The reason microlearning is so popular is that it is both engaging and effective. The science of microlearning — which builds on cognitive science related to adult learning — explains much of the success of microlearning. Principles of cognitive science reveal the secrets behind effective microlearning. Here, we’ll examine the science of microlearning.
The human brain learns two basic types of information: Cognitive and behavioral.
Cognitive learning includes factual information, rules and regulations, the steps in a process, how to use a tool or the format of a business letter. When introduced, this information is placed in short-term memory.
Repeated exposure and association with other information in short- or long-term memory might mean the brain moves a bit of knowledge to long-term memory. This is the goal of learning; the learner retains content in long-term memory for recall as needed.
The microlearning format of short, frequent, focused lessons encourages retention.
The behavioral skills side of learning includes motor skills — anything from riding a bike to performing heart surgery. It also includes so-called soft skills, like communication and understanding and using social conventions, the ways we interact with people.
Motor skills learning is a function of practice — often along with some natural talent.
Soft skills or “social” skills are often learned in the context of interacting with others. When one’s behaviors are acceptable, others reinforce this behavior with smiles, praise, and other responses that are emotionally rewarding. Conversely, unacceptable behaviors are punished with ostracism, ridicule, reprimands or other unpleasant consequences. The emotional responses trigger physiological responses that feel good, so we keep doing whatever triggered them — or that feel unpleasant, so we avoid repeating them.
Microlearning excels at teaching, refreshing, and reinforcing cognitive learning. As well, microlearning techniques may be used to practice and reinforce behavioral skills.
Engaged learners spend more time interacting with content. They pay more attention to the material — and they remember it longer. Engagement is an often-elusive goal in corporate eLearning.
One way many learning developers seek to boost engagement is gamification.
Gamifying content means adding layers of “game elements” on top of the instructional content. A game element might mean using the format of a popular game, like Jeopardy or a crossword puzzle, and working the content into that format. It can also mean requiring learners to move their character around a board or through a simulated environment to find questions or nuggets of information.
A common gamification technique is adding ways for learners to earn points or badges or compare their progress with that of colleagues or rival teams or departments.
It's also possible to build a “serious learning game.” This entails structuring the entire training as a game, with a challenge, goals and a way to measure and track progress.
Gamifying existing content is a quick and easy way to — many microlearning developers hope — make content more engaging to learners.
The reason that gamification appeals to people and gets them to engage is that people often are drawn to the opportunity to win — points, prizes or bragging rights. Gamification appeals to learners’ competitive sides and their motivation to keep playing until they succeed.
Successful microlearning might use game elements to build motivation. Gamification is one way to do this, but there are other ways that microlearning drives engagement, such as delivering short, focused and highly relevant content.
Regardless of how learners receive information, they learn it in small, bite-sized pieces. Delivering a 20-minute video or a 90-minute course doesn’t suddenly make learners’ brains expand to be able to swallow, digest — and retain — hundreds of separate pieces of information all at once.
The science of microlearning follows cognitive science: People learn incrementally. Microlearning accepts this science — and delivers learning in the way people learn.
An essential principle of learning science — and therefore of the science of microlearning — is that people learn in a hierarchical way. That is, they learn the simple elements first, then progress to more complex concepts. Successful microlearning presents content in this way.
In learning-science-speak, this is called scaffolding.
First, learners encounter vocabulary, definitions, the language of their field. They can then use those terms and definitions to understand how to do things — if training is intended for novice bank tellers, they might first learn the definitions of different types of accounts and transactions. Next, they will learn how to do things with those words: open or close an account; deposit, withdraw or transfer money. Finally, learners can apply this knowledge to a variety of scenarios.
Moving from simple to complex provides opportunities for both cognitive and behavioral learning; it also provides ways to ensure that learners get early successes. Feeling successful and seeing progress motivates learners to continue engaging.
Mastering vocabulary is a cognitive skill. Stringing steps together correctly to figure out how to do a key task is also a cognitive skill.
Mastering new concepts, terms and skills offers the opportunity for a reward, the great feeling of “getting it.” This is then magnified when the learner successfully applies new knowledge in scenarios that reflect tasks they will face daily on the job.
This type of learning is a perfect fit with microlearning, delivered in short focused lessons that begin with basic concepts and skills, then move to more complex training content.
Some microlearning platforms add an element that makes them more successful: adaptive learning. This could take the form of an algorithm that looks at learners’ past performance to identify what they already know. The algorithm also looks at the individual learner’s goals: which topics they need to know and how well they need to know them.
An adaptive learning platform delivers unique content to each learner. This content targets the knowledge gaps the algorithm has identified — the topics where learners often miss questions.
An adaptive microlearning platform will deliver more content on weak topics that an individual learner needs to master at a high level. It will not ask learners to engage with content on topics they already know well.
One of the things learners hate most about required training is having to go through an entire course when they already know — or don’t need to know — much of the content. Successful microlearning delivers short content on topics they need to learn.
This reduces learners’ frustration and improved their learning experience by ensuring that training is relevant to them. It also saves additional resources, time and money by reducing the amount of time that learners spend on unnecessary training.
If your learners are already proficient, and you can measure it, why waste time and money “teaching” them?
Exposing learners to information one time does not mean they’ve learned it — or even that they will remember it. In fact, the fire hose of information that most eLearning courses dump on learners is practically guaranteed to ensure that learners don’t remember specific bits of information.
Breaking content down into smaller pieces is the first step in improving learning and retention. The next essential element is spaced repetition.
Repeated exposure to information tells the human brain that the information matters and is worth remembering. The brain also looks for ways to connect new information with stored information.
Successful microlearning uses this principle of cognitive science.
Some platforms challenge learners and push them to the edges of their existing knowledge — then encourage them to stretch a bit more — by asking them to recall and apply information in different ways.
This approach applies the cognitive science principle of interleaved learning. One example is having learners move between different styles of questions: factual, fill-in-the-blank, etc. They might also encounter application questions — scenarios that they might encounter on the job, with questions that require recalling what they have learned to get to a correct response.
Mixing up the ways people use and apply information challenges the brain far more than rote recall of the answer to the same few questions. It also triggers the brain to build new connections among more pieces of information.
A microlearning app that does this — exercising the same knowledge over and over in a variety of ways — while targeting learners’ weak areas, is using brain science to ensure that learners understand and can apply knowledge.
By using a continuous practice or “drip delivery” approach, successful science-based microlearning platforms dramatically increase retention of learning, too, for long-term results that managers and business leaders can see, measure, and celebrate.
Providing instant feedback is a great way to bolster learning. If learners get specific, factual information about why a response is correct or incorrect, they’re more likely to understand the information and, once again, retain the correct information.
This continuous delivery of feedback is called “formative” feedback.
Microlearning excels at real-time delivery of information. Since microlearning delivers content in small chunks, it’s easy to support the cognitive science principle of providing formative feedback.
A common feedback approach is to wait until learning is “completed,” then administer a test and provide what is called summative feedback. At this point, though, the learner is grappling with many points of information. Expecting them to sort out which ones they missed, which ones they got right — and why — and remember all that is asking a lot.
Formative feedback is delivered in the moment. Immediacy is part of the science of microlearning. A platform that engages learners in activities like answering questions or choosing the appropriate response in a scenario can easily provide a sentence or two of feedback.
To be effective, formative feedback should not evaluate the learner’s effort with comments like “Good job!” or “Oops!”
Effective formative feedback sticks to the facts, explaining — using the learning content — why a response is correct or incorrect. This provides another opportunity to repeat and reinforce the learning — offering more examples of how to apply the science of microlearning to build impressive results.
While micro-sized eLearning is a fairly new development in online training, instructional designers will recognize the learning principles it applies: experiences with cognitive and behavioral skills, gamification, scaffolding techniques, recall practice, spaced repetition, formative feedback and mastery of vocabulary.
The science of microlearning relies heavily on learning principles, based in cognitive science, that have been around for decades. However, microlearning blends and applies these principles in an innovative manner.
A review of the cognitive science of microlearning makes it easy to understand why microlearning is so effective!