In its most basic form, personalized learning refers to any training approach that is learner-centered, and it includes such methodologies as differentiated instruction, competency-based education, and blended learning, just to name a few. In the 21st century, these learner-centered training approaches have become increasingly high-tech and software-based, which is why it has garnered the attention and endorsement from figures like Bill Gates.
Currently, adaptive learning is the most high-tech and sophisticated form of personalized learning available. These programs are comprised of computer-based or web-based training environments, where every decision a learner makes is captured and considered in the context of learning theory. According to the DreamBox Learning white paper “Intelligent Adaptive Learning: An Essential Element of 21st Century Teaching and Learning,” adaptive learning programs use the learner’s decisions to guide subsequent training, adjust the path and pace of learning, and provide formative and summative data to the trainer.
Mehrdad Fatourechi concurs in the VentureBeat post, "How Machine Learning Will Fuel Huge Innovation Over the Next 5 Years.” By using intuitive algorithms and sophisticated “machine learning,” adaptive learning modules can modify the presentation of training materials in direct response to the learner’s performance, thereby meeting them at their individual level of need, explains Fatourechi.
While adaptive learning is an effective and streamlined method to deliver appropriate content and assess whether that learner has mastered the content, it may not always be the most desirable training tool because it does not require the presence or facilitation of an actual trainer. The modularized training environment can easily adapt to the learner’s level of need, but it cannot pick up on cues such as frustration, confusion, or confidence in the way that a flesh-and-blood trainer can.
Indeed, the presence of a trainer during the learning process can be beneficial in identifying where the learner seems to be mastering the content with ease—and where the learner may be struggling. This information can be useful when placing that learner in future positions or assigning tasks and duties.
What, then, is the trainer who wants to streamline the training process to do? Is there a way to incorporate high-tech training modules into a program, while still maintaining the valuable human element? Enter the embedded assessment.
Embedded assessment is the predecessor of adaptive learning, and it shares all of the same features of adaptive learning except for the high-tech element. Twenty-first century forms of embedded assessment allow trainers to assess the learner’s mastery of content through scenario-based, auto-graded assessment modules that produce data reports designed to help that trainer pinpoint potential in their learner. However, content delivery and instructional interventions are left up to the expertise of the trainer as opposed to a digital platform.
With embedded assessment, the assessment results take on a diagnostic role and become a tool as opposed to becoming part of an invisible algorithm. L&D leaders can analyze the assessment results to diagnose the learner’s challenges, analyze the implications of this diagnosis, and identify an instructional intervention to meet the learner’s needs. When an adaptive learning program adapts to a learner’s performance, the adjustments are made in a virtual “black box” that‘s invisible to the user and the adaptations are made in response to keystrokes and mouse clicks as opposed to visible signs of frustration, confusion, or confidence.
By pulling this piece of the training formula out of the virtual world and placing it back into the human world, L&D is given more authority and agency in the training process. Their subjective account of the learner’s performance during the training process can prove invaluable when making decisions about that learner’s capabilities and competency.
Finally, one of the most desirable features of adaptive learning programs is that they can be administered in a low-maintenance “plug-and-play” environment. The world of embedded assessment is no different.
There are a number of assessment providers who boast extensive libraries of modularized assessments that measure learners’ mastery of content through scenario-based, auto-graded platforms. These modularized assessments automatically generate reports that can be analyzed. These advancements in technology free up time so L&D practitioners can do what they do best: provide learners with the knowledge and skills they need to perform at the best of their ability.
Does your L&D function use personalized learning and embedded assessments? If so, share the results of your efforts in the Comments section.