The promise and peril of E-learning’s future are evidenced by the last time I shopped online.
I am sure this has happened to you, you make a simple search for something you may be interested in, and it follows you until the end of time. For me, there were two items I searched for, dress shoes and an engagement ring.
What often follows is a strange juxtaposition of poltergeist-like ads that pop up in the oddest places, such as this article Second Marriage More Likely to End in Divorce next to this ad:
Facebook then apparently believed I was friends with various shoe brands. And then there’s Amazon... let’s just say I’m fairly certain Alexa is using hypnotic suggestions when I’m sleeping.
These ad-hauntings had two very different results. As for my shoe search, I was interested in pricing, not buying. I’ve taken many risks in life, but buying shoes without trying them on isn’t one of them. These ads followed me for months and turned me off from the brands they boosted.
In my quest for the perfect engagement ring, just when I had lost hope and was drowning in over-priced blood diamonds, one algorithm or another picked up that I wanted something unique and sustainable, and an ad led me to a boutique jeweler on Etsy.
These two examples illustrate the promise and peril of eLearning’s future. In the shoe example, the ad was irrelevant, redundant and annoying. For the engagement ring, it was relevant, timely and personalized.
The historic model of eLearning is a 45 min to 1-hour box of broad-spectrum information dump given to everyone via PC, whether they need it or not. The data gathered from it was limited to... did the learner survive to the end, and have they filled out a happy sheet (eval).
Modern approaches are shorter, data-driven, customized to the needs of the Learner and can be accessed on any device or browser.
Let’s explore how to leverage five current trends in eLearning to achieve this result.
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That information is much easier to remember when it is chunked into smaller bits. It is very difficult for working memory to hold more than four items at a time. That is the truism that drives Micro-Learning.
The promise: Micro-learning, when it’s short (2-5 minutes), reinforced over time, and applicable (useful to the learner), it is an extremely effective way to move information into long-term memory, transforming that information into knowledge.
The peril: You lose a degree of narrative and context when you employ micro-learning, and without careful thought and design, the learning bites can appear as non-sequiturs.
How to make it work: Implementing Micro-Learning requires a good delivery platform. This article lists some of the top providers. Finding the right one requires a good amount of research, and any solution is only as good as the quality of the content.
2. Adaptive Learning
Adaptive Learning “adapts” the content to the Learner’s instructional needs based on their performance.
The content may be question-based wherein the succeeding content will be either remedial or advanced based on the Learner’s answer. Or the learning may be a complex simulation or scenario, in which the activities that follow are based on the learner’s former behaviors/actions.
The instruction is always relevant to the learner and adjusts to the learner’s needs as determined by their ability. Also, the effectiveness of adaptive learning can be quantified, if properly tracked.
If the content isn’t robust enough, or well-designed, learners can get stuck and frustrated, like I was with the shoe adverts that followed me for months
How to make it work:
For adaptive learning to be effective, it requires a robust platform to manage the content. The more data you have on the Learners’ performance, the more effective the system becomes. For example, if close to 50% of the population performs poorly on particular topics or groups of questions, then more instruction may be needed there. If 80% plus have difficulty, then it’s more likely the content/instruction wasn’t well designed.
For the last few years, I have used the Axonify platform for adaptive learning and gamification, and one area in which it excels is the amount and type of performance data it generates. There are other systems with similar functions. It is important to take your time when selecting a system to ensure it meets your needs, both in the system’s functionality, but also per the vendor’s culture and support.
3. Personalized Learning
Personalized or targeted training is a furtherance of adaptive learning and ideally should be employed in tandem (most systems that do one, also do the other).
The difference is the term personalization focuses more on the information that is fed into the system, such as role, or an individual’s level of compliance risk.
For example, a frontline client-facing employee may need more robust instruction on detecting identity fraud, than a board member, likewise, a corporate officer may require more in-depth training on anti-trust or handling material (important) information that has not been made public (i.e. a merger).
The promise: The right information delivered to the right people at the right time.
The peril: Thorough risk, task and skill assessments are required to match the roles, duties, positions to the content. It may take some trial and error to get this right.
How to make it work: After performing the initial assessments, create a common core batch of content and use the results of the initial rollout as a baseline from which to determine where to focus your energy and to validate your assessments.
Have you ever gotten sucked into a mobile game, like Angry Birds, or Candy Crush?
Gamification plays upon our brain’s inherent reward system. When we reach a level or score high on the leaderboard, we get a dopamine hit. Gamification uses this natural response for good (we hope).
The promise: Making learning fun. Gamification can transform learning from a chore to a pleasurable challenge. It is also a proven method for promoting certain types of behavior.
The peril: The gaming mechanism can become more important than the learning content itself. Over the last five years or so, you’ve seen a plethora of systems that claim they can improve brain function through games (such as Lumosity). The results for this aspect of gamification are mixed. If not properly designed, gamification only succeeds at making the learner better at the game.
How to make it work:
The game/objective should align with the behavior you want to affect. One of the simplest examples of gamification are rewards cards (i.e., Get 10 coffees and get the 11th free).
The rules of the game must be easily understood, fair and consistent.
The game should not contradict learning content.
Everyone has to buy in, from the CEO down. The social aspect of gamification, such as leaderboards and badges, only works if everyone plays.
5. Training as Advertising.
In all truth, this really isn’t a trend in instructional design, but I believe it should be for certain topics, like the very basics of compliance and ethics. At a fundamental level, these topics aren’t about knowledge, they are about attitudes and behaviors - areas in which advertising techniques are most effective.
Remember my quest for the perfect engagement ring? That “tradition” was invented in the 1950s by advertisers.
Here’s a question for you:
Should you be nice to your mother?
B) Only on Mother’s Day
C) It depends on the psychological pain she caused
Now look at this picture:
The challenge of training concepts that are basic truism is that your audience already knows the answer, therefore the goal isn’t to test knowledge, but to strengthen the culture of compliance, or safety, or equality.
Appealing to your audience's emotions is an effective way to reinforce positive cultural elements.
Recently I began car shopping, just as I was finishing up this article, this advertisement appeared. The advert shows the exact car I’ve been looking for, and in the price range, I want. And there you have it, it is relevant, personalized, data-driven and most importantly, fills a need.
The future of learning is adaptive, personalized, contextual and data-driven.