has no scheduled events such us the Sunday mass, or Saturday soccer. For this child, there is less difference between Tuesday and Thursday than there is a difference between Ganymede and Calisto. To learn the names of the days is like attaching labels to vacuum. Entirely futile and asemantic. The adult may reason "these are vital terms the child will need in her life".
To remedy the deficit in educational empathy, the adult can imagine the year split into 36 ten-day periods with each period assigned a specific name: Primedec, Secdec, Winterdec, Febdec, etc. How about learning that sequence just for a test? Only 36 names (just to compensate for adult metacognitive superiority). All adults, with no exceptions, rejects such a proposition. This is exactly how a child feels. A bunch of names to learn with no content and no purpose! This is classic asemantic learning.
Problem with SuperMemo
This glossary entry is used to explain "I would never send my kids to school" (2017-2024) by Piotr Wozniak
Figure
Asemantic learning is nearly always a result of coercion. The brain naturally rejects meaningless knowledge. Regular coercion at school is a serious societal problem:
Figure: This is how school destroys the love of learning. Learn drive is the set of passions and interests that a child would like to pursue. School drive is the set of rewards and penalties set up by the school system. Learn drive leads to simple, mnemonic, coherent, stable and applicable memories due to the fact that the quality of knowledge determines the degree of reward in the learn drive system. School drive leads to complex, short-term memories vulnerable to interference due to the fact that schools serialize knowledge by curriculum (not by the neural mechanism of the learn drive). Competitive inhibition between the Learn drive and the School drive circuits will lead to the weakening of neural connections. Strong School drive will weaken the learn drive, destroy the passion for learning, and lead to learned helplessness. Powerful Learn drive will lead to rebellion that will protect intrinsic passions, but possibly will also lead to problems at school. Storing new knowledge under the influence of Learn drive is highly rewarding and carries no penalty (by definition of the learn drive). This will make the learn drive thrive leading to success in learning (and at school). In contrast, poor quality of knowledge induced by the pressures of the School drive will produce a weaker reward signal, and possibly a strong incoherence penalty. The penalty will feed back to produce reactance against the school drive, which will in turn require further coercive correction from the school system, which will in turn reduce the quality of knowledge further. Those feedback loops may lead to the dominance of one of the forces: the learn drive or the school drive. Thriving learn drive increases rebellion that increases defenses against the school drive. Similarly, increased penalization at school increases learned helplessness that weakens the learn drive and results in submission to the system. Sadly, in most cases, the control system settles in the middle of those two extremes (see: the old soup problem). Most children hate school, lose their love of learning, and still submit to the enslavement. Their best chance for recovery is the freedom of college, or better yet, the freedom of adulthood. See: Competitive feedback loops in binary decision making at neuronal level
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