This month, the AACE’s E-Learn conference in Las Vegas will focus on the challenge of designing, developing, and assessing E-Learning. In a nutshell it will be the premier conference where attendees debate how we make e-learning better. Everything from educational policy to augmented reality, the AACE conference will bring the very best to online classrooms around the world. But the question remains “how do you get students to those online classrooms in the first place?”
In building out online games, we were reminded time and again of the critical importance of onboarding students. If students can’t log in, sign on or pair up it won’t matter how engaging your active learning experience is, it won’t be played. So we decided to chronicle our process to help you and those at the AACE to decide how to bring your students to the online classroom.
To set a solid footing for our E-Learning experiences, we benchmarked digital onboarding across industries. Specifically, we focused on how other digital tools added users, created groups, and facilitated interactions – realtime or asynchronously.
Not surprisingly, the sector with some of the most relevant parallels was the video game industry. For decades, console and content developers have been innovating around how to pair or group people together to play, what they refer to as the ‘matchmaking’ problem.
For matchmaking to work you need enough people online at the same time. This stands in direct contrast with online learning where a primary benefit to students is learning on your own time. So for us (and possibly for you), this means we need enough people (who want to learn on their own time) online at the same time. Therein lies the challenge.
To structure the matchmaking solutions we found, we built a two by two that captured two of the most relevant criteria. On the y-axis we mapped how much time flexibility a solution provides and the x-axis maps player population. As you can see, the more flexibility you want (which most e-learners want lots of), the more players you need to make matchmaking work effectively.
We’ll go through each tactic one by one below. See which one is right for you!
On the top right of this framework is the lobby solution popularized in the early 1990’s by games like Quake and Diablo. Here players login whenever they want and enter a digital lobby where they are quickly matched with other players who logged in at roughly the same time.
The lobby works seamlessly with large player populations. It’s actually the onboarding method of choice for Fortnite, one of the largest and most successful games of 2018. If have the numbers to run a lobby, go ahead! But be aware of timezones if you have a lumpy distribution of learners positioned around the globe.
Smart slots automatically pair or group users based on shared available times. If I say I’m available between 9-11AM and my partner says she’s available from 10-1PM then a smart slot technology would alert us both to the common 10AM-11AM slot. With a large player population this isn’t wildly different from the lobby but as populations get smaller, smart slots ensure that all players are grouped and ready.
For E-learning courses with lower enrollments, we’ll need to sacrifice some flexibility so that matchmaking works. Similar to the online player drafts in fantasy football and prize-driven casual games, you can limit the number of time slots available and force students to choose. If 11AM and 1PM are the only options available, all students will need to attend at that time.
The last and final option restricts flexibility but is effective in managing small student populations. Not surprisingly video games pioneered this approach and in the nascent days of the internet, games like Doom connected users using IP addresses they shared with one another. This solution was elegantly simple, similar to setting a call time and swapping phone numbers, but it ultimately places the burden of coordinating the time and date on the player.
We always strive to put the student first and recognized that sacrificing some flexibility to ensure they completed an engaging exercise was in their best interest. Ultimately we went with time assignment paired with several scheduling tools.
Sure, ending up on the far left of the framework was unexpected, but it does help highlight an important dynamic absent so far in this discussion. In the end, the optimal onboarding structure depends on the goals of the instructor or institution. Those goals need to be superimposed upon this framework, and only then can it help craft the optimal onboarding approach.
Finally, keep in mind that like any new behavior, adoption is hard, so onboarding cannot be left for version two. We learned early on that the slightest difficulty pairing a player or unpacking a teaching point often led to abandonment. So as you work to design and develop the next big advancement in E-Learning, remember that there is a lot to learn from prior pioneers. And while onboarding may not grab headlines, it can be the difference between unmet potential and viral impact.