Serendipity35

The notion of talking to oneself, often dismissed as a mere quirky habit or a sign of preoccupation, is, in fact, a powerful, evidence-based cognitive tool essential for learning, problem-solving, and achieving self-regulation. For educators, understanding and deliberately integrating this "verbal thinking"—known in psychological literature as private speech, self-talk, or self-explanation—into pedagogical practice can unlock deeper comprehension and foster truly independent learners. 

The psychological roots of verbal thinking's benefit trace back most prominently to the work of Soviet psychologist Lev Vygotsky. His socio-cultural theory identifies a critical stage in a child's cognitive development where social communication turns inward to become a robust tool for thinking. Vygotsky outlined a three-stage developmental framework for language: beginning with Social Speech in young children, where language is purely external and used for communicating with others; progressing to Private Speech during the preschool years (ages 3-7), where the child begins to speak aloud to themselves, often in a whisper or mumble, utilizing this overt language as a self-guiding tool for planning, regulating, and controlling their own behavior and problem-solving attempts.

For example, a child engaged in a puzzle might audibly walk themselves through the steps: "First, put the red block here, then the blue block goes on top." This transitional phase ultimately leads to Inner Speech (age 7+), which is the fully internalized, silent verbal thought that most adults use for abstract reasoning, reflection, and sophisticated problem-solving. For educators, the key takeaway from Vygotsky’s work is that overt verbal thinking, or private speech, represents the crucial bridge from externally guided learning—where an adult or peer provides the instruction—to true self-regulation and independent, complex thought. By encouraging students to verbalize their process, teachers are helping them build the necessary internal scaffolding for later, silent, and more sophisticated thinking.

Crucially, verbal thinking doesn't just manage behavior; it fundamentally alters how information is encoded and understood by the brain, supporting both memory and comprehension. Research in memory retrieval highlights a phenomenon known as the Production Effect, which demonstrates that reading or generating information aloud significantly improves its memory retention compared to reading it silently. This memory boost occurs because speaking information aloud engages a greater number of sensory channels simultaneously. The learner uses visual input (seeing the text), verbal/motor input (the physical articulation of the words), and auditory input (hearing the words being spoken). This richer, multi-modal encoding creates a more distinctive and robust memory trace in the brain, making the information much easier to recall later. This distinctiveness is vital: when a learner produces a word aloud, it stands out against the background of other silently read words, making the item unique in memory. Therefore, simply having students read key definitions, summaries, or steps aloud in a low-stakes environment is a simple, yet highly effective, way for educators to leverage this proven physiological mechanism to strengthen long-term memory.

Perhaps the most powerful cognitive benefit, particularly for complex material, is the deep processing that occurs through self-explanation. This process is not mere repetition; it is the active, conscious act of trying to explain new information by relating it to what one already knows, making necessary inferences, and proactively clarifying any ambiguities. The first benefit here is powerful metacognitive monitoring: when a learner verbalizes a concept, the very act of articulation immediately exposes areas of confusion or "knowledge gaps." If a student struggles to explain a step in a math proof or a scientific concept, the flaw in their understanding is instantly revealed, prompting them to go back and refine their knowledge. This is a critical act of metacognition—the vital process of thinking about one's own thinking. Secondly, self-explanation drives coherence building. Verbalizing forces the student to translate disparate, often fragmented, pieces of information into a coherent, logical structure. They are not just recalling isolated facts but actively constructing a unified mental model of how the concepts interact. This principle is famously embodied by the Feynman Technique—explaining a concept simply as if teaching it to a novice—which serves as a form of high-level, deliberate verbal thinking that ruthlessly exposes the limits of a learner's comprehension.

The idea that talking to yourself out loud is not only "okay" but also an excellent learning technique is satisfying, but as I dug into this research, I recognized things from my college and grad school education courses. Other than the idea that it's not abnormal behavior to talk to yourself, this research is not completely new. I used several of these pedagogies in my teaching.

The challenge for educators, then, is to move verbal thinking from an accidental occurrence to a deliberate, scaffolded learning strategy within the classroom environment. One highly effective technique is the Think-Aloud Strategy, which focuses on teacher modeling. This strategy is used to make the invisible thought process of an expert visible and accessible to students, thereby explicitly teaching them how to engage in effective self-talk. To implement this, the teacher must first explicitly state the goal: "I’m going to show you how a skilled reader or problem-solver thinks by saying my thoughts out loud." Then, as the teacher reads a complex passage, works through a mathematical equation, or analyzes a primary source, they must stop frequently to verbalize their internal dialogue. This might involve using strategic planning language like, "I'm thinking I should use the quadratic formula here because the equation is set to zero," or demonstrating monitoring and correction by saying, "That word, 'ephemeral,' sounds like it means brief, so I’m going to pause and look that up to make sure I understand the context," or making connections: "The author just described the main character as restless. That connects to the idea I read earlier about his lack of a stable job. I wonder if this will lead to him leaving town." Once modeled, the teacher must transition students to practicing the strategy, perhaps through paired activities known as Reciprocal Think-Alouds, before expecting independent use.

A second practical technique is the Self-Explanation Prompt. This method strategically inserts verbalization breaks into a learning task to force metacognitive reflection and is particularly useful in technical subjects. Implementation begins by identifying key moments in a text, problem set, or lab procedure where a deeper understanding is absolutely necessary before the student can proceed. At these pause points, the teacher provides students with specific open-ended questions they must answer aloud to themselves or in a brief reflection journal. Prompts should be targeted to specific cognitive functions, such as focusing on rationale ("Why did I choose this variable to isolate?"), demanding synthesis ("What is the main idea of this section in my own words?"), or explicitly asking for a connection ("How does this new concept relate to what we learned last week?"). For maximum impact, teachers should then encourage a "Think-Pair-Share" approach where students must first explain their logic to a partner, which solidifies the idea and provides practice in articulation before the whole class moves on.

Finally, the "Teach It Back" Method is a form of high-stakes verbal thinking rooted in the pedagogical principle that to teach a concept is to truly master it. In this strategy, a student is assigned the role of briefly "teaching" a key concept, a section of the reading, or a part of the homework to a small group, to the class, or even to an imaginary audience. The critical instruction given to the student is to explain the topic as simply as possible, perhaps using an analogy, metaphor, or non-technical language if appropriate. The student must translate complex, academic language into straightforward, accessible terms, which serves as the ultimate test of their own comprehension. The teacher should provide specific feedback not only on the accuracy of the content but also on the clarity and logical structure of the explanation, reinforcing the importance of effective verbal articulation as a measure of understanding. By integrating these verbal thinking strategies—modeling, prompting, and teaching back—educators are not just improving a single study skill; they are building the core components of the resilient and self-regulated learner, equipping students with the tools for lifelong, independent cognitive growth.

SOURCES
Vygotsky, L. S. (1978). Mind in society: The development of higher psychological processes. Harvard University Press. (This source is foundational for the concepts of Private Speech and its role in Self-Regulation.)

MacLeod, C. M. (2011). The production effect: Better memory as a consequence of saying aloud during study. Applied Cognitive Psychology, 25(2), 195–204. (This research provides the physiological basis for the Production Effect and memory benefits.)

Chi, M. T. H. (2013). Self-explanation: The effects of talking aloud or writing on learning. Topics in Cognitive Science, 5(1), 1–4. (This source details the mechanism and benefits of Self-Explanation for deep comprehension.)

Berk, L. E. (1992). The role of private speech in the development of mental processes. Psychological Review, 99(4), 779–795. (This provides contemporary developmental research supporting and elaborating on Vygotsky’s observations of private speech.)

Julia for data visualization

A few friends and former students who are working as programmers have told me recently that I should write about Julia. Julia is not a person but a language. One person called this "the new Python" while another said it was the "Python killer."

Python is the so-far-unchallenged leader of AI programming languages and is used by almost 90% of data scientists, but it is probably not the future of machine learning. Programming languages, like all languages, fall out of favor and sometimes die. There is not much demand for the COBOL, FORTRAN and BASIC that was being taught when I was an undergrad.

Julia is faster than Python because it is designed to quickly implement the math concepts like linear algebra and matrix representations. It is an open source project with more than a thousand contributors and is available under the MIT license with the source code available on GitHub.

I have learned that you don’t need to know programming to do some AI. There are no-code AI tools like Obviously.AI, but programming is necessary for some devlopment.

The home site for Julia is julialang.org which has a lot of information.

An article I read at pub.towardsai.net led me to investiagte a free online course on computational thinking at MIT that is taught using Julia.

This is not a course on programming with Julia but almost all data and AI courses are taught in Python (perhaps a few using R and other languages) so this is unique as a course. The course itself uses as its topic the spread of COVID-19.and includes topics on analyzing COVID-19 data, modeling exponential growth, probability, random walk models, characterizing variability, optimization and fitting to data. Through this topic the course teaches how to understand and model exponential functions. That has much broader application into financial markets, compound interest, population growth, inflation, Moore’s Law, etc.

As that article notes, right now searching jobs on LinkedIn for “Python Developer” will turn up about 23,000 results, so there is a market for that skill set now. Searching “Julia Developer” will return few results now. You can find a LinkedIn group for Julia developers, called “The Julia Language,” so interest is there and the jobs are beginning to appear. A Julia specialits now has a big advantage in that there are fewer people with that skillset for the jobs that are appearing. The predictions (always a dangerous thing) are that Julia has a big role to play in the data & AI industry.

Writing online, I am kind of guessing about who are my readers. I know where they come from geographically and I know how they find me in a search and what articles they read and other analytics. I don't know what their reading level might be and every writing course will tell you that you "need to know your audience."

I make some assumptions that readers of a blog about technology and learning are mostly educators and so I further assume that they have a high school and above reading level. But how do you determine the reading level of what you are writing?

If you write in Microsoft Word, it is simple to use two major readability tests that are built-in: the Flesch Reading Ease and Flesch-Kincaid Grade Level.

For the Flesch Reading Ease and Flesch-Kincaid Grade Level statistics to come be part of the “Spelling & Grammar” review of your content, you will need to enable those statistics. To do this select “File” then “Options” next go to the “Proofing” tab and check the box that says “Show readability statistics.”

Flesch-Kincaid scores are readability tests designed to show how easy or difficult a text is to read. This score is given in two different ways. First is the “Flesch Reading Ease” number which ranges from 0 to 100. With a score of 90-100, your writing could be understood by an average 11-year old and a score of 60-70 could be understood by average 13 to 15-year olds. A score of zero to 30 means your writing could be understood by a university graduate.  A bit counterintuitively, the higher the score the easier the writing is to read and comprehend.

For comparison, Time magazine averages at a score of 52 and the Harvard Law Review falls somewhere in the low 30s.

The Flesch-Kincaid Grade Level applies a reading grade level to your writing. I learned many years ago that most general news articles in The New York Times have a tenth-grade reading level. Romance novels have about a fifth-grade reading level. 

I ran a recent article here through the test and got the results shown below. The Reading Ease score is about 55 and a Grade Level a tenth-grader in the middle of sophomore year. 

You might think that score seems to be low for a post I am aiming at educators, but many sources will recommend that ease of reading in order to boost your numbers and even in your emails and communications. I know that some researchers have said that your response rate varies by reading level. The article linked here claims that emails written at a 3rd-grade reading level were optimal with a 36% boost over emails written at a college reading level and a 17% higher response rate than emails written even at a high school reading level.

When Microsoft Outlook and Word finish checking the spelling and grammar, you can choose to display information about the reading level of the document using the Flesch Reading Ease test and the Flesch-Kincaid Grade Level test. You can also set your proofreading settings to flag things like jargon, which is often what pushes ease aside and pushes readers to leave.

This may sound like advice to "dumb down" your writing. I don't think it is that. The English major part of me is reminded of Ernest Hemingway's journalistic simplicity. You can still get across deep ideas in simple language. I like the Einstein quote “Everything should be made as simple as possible,
but not simpler.” 

“Serendipity is looking in a haystack for a needle and discovering a farmer’s daughter.” - Julius Comroe

 

Umberto Eco's book, Serendipities: Language and Lunacy, is not a book about education or technology. It is about some riddles of history and the "linguistics of the lunatic." I am an Umberto Eco reader and first noticed him, as many people did, with his novel The Name of the Rose. 

That novel takes place in 1327 in an Italian Franciscan abbey that is suspected of heresy. Brother William of Baskerville arrives to investigate and the story is a medieval mystery with a series of seven murders. Eco is

and he mixes in Aristotle, Thomas Aquinas and Roger Bacon. There are secret symbols and coded manuscripts in a higher level version of the Dan Brown novel formula.

I was partially attracted to Serendipities because of the title, but it's not an easy to read novel, but rather a non-fiction study.

Eco looks at mistakes that have shaped human history. For example, Christopher Columbus assumed that the world was much smaller than it is, land so he assumed he could find a quick route to the East via the West. He was wrong, but he accidentally "discovered" America.

Cults such as the Rosicrucians and Knights Templar seem to have resulted from a mysterious starting place that was a hoax. That kind of start made both groups ripe for conspiracy theories based on religious, ethnic, and racial prejudices. 

Eco posits that serendipities and mistaken ideas can have fortuitous results. 

On The Writer's Almanac, there was a nice short history of serendipity, parts of which I have also written about here. The word “serendipity” was first coined in 1754, and is now defined by Merriam-Webster as “the faculty or phenomenon of finding valuable or agreeable things not sought for.” 

“Serendipity” was first used by parliament member and writer Horace Walpole in a letter that he wrote to an English friend who was spending time in Italy. In the letter to his friend written on this day in 1754, Walpole wrote that he came up with the word after a fairy tale he once read, called “The Three Princes of Serendip,” explaining, “as their Highnesses travelled, they were always making discoveries, by accidents and sagacity, of things which they were not in quest of.” The three princes of Serendip hail from modern-day Sri Lanka. “Serendip” is the Persian word for the island nation off the southern tip of India, Sri Lanka.

The invention of many wonderful things have been attributed to “serendipity,” including Kellogg’s Corn Flakes, Charles Goodyear’s vulcanization of rubber, inkjet printers, Silly Putty, the Slinky, and chocolate chip cookies.

Alexander Fleming discovered penicillin after he left for vacation without disinfecting some of his petri dishes filled with bacteria cultures; when he got back to his lab, he found that the penicillium mold had killed the bacteria.

Viagra had been developed to treat hypertension and angina pectoris; it didn’t do such a good job at these things, researchers found during the first phase of clinical trials, but it was good for something else.

The principles of radioactivity, X-rays, and infrared radiation were all found when researchers were looking for something else.

A U.K. translation company put "serendipity" on a list of the English language’s ten most difficult words to translate along with plenipotentiary, gobbledegook, poppycock, whimsy, spam, and kitsch.

In Eco's intellectual history of serendipities, he includes dead ends and mistakes that were not fortuitous. Leibniz believed that the I Ching illustrated the principles of calculus. Marco Polo identified a rhinoceros as the mythical unicorn. 

Eco then turns to how language tried to "heal the wound of Babel." But throughout the Middle Ages and the Renaissance, various languages were held up as the first language that God gave to Adam. Greek, Hebrew, Chinese, and Egyptian were alternately seen as the starting place for language.

These essays by Umberto Eco are prefaced with his conclusion that serendipity is the positive outcome of some ill-conceived idea.