How Verbal Thinking Elevates Learning

student working on mathThe 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.)

Kids and AI

Artificial Intelligence is so ubiquitous -  both in visible ways and hidden away in how we use technology - that MIT Review published a piece on six things you should tell kids about artificial intelligence. I taught in a middle school and I know AI has entered that curriculum level, but even younger kids need to be taught and prepared for using AI.

One thing they suggest is the idea that AI is not your friend, but that doesn't mean it's your enemy. Chatbots are likely one of the first AI forms a younger child might encounter. That may be via a device such as Alexa. Yes, they "chat" in a somewhat friendly, conversational tone but younger kids need to be reminded that they are machines. That means not giving the machine any sensitive personal information which will be stored and used in a very large database. We know of cases where bots and apps have become "friends" for people in a serious way - and I'm not talking only about kids.

They also suggest conversations for young kids about how AI models are not replacements for search engines or a way to write your schoolwork. Following that idea, let it be known that teachers might accuse you of using an AI - possibly even when you haven’t used it.

Chatbots and recommender systems are designed to get you hooked and keep yu using them and maybe paying for a premium version. They might show you incorrect information. AI makes mistakes.

Of course, this is coming from Massachusetts Institute of Technology so their final suggestion is not to miss out on what AI is actually good at doing. "Students who find themselves struggling to understand a tricky topic could ask ChatGPT to break it down for them step by step, or to rephrase it as a rap, or to take on the persona of an expert biology teacher to allow them to test their own knowledge. It’s also exceptionally good at quickly drawing up detailed tables to compare the relative pros and cons of certain colleges, for example, which would otherwise take hours to research and compile."

The main is that parents and teachers need to have conversations about AI with the young people in their lives, and probably educate themselves about it too.

40 Years of Microsoft Windows

windows versions logoes

Recently, my laptop crashed, and I had to return to an old one that had been sitting on a shelf for a few years. It had Windows 8 from back in 2012. No updates available, and lots of websites and tools did not work. The laptop that crashed has Windows 10 and that will fade away from support in October 2025.

It got me thinking about the now 50-year history of Microsoft.

The company was at the top early on, then went through some tough years and is again near the top. It has been the first or second most valuable business on Earth for the better part of five years.

Microsoft is betting on AI to carry it into the next generation of computing. However, Microsoft's most enduring legacies may be the marks it left on society long ago via Windows. It's not a point of pride for the company or many of its users that much of our world still relies on aged, sometimes obsolete Windows software and computers. This ghost software is still being used, though it is somewhat crippled.

Here are all the versions of Windows so far:
Windows 1.0: November 20, 1985.
Windows 2.0: December 9, 1987.
Windows 3.0: May 22, 1990.
Windows 95: August 24, 1995.
Windows 98: June 25, 1998.
Windows ME (Millennium Edition): September 14, 2000.
Windows 2000: February 17, 2000.
Windows XP: October 25, 2001.
Windows Vista: January 30, 2007.
Windows 7: July 22, 2009 (released to manufacturing), October 22, 2009 (generally available).
Windows 8: October 26, 2012.
Windows 8.1: February 13, 2013.
What happened to Windows 9? (see below)
Windows 10: July 29, 2015.
Windows 11: October 5, 2021.

According to an article on bbc.com, many people and services still use outdated Windows versions.

"Many ATMs still operate on legacy Windows systems, including Windows XP and even Windows NT," which launched in 1993, says Elvis Montiero, an ATM field technician based in Newark, New Jersey. "The challenge with upgrading these machines lies in the high costs associated with hardware compatibility, regulatory compliance, and the need to rewrite proprietary ATM software."

What happened to Windows 9? 

Opening the Classroom Door Into 2025

Whenever I post predictions of what might be coming in edtech for the new year, I find myself writing about things that were present in the past year or even for several years. In other words, it takes more than a year for any trend or new thing to catch hold. And some things are predicted to be big for many years in a row but just don't seem to emerge. (item 5 in my list below is a good example.) 

I wrote earlier about the general trends for 2024 edtech, and honestly, it all seemed old already and one-sided..

So, what educational technology might we expect to be significant in 2025? I looked online for trend reports and the topics seem very familiar.

Here is the list I compiled from other writers' lists. How much of this list is familiar to you?

  1. artificial intelligence
  2. AI-driven personalized learning
  3. cloud computing
  4. immersive experiences with virtual and augmented reality (VR/AR)
  5. gamification
  6. hybrid learning models
  7. data analytics
  8. adaptive learning systems that cater to individual student needs

I find nothing new in this list; some have been on trend lists for years.

Is nothing new on the horizon in edtech?