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Mastery-Based Learning

At Azua, Mastery-based Learning is the pedagogy for the Core Curriculum. Make sure to first read about Our Pedagogy

One of the core values that distinguishes Azua from other coding schools is our focus on Mastery-based Learning (MBL). If the foundation of becoming a skilled and confident Software Engineer is a deep understanding of first principles, then the path to achieving that understanding lies in Mastery-based Learning.

This is the core philosophy at Azua, guiding every aspect of our curriculum and processes.

Our approach is driven by this principle, including our decision to charge a sustainable fee (more on that later).

But what exactly is Mastery-based Learning? In this document, we aim to explain what it is, explore its pros and cons, and discuss how it shapes the Azua experience.

Whether you’re new to MBL or have never seen it in practice, this document will provide valuable insights into what it entails and how we implement it at Azua.

If you’re already familiar with MBL, this document will reinforce your understanding and show how it influences everything from our curriculum and structure to our pricing model.

Before diving into the details of MBL and its alternatives, let’s first discuss the motivation and context behind this article.

Background

We often receive questions from prospective students who are considering starting their learning journey with us. Some of the most common questions include:

  • How do I get a job?
  • I have an idea for an app. How do I build it?
  • How do I become a professional developer?
  • How do I start a long career in software development?

What’s interesting is that the first two questions are asked far more frequently than the last two.

By far, “How do I get a job?” is the most popular question. However, the latter two questions are rarely asked. So, what sets these two sets of questions apart?

The answer lies in the difference between performance goals and mastery goals.

The first two questions are focused on performance goals—objectives that can be measured with clear, defined outcomes.

On the other hand, the latter two are about mastery goals, which emphasize the learning process and continuous improvement.

This distinction stems from what psychologists refer to as “Goal Orientation.”

Performance goals are prevalent in both education and the workforce as a way to motivate people to work harder.

Popular goal-setting frameworks like SMART goals or personal improvement plans are also performance-focused.

As a result, it’s natural for prospective students to ask performance-oriented questions, such as “How do I get a job?”

If we dig deeper and ask them to clarify, they might say something like:

  • “I want a program that will guarantee me a job at the end.”
  • (And for the more ambitious), “I want a program where employers are eager to hire its graduates.”

These responses reinforce the focus on performance-oriented goals. The phrasing suggests a sense of finality, with concepts like “graduation,” “a finishing line,” or “at the end.”

While these questions are perfectly understandable and common, many educational institutions design their curricula around meeting these performance-oriented expectations.

Though there’s nothing inherently wrong with these types of questions, focusing solely on performance can limit long-term growth.

At Azua, we encourage students to embrace mastery goals for sustained development and career success in software engineering.

Current Education Models

The prevailing education model that caters to performance goals is the factory-based education model.

This model is designed to satisfy performance-oriented goals by having a defined endpoint. When the objective is to achieve a specific, measurable result, such as passing a test or obtaining a certificate, the educational program is structured around that goal, with a clear end in sight.

This approach is not unique to the coding industry—it’s how most of us have experienced education, from middle school to high school to college.

We refer to it as the “factory model” because, much like a production line, it moves students along a conveyor belt from one topic to the next, often without ensuring they’ve fully grasped the material.

Students can pass classes with a C grade, even if they haven’t truly mastered the concepts, which can be especially problematic in subjects where knowledge is cumulative, such as math or science.

In the factory model, the primary concern is time rather than comprehension.

Students are expected to learn within a fixed timeframe, and once that time is up, they move on—regardless of their understanding. But the issue goes beyond time constraints.

Since mastery is not the goal, curriculum designers in factory-based education systems often focus on producing a bell curve of student performance.

Instead of aiming to help all students reach a high level of mastery, the success of the curriculum is often judged by how well student outcomes fit into this bell curve distribution.

By design, this approach ensures that only a small percentage of students reach an A+ level, while the majority do not achieve mastery.

Now, let’s examine how this model plays out in the learn-to-code industry. Many coding bootcamps adopt the factory model, promising quick results but often moving students along without guaranteeing a deep understanding of the material.

Below is a selection of blurbs from various coding bootcamp websites that illustrate this focus on speed and results over true comprehension.

Learning Framework for Our Pedagogy

What do all these pithy promises have in common? They reflect performance-oriented guarantees that are rooted in the factory model of education.

While it’s easy to critique these promises—after all, who genuinely believes they can become a professional at anything in just 12 weeks?—the reality is that these training programs feel compelled to define an arbitrary duration and endpoint because students demand performance-based results.

However, they conveniently omit a critical issue inherent in any factory model: variable comprehension among graduates.

Prospective students intuitively understand this. While these catchy promises aim to satisfy performance-oriented expectations, they also breed skepticism and doubts.

No one wants to graduate only to discover they’re on the lower end of the comprehension scale. This skepticism is widespread because, from our own educational experiences—whether in middle school, high school, or university—we know not everyone ends up with an A.

This implies that students don’t all emerge with the same level of understanding. As a result, prospective students often ask follow-up questions to address their growing doubts:

  • What’s your graduation rate?
  • What’s your hiring rate?
  • How can you guarantee I’ll get a job?
  • What’s the teaching format like?
  • Who are your instructors?
  • Where do your training materials come from?
  • Can someone help me with installing/configuring/setting up Vim, Ubuntu, or a Virtual Machine?
  • What if I’m too old/young, didn’t attend university, or went to too much university?

These questions reveal a great deal of anxiety and uncertainty, which stem from our previous experiences with the factory-based education model.

We know, from both personal and observed experiences, that this system doesn’t work for everyone.

Prospective students want to assess their chances of being on the successful side of the bell curve, especially since not everyone will become a top performer, yet the cost of admission is the same for all, and everyone spends the same amount of time in the program.

Essentially, these questions are asking, “What are my chances of ending up at the top end of that curve? Because I’ve seen this system fail—whether it’s friends, family, or myself—time and time again.”

To address these difficult questions, many training institutions divide their factory-based model into two key variants:

  • Variable Input → Variable Output
  • Quality Input → Quality Output

Variable Input Model (Public university)

In this variant of the factory model, the input (students) is highly variable. This variability can arise from many factors: differences in background, availability of time, or access to resources.

For instance, it’s common for some students to work full-time while attending school, while others may have the luxury of focusing entirely on their studies, benefiting from one-on-one tutoring and personalized support.

Yet, despite these differences, both groups are expected to master the course material within the same timeframe—the test is on Friday for everyone, no exceptions.

This variable input is then processed through a fixed-duration educational program—whether it’s 12 weeks, 24 weeks, or 6 months.

We refer to this as the “public university” model because the input isn’t selectively filtered; admissions are generally open to all.

The result of this system is predictable: when the input is variable but the time to learn is fixed, the output (graduates) will inevitably have variable comprehension. It cannot be otherwise.

There will undoubtedly be star graduates from this model, but employers must sift through the pool to find them.

By design, only a small portion of students will achieve an A level of mastery, while others may not grasp the material as fully.

This variability in outcomes makes it impossible to guarantee jobs for all graduates in such a system.

In fact, this is the type of education most of us have experienced throughout our lives.

While some variation in output is unavoidable in a system like this, there is another variant of the factory model that aims to minimize this variation. We’ll explore that next.

Quality Input Model (Ivy League)

This variant of the factory model significantly reduces output variation.

As shown in the diagram, this is achieved through a strict admissions process that filters out less-prepared applicants, leaving only top-tier candidates as input.

If you’re working full-time, you’re out; if you don’t have a computer science degree, you’re out.

This approach mirrors the model used by elite universities, hence we call it the “Ivy League” model.

However, while the admissions filter is different, the educational process remains the same—it’s still factory-based, meaning there’s a fixed duration for mastering the material.

The key difference is that by admitting only the best-prepared students, the system produces a more consistent, high-quality output.

Employers favor this model because it simplifies the hiring process—graduates from Ivy League schools are presumed to be highly capable based on the selective admissions process alone.

In many cases, just being accepted into one of these institutions is enough to impress employers, even if the student hasn’t yet graduated.

Taking Stock

Here’s what we’ve established so far:

  1. Prospective students tend to make performance-oriented demands and ask performance-oriented questions.
  2. Training institutions respond to these demands by creating factory-based curricula with two main variants:
    • Variable Input → Variable Output (public university model)
    • Quality Input → Quality Output (Ivy League model)

Each of these models has its own issues. The public university model struggles with variable output, while the Ivy League model requires strict admissions to ensure a consistent level of quality.

What If…?

These two factory-based models dominate education today, but they both have inherent limitations.

What if there were another way? What if we could create an education system that allows for variable input but still guarantees quality output?

Could we design a curriculum that ensures mastery for every student, regardless of their starting point?

This opens the door to exploring a new model of education—one that emphasizes mastery over time, allowing every student to reach the same high level of competence, no matter their background or resources.

If the answer were “no,” this document would end here—but it’s not. The answer is an emphatic yes, it’s possible.

For such a system to work, however, it must replace the notion of “X weeks of learning” or any fixed time duration with a central focus on mastery and comprehension.

Let’s emphasize this once more: the core of Mastery-Based Learning (MBL) is the replacement of time with mastery.

The factory-based education model revolves around a time-based conveyor belt that moves students through topics at a fixed pace.

In contrast, MBL has no such conveyor belt. Progress is not based on time, but on the student’s ability to pass a test of mastery before moving on to the next topic.

This is the essence of MBL: a pedagogy that transforms variable input into quality output.

Given a group of motivated, committed learners from diverse backgrounds, an MBL system is capable of producing graduates who all achieve mastery, regardless of their starting point or circumstances.

In this model, non-stars have the potential to become stars.

So, to clarify, the answer to the question posed earlier is a resounding “YES!”—because this isn’t just a theoretical idea.

At Azua, we’re not merely proposing a concept; we’re sharing our lived experience of implementing MBL as the foundation of our teaching methodology for several years.

In the following sections, we’ll delve into our experience with MBL, discussing its pros and cons and, most importantly, what it takes to succeed in an MBL system.

One major challenge for students in an MBL environment is the mindset shift it requires. Many students struggle with MBL not because of the material itself, but because they come from a factory-based educational background and are focused on performance-oriented goals.

For students to thrive in an MBL environment, these performance-driven goals and the factory-based mindset must be abandoned.

Only then can they fully embrace and succeed within a Mastery-Based Learning system.

Mastery-Based Learning 

At its essence, Mastery-Based Learning (MBL) is founded on two fundamental principles:

  1. Eliminate all time-based measurements from your learning journey.
  2. Advance to a new topic only after mastering the current one.

Let’s explore these principles in greater depth using an example of a skills/knowledge acquisition tree.

This tree diagram illustrates the interconnectedness of various concepts and techniques required to achieve expertise in any skill—be it piano, cooking, tennis, programming, or more.

 

Skills/Knowledge Acquisition Tree

  • Foundational Skills

    • Basic Concepts (e.g., notes for piano, knife skills for cooking, basic rules for tennis, syntax for programming)
      • Mastery of these foundational elements is crucial before progressing.

  • Intermediate Skills

    • Technique Development (e.g., scales and chords for piano, sautéing techniques for cooking, serving strategies for tennis, data structures for programming)
      • Only after achieving mastery in foundational skills should students delve into these intermediate techniques.

  • Advanced Skills

    • Complex Applications (e.g., playing a full piece of music for piano, creating a gourmet dish for cooking, mastering match play for tennis, building a full-stack application for programming)
      • Mastery of intermediate skills paves the way for tackling complex applications.

Analysis of Principles

  1. Removing Time-Based Measurements:

    • In a traditional educational system, students are often pushed to move through topics based on a predetermined timeline. This can lead to gaps in understanding and a superficial grasp of the material.
    • MBL abolishes this constraint, allowing learners to spend as much time as needed to achieve true mastery before progressing. This encourages deeper learning and retention.

  2. Advancing Only After Mastery:

    • The second principle ensures that learners have a solid foundation before taking on more complex topics. This progression ensures that each concept builds upon the previous one, creating a cohesive understanding.
    • In our example, if a student hasn’t mastered the foundational skills, they won’t be able to effectively develop intermediate or advanced techniques.

By following these two principles, MBL creates an educational experience that is personalized and adaptive, allowing each learner to reach their full potential without the limitations imposed by fixed timeframes.

This approach not only enhances understanding but also fosters a sense of achievement and confidence in the learner.

The foundational concepts at the base of the skills/knowledge acquisition tree—labeled A, B, C, D, and E—are referred to as atomic concepts.

They are considered “atomic” because they exist independently and cannot be broken down into simpler concepts.

The concepts situated on higher levels of the tree are known as compound concepts or integrated concepts.

This structure illustrates the dependencies between different concepts. For instance, mastering AB requires that you first attain mastery of both A and B individually.

In a Mastery-Based Learning (MBL) environment, learners progress down the tree only after achieving mastery and complete confidence in the concepts above.

Integrated concepts are only tackled after mastering their atomic counterparts—not after a set period of time.

The progression through the nodes of this tree is governed by each learner’s comprehension level rather than a fixed timeline. There are no imposed time limits for any topic; therefore, the duration needed to master all topics in the tree is indefinite.

The only benchmark at each step is the learner’s understanding of the topic at hand.

Contrast this with the factory model, where students may have a strict timeframe—say, one week to learn A and another week for B. If, for example, a student couldn’t reach 100% comprehension after a week of studying A and then another week for B due to illness or a busy work schedule, the factory model still advances them to the next topic: AB.

If the learner hasn’t fully grasped A and B separately, it’s highly probable they will struggle with AB as well.

In disciplines where concepts build cumulatively, the complexity and ambiguity can escalate dramatically if not addressed in a linear manner.

Within the factory model, a lack of understanding from week one can lead to significant challenges later, as the cumulative complexity will outpace the learner’s ability to catch up.

Over time, this gap can become so significant that it leads some students to disengage from learning altogether.

It’s a sentiment that many of us can relate to, having either experienced it firsthand or witnessed friends or classmates grappling with the repercussions of escalating complexity.

In contrast, MBL addresses complexity linearly—tackling each concept as it arises and in isolation—thereby preventing the compounding of difficulties.

This structured approach allows learners to build a solid foundation of understanding, ultimately leading to greater confidence and success in mastering advanced concepts.

Constraints of Mastery-Based Learning 

Mastery-Based Learning (MBL) isn’t a catch-all solution for every educational challenge; if it were, it would be more widely adopted.

By eliminating time constraints and insisting on mastery at every stage, MBL introduces specific limitations to the learning experience:

  1. Assessment-Driven: The curriculum relies heavily on assessments to gauge understanding.
  2. Depth Over Breadth: Progress is assessed based on your depth of understanding rather than the quantity of topics covered or time spent on each one.

Consequences of Mastery-Based Learning

These constraints lead to notable implications for students. To thrive in an MBL environment, it’s crucial for learners to comprehend these consequences and take proactive steps to mitigate any challenges they may pose.

Indefinite Duration

The most significant source of anxiety in an MBL framework often stems from the lack of a definitive timeline.

It can be challenging to align MBL with personal goals, particularly if you have performance-oriented aspirations, such as “I want to secure a job within six months.”

The indefinite duration may clash with these goals, making it harder to gain support from family and friends who may expect a concrete graduation date.

Self-Paced Learning

While self-paced learning allows for a tailored approach to mastering concepts, it can also result in a solitary journey, as the traditional support system of classmates is absent.

Assessments

In MBL, mastery is paramount, and assessing a student’s understanding is a key aspect of the curriculum. At Azua, we utilize rigorous assessments commonly employed by leading employers, such as written exams, one-on-one live coding interviews, and take-home projects.

While this structure is beneficial in theory, not passing an assessment can be frustrating.

After receiving a “Not Yet” on an assessment (we prefer this term to “fail”), a mandatory waiting period is imposed before a retake, which can be particularly irksome if you’re eager to move on and not fully committed to the MBL approach.

Depth of Knowledge

When progress is gauged by the depth of understanding, it’s easy to feel stagnant, as you may not be rapidly advancing to new topics.

This can be less thrilling than swiftly transitioning to fresh concepts, but prioritizing excitement over genuine learning can undermine true comprehension.

The Mastery-Based Learning Process

To illustrate how MBL operates within the framework of the skills/knowledge acquisition tree, let’s revisit the diagram.

This time, we’ll incorporate the components of MBL to demonstrate the progression from atomic concepts to integrated concepts.

[At this point, you would typically include a visual representation of the skills/knowledge acquisition tree, integrating the principles of MBL, to provide a clear understanding of how learners advance through their educational journey.]

In summary, while MBL has its challenges, it fosters a deeper understanding and mastery of concepts that ultimately lead to greater success in the long run.

By recognizing and addressing these constraints, students can better navigate their learning experiences and emerge as proficient, capable individuals in their chosen fields.

This depiction of the learning process for concepts A, B, and AB through a mastery-based approach emphasizes a critical point: the absence of any mention of time constraints in the learning sequence.

In an MBL framework, the learning process involves two primary steps:

  1. Practice Atomic Concepts: Students focus on understanding and mastering each atomic concept independently.
  2. Pass an Assessment: After achieving mastery in the atomic concepts, students must pass an assessment to confirm their understanding before moving on to integrated concepts.

Once both steps are completed for all prerequisite atomic concepts, the process is then replicated for the integrated concept.

This structure allows for complete disregard of how long it takes to achieve mastery, thus eliminating the anxiety and pressure that often accompany time-based learning environments.

Local Mastery Before Integration

A phrase often used to encapsulate this philosophy is “local mastery before integration.” While grasping atomic concepts typically isn’t the hardest part of learning (with sufficient time and practice, most students can achieve understanding), the challenge arises when it comes to integrating these concepts.

Confusion often occurs when learners rush the integration process without achieving local mastery first.

In an MBL program, the emphasis is on ensuring comprehension without compromising on mastery, contrasting starkly with the factory model of education that mandates a fixed duration for topic coverage, regardless of the level of understanding achieved.

How to Learn with Mastery

Having established what MBL entails and how it contrasts with time-based or factory models, let’s delve into practical strategies for shifting from a time-centric mindset to a mastery-based approach.

This transition can be challenging, even for those who intellectually grasp the concept of MBL. Here are several pointers to facilitate this shift:

1. Eliminate Timelines

A common concern among prospective students at Azua revolves around timelines. Many will look at their calendar and set arbitrary deadlines for program completion. For instance, they might allocate two weeks for topic A.

However, if they find themselves in week three, only halfway through, anxiety often sets in. This anxiety leads to altered behavior that may encourage shortcuts in understanding, undermining the core principles of MBL.

Instead of performance-oriented goals, it’s vital to set mastery-based objectives, such as “I will dedicate 10 hours to studying this week” or “I will focus on exercises for two hours today.” Remember, mastery cannot be rushed into a time frame.

2. Stay Focused on Fundamentals

While it’s easy to advocate for a focus on fundamentals, implementing it can be more difficult, especially for the curious-minded.

The desire to keep up with every emerging technology can be overwhelming, fueled by the fear of missing out.

Articles proclaiming the obsolescence of certain technologies can exacerbate this anxiety.

However, it’s essential to prioritize the fundamentals—concepts that remain relevant and serve as a foundation for your career.

3. Work in a Sustainable Fashion

Developing sustainable study habits is critical, as your learning journey doesn’t cease upon entering the workforce.

In fact, ongoing learning becomes even more important.

Establish effective study habits not only for your current training but also for your professional growth.

Avoid cramming; it doesn’t support long-term retention or help with assessments requiring fluency.

As demonstrated in Kathy Sierra’s example of a “chick sexer,” achieving mastery requires consistent practice and time for your brain to form accurate mental models.

This deep, intuitive understanding cannot be hurried, and true expertise arises from prolonged, deliberate practice.

4. Eliminate Expectations and Milestones

Finally, it’s crucial to eliminate milestones and performance expectations.

This principle ties back to the concept of mastery versus performance goals. In a mastery-based system, an effective metric of progress is the amount of time dedicated to learning, rather than arbitrary deadlines or completion milestones.

Consider what “finishing” a course really means: If you skim through all assignments but only achieve 60% comprehension, can you truly claim to have finished?

Instead of adhering to a timeline, focus on mastery-based goals, such as “I will spend 100 hours this month on this topic,” to gauge your progress.

Conclusion

By adhering to these strategies, learners can cultivate a mindset conducive to Mastery-Based Learning.

Emphasizing comprehension over completion, fostering a focus on fundamentals, and adopting sustainable study habits will ultimately facilitate a richer, more fulfilling learning experience, paving the way for success in both academic and professional endeavors.

Mastery-Based Learning at Azua

At Azua, Mastery-Based Learning (MBL) is not just a theory; it’s a structured, practical approach to education that emphasizes understanding and mastery over arbitrary timelines and completion goals.

Here’s how MBL manifests in everyday scenarios at Azua, focusing on the curriculum’s structure, assessment methods, and the overall learning experience.

Structure and Sequence

The curriculum is designed around a tree of concepts, similar to the skills/knowledge acquisition tree discussed previously.

The learning progression follows this sequence: first, students master atomic concepts (like A and B), then they tackle integrated concepts (like AB).

In a mastery-based environment, students sometimes need to revisit earlier concepts, which may feel counterintuitive.

For example, a student might successfully master concepts A and B, passing assessments on both.

However, upon reaching the integrated concept AB, they might realize they don’t fully understand A or B anymore due to a lapse in retention or understanding.

In this scenario, the right course of action is to revisit and refresh their knowledge of A or B before attempting the AB assessment.

In a factory model, going “back” might carry a stigma of failure, but in a mastery-based system, this backward movement is an essential part of the learning process.

It emphasizes the importance of comprehension and retention over mere progression through a set curriculum.

Assessments

Assessments play a pivotal role in diagnosing students’ mental models and identifying areas for improvement.

At Azua, considerable time and effort are devoted to crafting and refining assessments, which are seen as a serious component of the learning journey.

These assessments mirror the hiring processes of top employers, utilizing various formats to evaluate comprehension:

  1. Written Answers: Students must articulate their understanding of concepts clearly and concisely, demonstrating their ability to communicate technical ideas effectively.

  2. 1-on-1 Live Coding Interviews: This format simulates real job interview scenarios, allowing students to showcase their coding skills and problem-solving abilities under pressure.

  3. Take-Home Code Challenges: Students are tasked with completing coding projects that reflect realistic job requirements, providing an opportunity to apply their knowledge in practical situations.

These assessments are rigorous, ensuring there is “no place to hide.”

They compel students to thoroughly understand the material, preparing them for actual job interviews and reinforcing the MBL philosophy.

The Learning Experience

The overall learning experience at Azua fosters an environment where mastery is prioritized over speed.

Students engage in a self-paced curriculum, allowing them to invest as much time as needed to fully grasp concepts before moving on. This structure promotes:

  • Deep Learning: By focusing on mastery, students develop a comprehensive understanding of each concept, which enhances their ability to tackle more complex topics.

  • Resilience: The emphasis on revisiting and mastering concepts cultivates a growth mindset, encouraging students to view challenges as opportunities for learning rather than setbacks.

  • Community Support: While students work independently, they are part of a broader community of learners. This community offers support, encouragement, and collaboration, making the journey less isolating.

Conclusion

Mastery-Based Learning at Azua is designed to cultivate a deep understanding of software engineering concepts through a structured, assessment-driven approach.

By prioritizing mastery over timelines and encouraging the fluid movement between concepts, students are empowered to develop a robust skill set that prepares them for successful careers in tech.

The integration of assessments that mirror real-world job requirements further enhances their readiness for the workforce, ensuring that graduates leave with the knowledge and confidence to excel.

Focus on Things That Don’t Change

The last thing that we want to mention here as a direct consequence of our mastery-based Learning approach: our focus on things that don’t change.

If you’re asking people to learn to mastery, you can’t teach them something that will not be as useful after a year or two.

At Azua, we’re not trying to catch a wave or take advantage of a surge in demand.

Instead, we’re trying to focus on things that’ll be useful to you for decades to come, such as a systematic problem-solving approach or learning how to deconstruct a programming language or building sound mental representations of how web applications work.

Everything we’re trying to do at Azua is with an eye towards sustainable studying habits and building skills for a long-term career.

At that intersection are “things that don’t change”.

Conclusion and Final Thoughts

We believe in Mastery-Based Learning so much that we have organized our entire school around it.

We believe that the best way to learn any useful skill so that you can get to a professional level is to pursue mastery-based goals.

But in reality, this is not easy and does not come naturally to everyone.

The most difficult thing to come to terms with is the indefinite duration.

It’s the main source of trepidation and anxiety for students and it’s exacerbated by a lot of social factors: for example, people around you ask questions and they’ll want to know when you’ll be graduating.

It’s not easy for them to understand that learning to mastery takes an indefinite amount of time.

You may even sense that they are starting to question your ability. It’s not easy for anyone to face that social pressure and stigma, but know that every Azua student faces these pressures, too.

In order to better handle these pressures, be open to the idea of removing timelines and dealing with all these different sources of anxiety and try to create an environment for yourself that’s conducive towards mastery-based learning.

We’ll end with a hopeful thought: we’ve all experienced the limitations of the factory-based education model and have seen that a lot of people were let down by it.

With mastery-based learning, on the other hand, it’s in your control, so you can do it!

Allow your brain the time to fully absorb the material and get into the habit of learning deeply as opposed to quickly moving from one topic to the next.

If possible, try to get rid of the sources of anxiety, time constraints, short-term expectations, social pressures, and then fully allow yourself the room to learn to mastery.

Follow this slow, patient, consistent path, and you will get there.

Resources

  • We ask that all prospective Azua students read the book Mastery by George Leonard. It’s a short book and takes very little time to read, and is mandatory. Please buy it and read it before starting the paid course at Azua.

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