Everyone Learns At Their Own Pace
Each individual learns at their own rhythm and according to their own acquisition process. Adapting one's teaching methods to each learner is a major challenge for teachers and trainers. In terms of eLearning, this personalization is all the more important as we target a larger number of students: this is the whole point of adaptive learning, a notion that everyone is talking about... But, how to actually implement this?
What Is Adaptive Learning?
Adaptive learning can be defined as a teaching process that adapts automatically to each student or learner, meaning that the courses, the assessments, or the teaching resources are used to tailor the course to each individual’s progress, abilities, and even choices. The goal is of course to allow each individual to learn efficiently and to optimize the learning courses.
Adaptive learning is based on automated mechanisms that can only be enabled by strong digital learning tools. We can distinguish two components of adaptive learning: diagnosis, and the consequences. The diagnosis provides the information—"the data"—on which the consequences and its personalization are based for the learner.
A Necessarily Multidimensional Diagnosis
For the most part, the diagnosis is based on assessments that must be integrated at several stages in the eLearning process. Fundamentally, an assessment should not produce only a score, because a single score provides too little information to be able to make truly personalized analysis and decisions. Admittedly, one could use simple rules such as, "if the score obtained is lower than such a threshold, then direct the learner towards such an activity, which is easier." This is indeed a form of adaptivity, but it is insufficient, too primitive, and it does not focus on helping the learner improve in the area where the knowledge is lacking.
For more elaborate processes, the assessment must be able to produce a multitude of scores, corresponding to as many axes or dimensions that there are of the knowledge. For this, it is necessary to bring in the notions of domains and tags. We can associate each question with one of the domains that structure the theme, so that the result obtained for this question feeds a score per domain. Similarly, each question can be associated with a set of tags, labels characterizing the knowledge associated with the question. Thus, the answer given by the learner will feed a whole set of scores per tag. At the end of an assessment, we therefore have a global score, and a set of scores by domains and scores by tags, which together constitute complete, objective, and precise diagnostic elements, from which we can make precise and tailored decisions.
Tailored Assessments For Adaptive Learning
Let us mention another aspect of personalization, in terms of evaluation: the dynamic selection of questions. Assessments can be used as a strong and efficient way to improve training and learning. This notion concerns the conception of a questionnaire whose questions are chosen dynamically, according to the answers previously provided by the user, so as to optimize their learning curve.
Once a concept has been learned and confirmed, there is no need to go back to it; on the contrary, we will submit questions relating to the notions identified as not acquired. Dynamic selection of questions can be used within an eLearning course to train the learners, but also when using a microlearning approach to tailor the questions sent to each individual.
Personalization Decisions And Actions
Let's now look at the personalization aspect, that is to say the decision-making, based on the diagnosis, as to the conduct of the course. As stated before, adaptive learning must rely on a strong digital learning solution. The platform must rely on a system allowing the teacher to set conditions with multiple levels of complexity (based on analysis), in order to respond to different use case scenarios.
The actions that must be possible include submitting another assessment, recommending another eLearning module, subscribing the user to a group, assigning the user a badge, validating a skill or ability, and sending an email to a trainer or tutor. This is how we can define the rules that govern the personalization of the course.
Adapted And Optimized Learning
These powerful features may seem sophisticated, but they are simple to implement. The benefit, for the learners, is considerable: it is the guarantee of adapted and optimized learning, adjusting the progression, avoiding irrelevant repetitions, and giving everyone the time that suits them.
First published on elearningindustry.com