A guide to mental strategies for effective learning
By MITHorizon
What does learning look like? Most people picture someone sitting at a desk with books open, intently focused on reading and highlighting. Maybe the learner is also taking a few notes. Or maybe they are staring at a computer screen. These mental images align with the general stereotype of learning activity, which is oriented toward the dedication and hard work we can observe in a diligent learner. In reality, so much of what really makes learning happen is not in those visible behaviors, it is in the mental activity a person isdoing.
Beyond the motivation to focus, effective learning requires using the right mental strategies. The approach you take can significantly affect how quickly you learn, how deeply you understand, and how well you’ll be able to remember.
Your goal may determine which strategy will be most useful. Even if your goal is complete mastery, considering the sequence of approaches can also be useful. It is important to consider how to choose the right strategy at the right time, so let’s discuss some ideas to keep inmind.
Fact-based learning and automaticity
Some parts of the learning process depend upon memorizing information accurately and being able to recall it quickly. This kind of learning can serve as the foundation for more complex conceptual understanding, both by solidifying some knowledge and, perhaps even more critically, by freeing up cognitive resources to think about biggerideas.
For example, imagine trying to solve a simple algebra problem like 6(x + 7) — 4(x — 9) = 14 if you had to recalculate 6 * 7 by adding 6 + 6 + 6 + 6 + 6 + 6 + 6 (I’m tired just looking at that!). Or trying to read a sentence, say in a foreign language, without knowing what sounds each letter makes. You’d spend a lot of your time and mental energy on really basic elements and not have the brainpower left over to actually think about the underlying meaning of what you are doing or how it relates to anythingelse.
We need a large base of knowledge in a given domain before we can start to think deeply about it, so how can we help ourselves learn these kinds of facts more effectively? One important principle is the idea of retrieval practice. This is the idea that, to get better at recalling information fluently, we need to practice retrieving it. This doesn’t sound revolutionary, but think about how people generally study — they often reread material and repeat important information to themselves. Unfortunately, that doesn’t best prepare them for actually getting information out. Instead, techniques like practice quizzes and flashcards can be used to practice recalling something when you needit.
Memory research has found that spacing out this kind of practice makes it more effective. We’ve all had the experience of cramming for a test, remembering some of the information the following day, and then promptly forgetting it. That is because our ability to recall information tends to decrease over time, so we need to occasionally practice for a memory to become more robust and reliable.
Ideally, with enough retrieval practice, spaced out over time, our ability to recall information becomes automatic, something that requires little to no cognitive effort, leaving us free to spend our energy focusing on developing conceptual understanding.
Conceptual understanding
While it can be impressive to rattle off specific facts from memory, the goal of learning is not just to recall pieces of information easily. We know we’ve learned well when we understand something deeply and can explain the underlying ideas and principles. To make sure that kind of learning occurs, we need to use techniques that target that level of knowledge. One surefire way to foster that kind of learning is by asking yourself questions. Research on both “self-explanation” and “elaborative interrogation” reveal a benefit for asking yourself questions such as “how?” and “why?,” and making sure you can respond in your own words and in a way that is clear to yourself.
There are a few reasons this technique is important to spotlight. For one thing, we can often trick ourselves into thinking we’ve learned something more deeply than we really have. After reading some text on a new topic, it is easy to feel like we’ve learned something from it. But if we need to explain what it is we learned, we may discover that we are missing some critical elements. For another thing, explaining in our own words can help us make connections that deepen our understanding. Creating your own, personally relevant analogies and metaphors will help you learn in a robust way. For example, someone learning about generative AI systems might come up with an explanation like “Generative AI systems are like chefs who have trained by watching tons of chefs do all sorts of things related to cooking for all different cuisines and techniques (its training data). When you ask this chef to make a new recipe (to generate a response), it draws on that knowledge to come up with its own ideas.” This is a surface-level kind of mapping, but this kind of thinking can lead to a better understanding of the underlying ideas.
Generative learning
Self-explaining and responding to those kinds of internal prompts are ways to get yourself to actively produce something with the information you’ve been learning. As discussed above, learning is not just about getting as much information as possible into your head; the measure of successful learning is being able to do something with the knowledge and skills you’ve internalized. So, practice doing that! One simple way you can do that is by creating a written summary of what you’ve covered. You can also create other kinds of visual representations, like concept maps or flow charts. And you can go beyond self-explanation (making sense of things in your own mind), to trying to teach others; often, we find the explanation that made intuitive sense and made us think “yeah, I’ve got this” falls apart when we try to verbalize it in a way that is clear to someone else. If your learning is happening as part of professional development, schedule some time to tell your colleagues about what you’ve been learning. Or, try creating a sample project that uses what you’ve learned. Even if the work isn’t perfect, the act of trying to productively use your knowledge will only help solidify it and build a foundation for nexttime.
The righttool
Analogies can be effective ways to improve learning, so here is one to consider: Choosing the right learning strategy is like selecting the most suitable tool for ajob.
It can significantly enhance efficiency, effectiveness, and satisfaction in the learning process. Whether it’s mastering fact-based knowledge through spaced repetition and practice testing or developing a deeper conceptual understanding via elaborative interrogation and making analogies, the key lies in aligning your approach with your ultimate goals and sequencing your learning in a way that helps you achieve thosegoals.
Originally published at https://horizon.mit.edu/. Part of MIT Open Learning, MIT Horizon is comprised of a continuous learning library, events, and experiences designed to help organizations keep their workforce ahead of disruptive technologies.
Choosing the right learning strategy was originally published in MIT Open Learning on Medium, where people are continuing the conversation by highlighting and responding to this story.
