A resource for safe and effective troubleshooting from the leaders in simulation training.

Greetings, Troubleshooters! Thanks for tuning in to TST today. 

Today is a milestone for us—this is our 100th TST post! That’s 100 articles on topics of interest to manufacturing executives, HR personnel, Directors of Technical Training, and of course, electrical troubleshooters everywhere. You can find our searchable archive of Troubleshooting Thursday posts here, and look for tips on everything from calculating your training ROI to meeting ISO continuous improvement standards, to testing for opens with a voltmeter.  

Our goal has always been to keep manufacturers informed about the many benefits of high-quality electrical troubleshooting training. Today, we’re beginning a new series on adaptive learning, a new feature we’re integrating into our simulation-based electrical troubleshooting training system.

What is Adaptive Learning?

Adaptive Learning (AL) (sometimes called adaptive teaching or personalized learning) is an educational technique that uses computer algorithms to provide customized, optimal learning paths for individual learners. 

Every learner has unique abilities and needs, but all too often in the modern learning environment, education has become a “one-size-fits-all” solution in which the same content is delivered at the same time to a classroom of people of varying abilities, skills, and knowledge levels. Some are way ahead and get bored, while others are bewildered and left behind. AL seeks to turn that reality on its head. 

In an AL scenario, content is delivered to learners via computer software powered by algorithms that use data such as the learner’s responses to questions, tasks and experiences and their response times, etc., to generate real-time feedback such as hints or encouragement, or to decide what content the learner needs to see next. As opposed to one-size-fits-all teaching, AL helps the ones who are behind to catch up, and the ones who are ahead to advance “unshackled,” so that in the end everyone successfully reaches an acceptable proficiency level in the shortest amount of time.

AL technology relies on knowledge and best practices from several different fields: artificial intelligence, education, psychology, brain science, psychometrics (the science of objective psychological measurement), and predictive analytics. The algorithms are often complex, measuring not merely whether a learner’s responses are right or wrong, but how long they take to answer, and, if they are wrong, why they are wrong (are they misunderstanding a key point? are they just guessing?) Based on this data, the software can then determine what the source of the misconception or information gap is, and remedy it through targeted hints or additional lessons. In a nutshell, adaptive algorithms first determine what learners know, and then what they need to see next.

The EdSurge Framework: Three Components of Adaptive Learning

EdSurge is a California-based organization that specializes in education technologies. They have identified three sub-categories of AL: adaptive content, adaptive sequence, and adaptive assessment. 

Adaptive Content 

Adaptive content is real-time feedback that is instantly adapted to the learner’s performance. Usually, this feedback is more helpful than just whether an answer is right or wrong. It may take the form of remediation of an incorrect assumption, encouragement, or additional information or hint questions. Real-time feedback provides additional teaching, and encouragement helps keep users engaged, a critical part of successful learning. 

Adaptive Sequence

Adaptive sequencing refers to AL that continually analyzes student performance data to decide what content the student should see next. This could apply to the type of content, or the sequence in which content modules are presented. In real time, the algorithms create differentiated pathways for individual students by varying the sequence of content—for example, providing additional modules on concepts that a learner is finding difficult, or fast-tracking learners that have grasped the material successfully.

Instead of each learner experiencing every piece of available content, as they do in traditional classrooms, with AL, they only experience what they need to see at that point in time for optimal learning, much the same as happens with individual tutoring or apprenticeship.

Adaptive Assessment

Adaptive assessment refers to adapting the assessment material in real time, that is, altering the difficulty of a test question based on the response to a previous question. The difficulty increases if the learner gets it right, but remains easier if they are struggling. Testing can be a high-stress and discouraging aspect of education. Adaptively assessing learners can result in less discouragement and therefore greater willingness to engage with the material and continue on. Instructors are made aware of the varying assessment paths of each learner.

Adaptive Learning Is Scalable

Of course, the ideal mode of instruction is the one-on-one human tutor or mentor. In one-on-one instruction, a real live teacher brings to bear all the advantages of intuition sharpened by years of experience. The teacher works closely with the learner, observing their verbal and nonverbal cues when they’re having trouble understanding a concept, and adjusting their hints and feedback accordingly. In making those on-the-spot adjustments, that instructor is adapting to the student. It’s a beautiful relationship.

Unfortunately, this kind of attention can only be lavished on one or two students at a time. The real power of AL is that it is scalable. AL technology means that a hundred or even a thousand students at once can benefit from this same kind of individual attention and adjustment. And that’s great news for anyone who’s trying to train large groups in a short amount of time or with a limited budget.

Okay, that’s it for today, Troubleshooters. Please tune in again next week for Part 2, personalized learning 101!

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