Principles outlined by Stephen Kossyln in the chapter Science of Learning in his book - Building the Intentional University
The science of learning matured decades ago, but it is rarely used to facilitate teaching. Most classes are using methods that were developed over a thousand years ago.
Learning vs Teaching
- Teaching focuses on information transmission, learning is about knowledge acquisition.
- Teaching is often done a way that is convenient for the professor, with little thought on how best to facilitate student learning. Example: Lectures are a superb way to teach tends of thousands of students, but study after study has documented lectures a terrible way to acquire information.
Maxim I: “Think it through”
- The more you think something through, paying attention to what you are doing, the more likely you are later to remember it.
First four principles strongly focus on the fact that more processing of the relevant information will produce better memory.
- Evoke deep processing
- The more cognitive operations one performs while paying attention to such operations, the more likely once call recall that information.
Example
If you formulate example of each principle below, you will remember them much better than if you simply read and understand them.
- Using desirable difficulty
- Learning is best when the task is not so easy as to be boring but not so hard as to be over the learner’s head. Also, referred to as “Goldilocks Rule”.
Example
If you are good at math, you will need more challenging examples of new mathematical concepts to stay engaged than would someone who has less knowledge.
- Eliciting the generation effect
- Recalling information, especially when effort is required – strengthens memory for that piece of information.
Example
Frequency testing can enhance learning if it leads learners to recall relevant information.
- Engaging in deliberate practice
- To learning effectively you need to pay attention to and think through specific aspects of what you are learning
Example
If you are learning French, it’s good to have a native speaker listen to you and carefully correct your pronunciation.
Next three principles focus on ways to induce people in additional processing.
- Using interleaving
- Instead of just focusing on one type of problem its best to intermix different types of problems.
- Interleaving helps because its easier to pay attention to something new than to sustain paying attention to the same material.
Example
You will learn this material better, if you did something else after you finished reading this section and then returned back.
- Inducing dual coding
- Presenting both verbal and visual material enhances memory - it allows the brain to store multiple representation in memory.
Example
If you are given only a name or a verbal description to remember, your memory will be vastly improved if you can visualize the named object or scene.
- Evoking emotion
- Leading someone to feel emotion when experiencing an event generally will enable him or her to recall that event more effectively.
Example
If you are anxious about how an interview will go, you probably will remember more details about the interview than if you are not anxious.
Maxim II: “Make and use associations”
Structure information by using associations
- Promoting chunking
- People can only take in about of three or four organized units of information, “chunks” at the same time.
Example
To learn a list of 16 things, you can organize them in roughly four groups of four.
- Building on prior associations
- When learning something new, the more associations you can find with information already stored in memory, the better.
- Researchers have learnt, the more information you already know, the more existing associations you can use to store new information. The more branches you have, the more leaves and fruit can be hung on this structure.
Example
When meeting a new person, you may recall that person’s name by associating it with someone with same name whom you already know.
- Presenting foundational material first
- Presenting foundational material first provides a backbone to which one can attach additional information, allowing an organized mental structure.
- Exploiting appropriate examples
- Abstract ideas cannot be fully understood without examples. Examples must be memorable, in part by being associated with prior information.
- Multiple examples of the same material must be associated with each other so that they form a cluster that is associated with the to-be-learned material.
- Relaying on principles, not rote
- Learning typically requires not just becoming familiar with examples but also understanding the principles that organize and integrate examples
Example
Principles of debate can also be used in teaching, but that doesn’t require becoming confrontal (a surface characteristic of debate) but rather being sensitive to the other person’s goals and perspectives (a deep characteristic).
Creating rich retrieval cues
- Create associative chaining (a.k.a using story telling)
- Stories are built on interlocking causes and effects - this is the essence of a plot.
- Creating sequence of associations not only helps create chunks but each part of the story can serve as cue for the next.
- Using spaced practice
- It is much better to use information repeatedly over a relatively long span of time than to cram it in all at once. Spreading out learning, gives your brain more cues in the room you study, how you feel and your thoughts at the time.
- Establishing different contexts
- Far transfer is holy grail of learning. As noted earlier, far transfer occurs when information learned in one context is retrieved and applied in a very different context.
- Avoiding interference
- Distinctive retrieval cues are crucial in part because they can help the learner avoid interference from other information.
- Proactive interference occurs when material you have learned previously interferes with learning new information.
- Retroactive interference occurs when learning new material impairs your ability to recall previously learned material.
There seems to be some overlap between these principles from my current understanding and these principles are refined to five in the more recent book on Active Learning with AI.
- Deep processing
- Chunking
- Building associations
- Dual coding
- Deliberate practice
The remaining principles are applications