Learning methods suitable for INFP, efficiently build your knowledge system

1. INFP's Common "Network Learning" Mode

INFP prefers network learning. When they come into contact with a system, they like to first grasp multiple fragments within the system and become interested in learning these fragments. This is related to INFP's imaginative personality. They are too curious, so they like to expand the breadth of knowledge and explore countless possibilities.

For example, taking the history of a certain subject as an example, the common learning method is to study from beginning to end, according to the course system and timeline; for INFP, it is very jumpy. After reading the first chapter, they may not be interested in the second chapter, so they will immediately jump to the part they like to study. In the process of studying this chapter, they will find some new curiosity points, and then jump to the corresponding chapters according to these curiosity points until they have finished reading all the content they like.

"Network learning" is the learning mode that INFP likes, but it is not recommended from the perspective of the "linear mode" advocated by domestic education.

2. Advantages and disadvantages of "network learning mode"

The advantage of the network learning mode is that learning driven by curiosity will be very interesting, allowing INFPs to continuously enter the flow state without sleep and reach one learning peak after another; and the extended learning method makes it easier for us to break away from the existing learning system and generate new inspiration and creation. Our learning and results are more creative and less rigid.

But we must admit the flaws of this method:

First, too extensive learning is doomed to be insufficiently deep in certain points;

Second, it is easy to create an "information cocoon" for yourself: The instinct of the human brain is to like the known and hate the unknown, so you are likely to happily circle around in the known content but without any new breakthroughs;

Finally, if you need to quickly build a knowledge system, the "network learning method" is not as fast as "linear learning".

So, how to maximize the advantages of the network learning model, make up for its disadvantages, and easily achieve results in learning?

3. Optimization methods of the “network learning model”

To optimize the network learning model, I would like to introduce the following methods, which are in chronological order:

(1) Explore your interests: In the early stage of learning a knowledge system, if you have enough time, explore according to your interests, just like catching stars, grab every knowledge point you like, read it and use a computer (it must be a computer) to record these learning results; in fact, in the process of continuous learning of your interests, many extensions will naturally arise. For example, when you are learning a certain knowledge point, you have some doubts. The solution to these doubts must be supported by another knowledge point. You will naturally become interested in new knowledge points (these new knowledge points may be those you didn’t like before but now like very much). Driven by curiosity, the knowledge star map you light up will become brighter and brighter;

(2) Establish connections between points of interest: Believe in your clever little head, and mainly believe in your subconscious mind. In fact, when you are learning the fragments of knowledge that interest you, your subconscious mind has already outlined the internal connections between them; therefore, when you have a certain fragmented foundation for a certain knowledge system, you need to logically sort out the fragmented results you have previously recorded, including: cause and effect, time relationship, contrast relationship, subordinate relationship, parallel relationship, etc. This is why you are asked to use a computer to record the fragments. Because at this stage, you will definitely frequently copy and cut a lot of your content here and there until you form the most reasonable logical system. Writing by hand is too inefficient. (3) Forming linear tree logic: Through the study of steps 1 and 2, you have formed many small knowledge systems. There must be an internal relationship between these small knowledge systems. At this time, you should merge them into the final knowledge system. An ideal knowledge system should be linear, with tight logic, all similar items merged together, and as simple as it can be. Your linear tree logic may end up being the same as the reference book, but your cognition is much deeper than if you used the reference book to build the knowledge framework at the beginning. 1.5;">（4）Return to the first point:After you have formed the linear tree logic, you have a very deep understanding of this learning system. What you need to do next is to return to the first point and conduct extensive learning again, discover new related knowledge points, and capture better and more useful knowledge points to supplement your knowledge framework or replace the previous knowledge points. This step is an iterative process that requires continuous updating and optimization.

Use this method to carefully add, delete and prune the tree-like knowledge system formed in step 3. In the end, you will find thatyour knowledge system is like an exquisite building carved by you, standing in your mind, and the large and small knowledge points are the different rooms and furniture in this building. On this basis, it will be easy to recite certain key points, because you have edited, added, deleted, and optimized them countless times. You know with your eyes closed where this point is located in the entire knowledge system and how it was designed at that time... At this stage, your learning will become easy, and you will feel a great sense of accomplishment and satisfaction from learning throughout the process.

Summary

The "network" learning method commonly used by INFP is actually not only useful in the learning process of postgraduate entrance examinations and civil service examinations, but also in paper writing, work knowledge building, and even daily thinking patterns.

From the interest-driven point in the first point to the sense of accomplishment of building a knowledge building in the end, the entire learning method is friendly and interesting to INFP.