How risk and reward can increase engagement in elearning
Engagement is unsurprisingly one of the most desired trophies in the elearning arena. How do you keep learners interested enough in the content that they actually reach its learning goals?
It’s an uphill battle to be sure, but there are ways to tip the fight in your favour. One seemingly underused tactic comes from game theory: risk and reward.
Reward as a concept is pretty easy to connect to elearning. Not only are you supposed to benefit from the learning both personally and professionally, but it’s common to be showered in praise, points and prizes. Risk is a little trickier to associate with the field, and you’d be forgiven for that. After all, why would you want to risk anything when learning?
Double or nothing
Well, risk and reward are two sides of the same Poker chip. Risk without reward isn’t particularly fun, but reward without risk can often feel meaningless, or at the very least uninteresting. A good balance of the two can actually produce a more compelling experience that not only gives the intended lesson some impact, but also helps to keep learners engaged.
Sid Meier, creator of video game series Civilization, once spoke about this aspect of design at the Game Developers Conference in 2012 :
One classic decision type is a risk-versus-reward scenario that asks the player to weigh potential penalties against the possibilities of rewards.
Introducing decisions like this in elearning may sound counter-intuitive on the surface, however, it’s important to remember that unfavourable outcomes don’t necessarily hinder learning and understanding. Instead, they have a greater impact and can motivate the learner to beat the system. This is why growth is commonly offered as a caveat to failure, and why games function extremely well as an interactive medium.
Good decisions are situational. There’s a very key idea that when the decision is presented to the player, ideally it acts in an interesting way with the game situation.
Interesting decisions are persistent and affect the game for a certain amount of time.
Now, Civilization is a complex game, so there are countless variables to play with and a lot of the choices are explicitly presented to the player, but risk and reward can be found in many forms. It can be more organic and spontaneous, occurring naturally through active play as opposed to measured pivot points.
In his Gamasutra article, Super Meat Boy co-creator Edmund McMillen  considers how Pac-Man, a very simple game, uses risk and reward to great effect:
So the first and most obvious aspect of r/r in Pac-Man is the blue ghost multiplier. When the player eats a power pellet, the four ghosts chasing you become edible for a small amount of time and attempt to avoid you.
Eating said ghosts will result in score points, and with each ghost eaten after the first, that score is multiplied.
The risk here, of course, is the fact that the ghosts will turn back to normal very quickly, so eating them becomes a race against the clock, if you’re too close to one when they become normal you usually die.
McMillen continues to clarify that the risk is a loss of life and the reward is a higher score and additional lives. He also highlights that this path of action is completely optional – the player can choose to avoid that mechanic altogether if they wish.
Putting it into practice
With that kind of design in mind, the takeaway for maintaining engagement in elearning lies in consequence and agency. Choices that carry some amount of weight are choices you become invested in, and the reason those choices carry weight is that there’s an element of risk or compromise.
Talk of consequence can give the impression of something grandiose or overwhelming, but it can be delivered intuitively and doesn’t need to have a significant effect – just an effect that demands consideration. One example can be found in a module Kallidus developed for national transport organisation Govia Thameslink Railway.
The module has a core set of challenges that the learner must address as they follow four established characters on separate journeys across the country. Light-hearted questions on company standards and railway trivia pepper the four sections, for which points are awarded. At the end of each section is a three-question bonus round where the learner has the opportunity to gain additional points. Each correct answer doubles your acquired bonus points and then some.
However, that alone could feel a bit hollow. The catch is that you only have 10 seconds to respond to each question, and a wrong answer results in your expulsion from the round and the loss of all bonus points (non-bonus points are safe!). The especially interesting part is that between questions, you’re given the opportunity to either bank your earnings and leave the round or continue.
So, the risk in this scenario is clear; the loss of all bonus points. The potential rewards are also clear; doubling your bonus points sounds rather enticing. Having to answer the questions against the clock adds another layer to the decision making and increases tension.
The choice to either bank your points or continue is what brings the whole interaction together, though. The outcome has consequence either way and more importantly, the learner is given a satisfying amount of agency. What if you’ve correctly answered the second question by the skin of your teeth and have the opportunity to lock your points in? Do you take the spoils and run, or attempt the final question for maximum points?
Here’s what you could have won…
If you’re unfamiliar with video games, you might liken this interaction to the sort you see in TV game shows. These shows have to be compelling for both the players and the viewers, so risk and reward, and ultimately choice, is baked right into their core. There might not be a whole lot of subtlety in these implementations, but that’s arguably what enables them to be immediately understood and exciting.
Though e-learning has evolved to incorporate more game-like design as time has passed, there’s certainly still a temptation to shy away from granting agency to the learner, as well as the opportunity to fail. However, we can see that using risk and reward in tandem as the fundamental model of interaction (without overshadowing the content’s purpose) can really serve to boost engagement levels in elearning.