How I Approach Structured Learning After School – Radi's Blog

How I Approach Structured Learning After School – Radi's Blog

Structured learning, as opposed to unstructured learning, is characterized by intentionality. It involves clear learning paths and objectives that are charted out, such as higher education, gap years, or apprenticeships. However, after school, structured learning can become more challenging. In this article, I will share my approach to structured learning and how I navigate the different phases of this process.

The first phase of structured learning is discovery. This is where I actively seek new ideas and knowledge by immersing myself in various activities and experiences. I walk through life with my eyes wide open, taking cues from work, hobbies, consumed media, or literature. The goal is to generate a backlog of topic areas that I can delve into, each tied to a specific objective. By doing so, I can identify where and how I can realize the value of learning a particular topic.

The next phase is sense-making. Once I have identified a topic of interest, I dive deeper into research and exploration. This is where I engage in active learning and document my findings. I create artifacts on my public channels, such as tweets, articles, or visuals on my blog. By documenting my learning journey, I not only solidify my understanding but also contribute to the public record of the internet. This encourages others to engage in similar behavior and allows them to benefit from my insights long after I have moved on from a particular topic.

To ensure I capture as much information as possible, I make use of tools like Glasp to annotate and organize written content. This helps me create a comprehensive collection of literature that I can refer back to and share with others. Through this process, I feel a sense of joy knowing that I am leaving breadcrumbs for others to find value in, even after I have moved on from a particular topic.

Now, let's shift gears and explore the concept of network effects and how they can be measured. Network effects refer to the phenomenon where a product becomes more valuable as more people use it. To create and grow this value, it is crucial to understand and measure various metrics related to network effects.

One important metric is the percentage of organic users versus paid users. In a business with network effects, the share of organic users should increase over time. This indicates that as the network grows and becomes more valuable, more users are naturally drawn to join without the need for paid acquisition efforts. Companies like Facebook and Uber initially invest in paid acquisition to jumpstart growth but eventually reduce their reliance on it once they achieve critical mass.

Another metric to consider is the source of traffic. Valuable networks should not only attract more users but also become a destination where users spend more time. By analyzing how much traffic or transactions are generated internally within the network versus from external sources, we can gauge the network's value and growth.

Paid customer acquisition costs (CAC) is another important metric to track. While CAC should theoretically decline as network effects accelerate, external factors like market competitiveness and viral loops can affect the actual cost. Monitoring the time series of paid CAC can provide insights into the effectiveness of acquisition strategies.

Competitor-related metrics are also crucial in measuring network effects. Understanding the prevalence of multi-tenanting, where users use multiple similar services, can help identify opportunities to improve the product and reduce user attrition. Additionally, assessing switching or multi-homing costs can shed light on how easy it is for users to join a competing network and whether they would find more value in doing so.

Engagement-related metrics, such as user retention cohorts and core action retention cohorts, are indicative of network effects. Newer cohorts should exhibit better retention rates and engagement levels compared to older cohorts, demonstrating the growing value of the network over time. Dollar retention and paid user retention cohorts are particularly relevant for subscription and paid products, as they reflect the financial sustainability and attractiveness of the network.

For marketplace businesses, metrics like match rate, market depth, and time to find a match are key indicators of network effects. A high match rate and sufficient market depth ensure a positive user experience and increase the likelihood of successful matches between supply and demand. Furthermore, assessing pricing power and unit economics can provide insights into the overall health and profitability of the business.

In conclusion, both structured learning and measuring network effects require a systematic approach and careful analysis. By approaching learning with intentionality and documenting our findings, we can contribute to the collective knowledge and inspire others to engage in similar behavior. Similarly, by measuring the right metrics and understanding the dynamics of network effects, businesses can create and grow value for their users and stakeholders.

Actionable Advice:

1. Embrace structured learning: Actively seek new ideas and knowledge by immersing yourself in various activities and experiences. Create a backlog of topic areas tied to specific objectives.

2. Document your learning journey: Use tools like Glasp to annotate and organize written content. By documenting your insights and sharing them publicly, you contribute to the collective knowledge and inspire others.

3. Measure the right metrics for network effects: Pay attention to metrics like organic vs. paid users, traffic sources, retention cohorts, match rates, and market depth. These metrics provide insights into the value and growth of your network.

Incorporating unique ideas or insights:

In today's digital age, the internet serves as a powerful accountability incentive for structured learning. By leaving a public record of our learning journey, we not only benefit ourselves but also contribute to the knowledge of others. This shift from institutional oversight to collective learning is a unique aspect of modern education.

Additionally, the concept of network effects extends beyond just businesses. It can also be applied to personal networks and communities. The value of a network increases as more people participate and engage, leading to greater opportunities for collaboration and connection.

In conclusion, structured learning and network effects share common points in terms of intentionality, growth, and value creation. By adopting a systematic approach to learning and measuring the right metrics for network effects, we can enhance our personal and professional development while contributing to the growth and success of the networks we are a part of.