Teachers have long recognized that their students learn at different rates and in different ways. Recall the frustration of a gifted student who had learned a concept through their own reading, needing to curtail their enthusiasm during a particular lesson while the rest of the class caught up. Or the contrary: the struggling student, who may have missed a class in a previous grade, and does not have a foundation skill with which to build comprehension of the new lesson.

In the hurly-burly of a class, the subtleties of who is chomping at the bit to learn more and who is struggling may not always be that obvious. Students will seldom put their hands up and self-identify as being “bored” or “struggling”. In this way, critical opportune moments to intervene and adjust a lesson plan are missed, and with it the chance to bolster a student’s learning curve.

Adaptive learning is an ambitious objective of advanced pedagogical ideas and systems and describes the ability of a learning system (in most cases digital) to adapt to the learning differences across individual students, as well as to adapt to changes within the learning trajectory of individuals. Adaptive learning systems are part of quality learning management systems and respond to subtle changes in a student’s comprehension of a subject.

The promise of adaptive technology cuts to the heart of burning pedagogic questions – some as old as the formal educational system itself:

• Is there a better way to frame concepts for students other than textbooks?
• What kind of change in curricula will bolster student engagement, and by extension motivate self-driven learning?
• Can changing the structure, style and sequence of a lesson increase uptake and comprehension?

Many LMS packages offer an adaptive learning component, however, all are not created equal. Adaptive learning technology requires an enormous amount of planning and back-end engineering to create a system that is user-friendly and effective.

Let’s review the characteristics of an effective adaptive learning system

1. Big Data

The starting point is data. Beyond normal assumptions based on age and grades, adaptive learning systems should feed teachers with a mine of other useful data that reflects how individual students are responding to various lessons and learning content. By adding an LMS component to their curriculum, empowered with adaptive learning technologies, teachers are able to adopt real-world changes in class, assignment design and grading to accommodate the subtle differences in the learning profiles of their students.

2. Detailed design

LMSs that endeavour to offer an adaptive learning component should be exceptionally well-designed and take into consideration the many permutations of the content, based on the student’s interaction with it. It is almost like writing one of those open-ended stories where the reader chooses the actions of the hero at every turn. The content must be designed in such a way that an almost protean number of options, and paths, are integrated into every lesson.

3. Quick Adaptation

The degree of adaptability cannot be superficial, offering broad adjustments based on a few variables. It must be finely tuned so that every choice the student makes on their path through the material is mirrored by a complex back-end system that is continuously changing the student’s experience based on the way they are completing the tasks. In addition, the adaptations must feel seamless to the user (student), and their experience must remain positive, reinforcing comprehension and learning regardless of the times they may need to repeat something or notionally fail at a task.

4. Feedback Mechanism

A quality adaptive learning system will have varied feedback mechanisms that alert students to an error, and guide them – based on the nature of the error – back to a previous point in the lesson, or offer hints and tips as to how to resolve the current task. Constructing and designing these feedback mechanisms, which can also take a game-based approach, is crucial in retaining a tonality of encouragement and maintaining student interest.

5. Artificial Intelligence

A well-engineered adaptive learning system has the ability “read” a student’s interactions, and make the decision to change the sequence of tasks and lessons accordingly. The system will be pre-loaded with a number of variables that allow it to decide to switch up a sequence of content, based on whether the student’s responses denote a difficulty. By the same measure the system should be able to identify swift comprehension and completion of tasks, and gear up the lesson to accommodate gifted, or interested students.

Conclusion

Adaptive learning systems remain a “hot potato” among schools, only few schools have so far adopted adaptive learning technologies, with many school boards and parents quoting privacy concerns and financial constraints as limiting factors.
Empowered by technology, content and learning systems continue to evolve. As with every evolution, those that have a strong student-centred focus, based on resolving real-world educational challenges will survive.