Learners are goal-driven decision-makers. They invest their limited time, energy and other resources in learning in order to gain in some way from that investment. Therefore a rational learning system must record, analyze and balance value and cost to maximally fulfil the user&-39;s goals in learning.
Learning systems that do not consider value and cost will direct learners to make suboptimal investments in learning.
Value is subjective. That means two different learners may have completely different "value functions".
My personal theory of the value of learning can be summarized as:
Learning is worthwhile insofar as it contributes to creative output which is shared into the real world.
The purpose behind the separation of learning processes into Input and Output (the subject of this article) is to make "creating value" (as I define it) easier.
Learners are resource constrained and must carefully allocate their limited resources to maximally satisfy their subjective theory of value.
Learning processes all have associated costs. For example:
The purpose behind the separation of learning processes into Input and Output (the subject of this article) is to minimize the cost of sharing creative output.
Specific value functions may differ between individuals - not everyone will agree that creative output is the ultimate purpose of learning, however, the maximization of value (however it is defined) per unit of cost is a goal shared between all rational learners.
The goal of the Rational Analysis of Learning is to record, balance and optimize a learning system in accordance with the learner&-39;s value function.
One distinction people often make when discussing learning processes is the difference between Passive and Active learning. It is useful to review, compare and contrast the Passive-Active model with my Input-Output model.
Passive learning is characterized by passive consumption of information.
Examples of passive learning techniques include:
Active learning strategies involve retrieval of information in response to some stimulus.
Examples of Active learning techniques include:
There is also a middle ground containing learning processes that require both passive and active components.
The Passive-Active distinction can be represented as an overlapping Venn diagram.
While the passive-active model is useful, I prefer to categorize learning processes into "Input" and "Output" in order to integrate the passive-active distinction with my theory regarding the value of learning.
The idea behind splitting learning processes into Input and Output is to distinguish more critically between those active learning processes that result in "real world" creative output (my subjective understanding of the value of learning) vs those that do not.
This model is useful to me because:
In addition to the "obvious" input activities like reading, watching and listening, my definition of Input includes certain Active learning processes, such as textbook exercises and SRS repetitions.
An Input process adds a new stream of information into the brain. Through generalization, the information stream is filtered and condensed, concepts and abstract rules are extracted and new knowledge is integrated into the brain&-39;s latticework of prior knowledge.
According to my subjective "value function", no value is created until my knowledge is employed in some process that results in creative output into the real world. Since no Input processes satisfy that requirement, none of them create value by themselves.
Input processes may directly or indirectly increase my ability to produce Output.
A rational learning system...
My understanding of Output excludes those Active learning processes that do not result in "creative output that is shared into the real world".
The information stream, having been filtered, condensed, extracted and assimilated, provides a meaningful association that results in a creative solution to some problem which you consequently share with the world.
Output processes are what I define as the "value creation" part of learning.
They represent "creative output into the real world".
Examples of processes I believe satisfy that definition:
Examples of processes I believe do NOT satisfy that definition:
The Input Output distinction can be expressed as a practical system of organization for learning. The following is an implementation of the Input Output Design Pattern in SuperMemo.
All of the elements in my knowledge tree are divided into two folders underneath the root node - Input and Output.
This separation is convenient for the following reasons:
The output branch consists of folders or concepts, each representing a piece of creative output in progress.
In the picture you can see how I organized the branch representing this article within the Output branch:
I choose to use concepts as the root element for projects I&-39;m actively working on rather than dismissed topics because of the ability to use neural links.
Using neural links allows me to 1) avoid moving material back and forth between the Input folder and the Output folder, 2) enables me to share useful material between multiple active projects and 3) allows me to use neural review to explore related ideas and projects.
Rather than using a separate task list for each project, I&-39;m currently using one &-39;global&-39; task list for the Output branch. I move individual tasks into "Tasks" folders based on the project.
Advantages and disadvantages of a global Output task list:
|Easier to compare the priority of tasks across multiple projects - can open all Output tasks in a Task list browser and sort by priority.||More difficult to compare the priority of tasks within a single project - have to use the subset browser.|
The input branch is quite chaotic. I haven&-39;t spent much time organizing it. It might not be worth the time it would require to organize it properly.
I keep a &-39;global&-39; input task list for recommended content. Recommended content includes:
One interesting neural link I made was between the concept representing this article and a concept called Knowledge Tree Design Patterns. I realized that the article&-39;s content, plus any feedback from readers could be reused as input into a broader, more general article on Knowledge Tree Design Patterns.
In this way, the entire learning process can be thought of a cycle where Input processes support creative output through Output processes, which in turn can be recycled into Input...
Initially I found it unintuitive to use neural links, as opposed to moving Input material directly into the Output folder since I was more familiar with the move function than neural linking. Later I decided that there are potentially many projects that could use the same Input material, so neural links would make it simpler to share material in the Input folder with multiple active projects.
Rather than classifying creative output into folders like "Incremental writing" or "podcast", I found it more valuable to make the creative output concepts medium-agnostic. This encourages me to share the creative output and get feedback from multiple platforms.
The emphasis on neural links in this pattern is useful because it will build up the number of high value links in your collection, making neural review much richer.
Neural links are good at expressing one-to-many relationships between valuable concepts and projects. Sharing between projects maximizes modularity and prevents ideas getting trapped into silos.
You can also inspect links to see which concepts you reuse the most in your projects.
Learners are resource constrained and goal-driven; different learners may have different beliefs regarding the end goal of learning, but all rational learners want to maximize the value they get from their learning.
Finally, the Input Output Knowledge Tree design pattern is a practical implementation of the distinction between Input and Output processes.