A resource for safe and effective troubleshooting from the leaders in simulation training.
Greetings, Troubleshooters! Welcome back to TST. We’re currently in the middle of a series of posts on adaptive learning. In case you missed it, Part 1 was an overview of adaptive learning (AL), and Part 2 was on personalized learning 101. Today we’re talking about why adaptive learning and computer simulation training go hand in hand.
Okay, let’s dig in!
Data—AL Needs It, Simulation Feeds It
The major reason adaptive learning and computer-simulation-based learning are such a natural fit is data. One the one hand, you have digital, simulation-based learning software, churning out all manner of data about learners—tracking their progress through the course content, their performance during assessments, the time it takes them to complete a simulated scenario, etc. And on the other hand, there’s AL, just itching for data to feed its algorithms. Talk about a symbiotic relationship!
In order to be truly useful, AL needs data. AL collects data from large numbers of learners to see how they learn the specific material, and then in turn itself learns from that data (machine learning) so that it can become more intelligent over time with respect to the content sequencing, learning objectives, and learning style, and provide sophisticated, real-time adaptation of course material based on learner performance and behavior.
Admittedly, very basic adaptive learning systems that don’t use machine learning to analyze user data can still guide learners through a custom path based on their performance and behavior. They could in fact do that with just one user and no knowledge of what other users are doing. But in order to create a more personalized and better-informed overall experience for the learner, data from large numbers of users for the machine learning (adjustment of the algorithms) is required, providing higher-level analysis that benefits from seeing the bigger picture.
Just as an example, suppose you subscribe to a movie streaming service and one night you watch Avengers: Infinity War. The next night when you log in, the service suggests you might also like Thor, Ant-Man, Iron Man 2, Justice League, Black Panther, Captain America, and … Gone with the Wind. Now there’s an algorithm that needs a little more data! If that service had more viewers watching those movies, it would know that (almost) no one who’s watching superhero movies is also watching epic historical romances. More data gives AL the ability to more optimally personalize the learning path.
AL Refines Simulation-Based Learning
But it’s not just AL that needs simulation’s data. Simulation learning systems benefit from integrating adaptive learning capabilities. With access to the data generated by computer-based simulation e-learning software, AL can detect, for example, significant spikes in course failings and alert the instructor that something is wrong pedagogically. It can also see if 90% of learners are getting a certain question wrong, and alert content designer that it has to be reworded. It can alert course designers if students are being routed past a particular lesson because they already know the material, but then are failing the assessment. AL can give valuable feedback to the content designers so that they can refine the simulation to address these (and far less obvious) issues, so that the simulation-based education or training system using AL is improving, rapidly and continually.
Why simulation rather than just e-learning?
At this point, you may be asking yourself, why is simulation-based learning in particular a better fit with AL than other forms of e-learning? The answer is simple: simulation learning allows generates more detailed data than just online reading and testing. With simulation-based learning, you can get data about a wider range of activities that give better clues to help the AL diagnose what stage the learner is at and what they might be missing. For example, a strictly online learning program in which students read lessons and watch videos and then answer multiple-choice questions can tell the AL program a certain amount about what the student doesn’t understand.
But imagine a training simulation for electrical troubleshooting that can tell when a learner virtually “walks” into a simulated greenhouse and stands in a puddle of water while repairing a circuit. Or forgets to turn off the power before working on a fault. That kind of richer, deeper data can be collected from a simulation and used to help an AL system determine that the learner needs a refresher on basic safety before they actually are deployed on the factory floor.
When training for specific tasks, using a simulation can reveal important details such as how long it actually takes the student to perform that task, and whether they need more practice. It can keep track of how many attempts the learner makes to solve a problem before deciding on an answer. It can make more accurate assumptions about when a learner is guessing, or solving a problem by trial and error rather than by knowledge.
Powered by Sound Pedagogy
Pedagogy is the method and practice of teaching, i.e., how you teach what you teach, so that students can understand it and master it most efficiently. Sound underlying pedagogy is absolutely critical to the success of both simulation e-learning and adaptive learning.
For example, who decides exactly what percentage of correct answers on the assessment of one module means that a student can skip the next one? Who anticipates all of the potential actions a learner might perform in a non-multiple-choice task in a simulation? Who decides what the student’s misconception is based on wrong multiple-choice answers, and what the remedy is? Who writes the hints and other feedback that guide the learners to rethink wrong answers and come up with the right ones? All of these issues and more must be thoroughly anticipated and tested by subject matter experts (SMEs) who have actually taught the material, in order to be sure the algorithms are working as intended. The nice thing is, AL can also keep track of how successful in general learners are with a certain course structure, and monitor how success rates change after tweaks are made to the content.
All of this is the hard and expensive part. By comparison, the technology is easy!
Purchasing Simulation Software with Integrated AL
Because it’s a big, expensive job, not all manufacturers can afford to create their own simulation-based, AL integrated training programs. Nor is it necessary. If you’re training for generic skills and not on a proprietary system, you can avoid reinventing the wheel and purchase existing training software. Just be sure to research the quality of the program or system first. Not all are created equal, and it really is a question of the vendor’s experience in the subject matter, in sound pedagogical theory, and in the technology.
And that’s it, Troubleshooters! Tune in next week when we pull out our crystal ball and reveal … the future of learning!
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