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Metacognition Demystified


At AJ Tutoring, a primary goal of our academic tutoring as well as our study skills process is to teach students how to learn in the way that best suits their unique minds. In order to accomplish this, our professional tutors aim to move beyond homework help and the specifics of today’s lesson to develop our students’ academic skills on a deeper level.

Albert Einstein once said, “The development of general ability for independent thinking and judgement should always be placed foremost, not the acquisition of special knowledge.” But what is the general ability for independent thinking and judgement, and how would it be placed “foremost”? This post aims to demystify metacognition and briefly discuss the development of metacognitive skills, which are crucial for becoming a more effective thinker and learner at any age.

What does “meta” mean? Meta is self-referential; it indicates a higher level of abstraction.


  • Metadata (data about data; in computer science, categorical information about stored data)
  • Metaphysics (concerns the nature of nature)
  • Metamorphoses (transformation of an established growth pattern; a morphing of morphing)

So, it follows that meta learning is learning about learning. Metacognition is cognition of cognition. In general, a discussion of metacognition and metacognitive skills involves how to approach learning in an optimal way. Where do we see meta-learning knowledge applied today? What are some of the best examples of high-quality learning?

The most prominent example is with developments in artificial intelligence. For example, you can read here about Alpha Zero, a chess program created by DeepMind. Unlike other chess programs, Alpha Zero teaches itself chess, and with remarkable success: within 24 hours of initiation Alpha Zero was able to best one of the most powerful existing chess programs. Because it teaches itself, Alpha Zero is an example of strong metacognition.

Today, Google/DeepMind and AI is emphasizing neural networks, which models learning in a way more closely resembles the human process of learning rather than a process emphasizing rote memorization and raw power that we traditionally associate with computers’ learning.

If we want to learn better, we may want to begin with analyzing learning. But is it even possible to breakdown learning? Yes, it is! Please find below a breakdown of the steps required to approach a problem or concept with an eye to improving metacognitive processes.


  1. Detachment: The goal is no longer to solve the problem directly, but to understand the nature of the problem and situate it in a greater context.
  2. Evaluation: Identify structural aspects and components of the problem.
  3. Creation: Create a new line of inquiry via simplifying one or more aspects of the problem. This may involve removing components in order to reduce complexity.
  4. Application: Apply problem-solving skills to obtain a solution to the new problem created, or question asked.
  5. Analysis: Analyze your results and re-evaluate the original problem. Do these results assist with the original problem?


  1. Detachment: The goal is no longer to solve the problem directly, but to understand the nature of the problem and situate it in a greater context.
  2. Gathering: List true statements and any info that may be relevant to the problem.
  3. Production: Use the statements you have to produce new factual information relevant to the problem. Use “if … then” statements in order to deductively reason.
  4. Analysis: Re-evaluate the problem with the new plethora of information you have. Is it easier to solve? If not, continue to repeat this technique or switch to using the “Simplification” technique.

Essentially, this is a concrete methodical skill-based approach to training your inductive and deductive reasoning skills. Train, train, train—the key is having lots of iterations and lots of failures to learn from. And this produces high-quality learning experiences.

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