Just about every program describes itself as "adaptive," but precisely how they adapt to a student frequently differs.
Broadly speaking, an "adaptive" program offers different content to learners, based on an assessment of what they seem to know.
Some programs "adapt" based on the results of an assessment at the end of a unit. A student might take a quiz, for instance, and depending on how the student scores on that quiz, the program may move the student ahead or reset the learner's path so that he or she has an opportunity to review (or relearn) a supporting skill.
Other programs monitor the responses that a student provides while he or she is moving through the program. These programs might monitor a host of parameters: how long does the student take to answer a question? How many "hints" does the learner need before scoring a correct answer? Rather than waiting until the "end" of a lesson, the program will continually remap the learner's path even as they are working on concepts.
Relevant questions to understand how "adaptive" a program is would include:
- At what point in the learning process does the program evaluate the student?
- How many points does the program evaluate the student's work?
- Does the program have an algorithm for weighing different factors? Does the program (or company) share that algorithm or is it a "secret sauce"?
One consequence, of such "adaptation," is that it can be challenging for a teacher to "interrupt" a student and redirect (or "assign") a specific unit. The program instead will deliver each unit when it has judged the student has mastered the preceding topics.
Adaptive learning tools need copious amounts of data about user actions to do a good job of plotting an optimal learning path for students.
A contrasting idea is whether a program is considered "assignable," namely that a teacher can redirect a student to work on a particular section, independent of whether the student has completed other sections of work in that program.