Note: this post was created as an assignment for the course LDT200x Instructional Design Models. You can learn more about this course (and the Micro-Master's program it is a part of) here. This is not meant to be an exhaustive discussion of adaptive learning or traditional classroom-based or e-learning strategies!
In a more traditional learning model, students read course materials, listen to the instructor lecture over the material, and end with an assessment. Maybe, if we're getting really wild, there's a couple of videos or activities thrown in there, too.
While this type of information transmission can work for some students, it is a "one size fits all" model that can leave behind students whose knowledge, skills, or aptitudes do not match the structure of presenting the material. Typically, this type of learning model produces a "bell curve" or "standard distribution" of grades (a few As, a few Fs, with most students in the low B-C range).
Some of us may have even been encouraged (at the department or institutional level) to aim for this form of grade distribution at the end of our classes. While this may send the impression that our classes are "more challenging," it is NOT best-suited for ensuring student learning, engagement, progression, and content mastery. This is where adaptive learning comes in.
Adaptive learning is a personalized learning experience that leverages educational technologies for a student-centered approach to instruction. You can learn more about adaptive learning by clicking here.
In adaptive learning, the focus is on mastery of material (and most are self-paced). Learners must progress through the course modules, demonstrating mastery of specific learning objectives before moving on (i.e., they cannot "skip ahead"). Some students will progress quickly through the module, while others will be presented with remediation questions and content-refreshers to assist them in completing the course. One of the assumptions of adaptive learning is that students commit information to memory through practice. This is based on the theory of deliberate practice- that we learn through repetition, especially of things we struggle with; and the Ebbinghaus Forgetting Curve- that we need to be reminded of information right before we forget it in order to commit it to long-term memory. Adaptive learning applies these theories by using probes (questions that check learners' knowledge) that identify what they know, what they need to know, and what they need to be reminded of (and when to remind them of it).
The algorithm uses the following learner-driven characteristics to gather information on the student and adapt to their learning:
How long a learner spends on a question
The degree of the learners' confidence that they know/don't know the answer
Their accuracy (is the answer correct or incorrect)
The algorithm uses data to focus on areas where learners need the most assistance, insert previously-answered questions to help learners commit the information to long-term memory, and stagger question difficulty so learners do not get bored (too many easy questions in a row) or discouraged (too many difficult questions in a row).
In summation, adaptive learning differs from traditional learning primarily in the ability to offer learners a personalized experience tailored to their unique knowledge and skills. The differences look something like this:
Source: McGraw Hill
In my signature assessment for the course, I want to provide professors with the skills to assist students who struggle with reading challenging academic tests. Although a significant portion of this training (in practice) involves instructors creating activities, choosing readings tailored to their courses, and workshopping their creations with their peers, there is a degree of background knowledge in this workshop/training that would be well-suited for adaptive learning. Part of the content involves distinguishing different learning theories and identifying their applicability to course content (i.e., readings) and assessments/activities. Adaptive learning would be useful here, as the content could be broken down into multiple micro-learning objectives for each theory, (define, then build upon these skills by recognizing examples, compare and contrast theories, etc...). Some participants may have a background in these theories, which would allow them to progress quicker through the modules, while others would have the benefit of personalized learning paths that provide remediation where necessary. Utilizing adaptive learning for this section of the training would enable participants to commit the learning theories to long-term memory, as opposed to traditional learning models where they could easily skip through them or not demonstrate mastery and move quickly into the next phases of the training.
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