Personalized learning is all over the educational landscape these days, even though nobody can offer a clear and consistent explanation for what it might be. The field encompasses everyone from teachers designing more effective methods to businesses with a new edu-product to sell. Assuming for the moment that there is no solid, universal definition, let's consider the different aspects of instruction that could be involved when someone is pitching personalized learning.
Personalized learning can refer simply to pace. All students cover the same materials in the same order, moving at whatever speed seems to best suit them. If you're old enough to remember doing SRA reading exercises out of the box in your elementary classroom, you have experienced this type of personalization.
A more extreme version of pace. Some versions of personalization involve flexible time, with the student allowed as much (or as little) time as is required for them to show mastery of that particular unit. This often requires changes to the traditional rules in order to accommodate students with wildly different, so that Pat's school year may be less than 180 days and Chris's might be more. This could mean that Pat could finish high school by age 15 while Pat was still there at age 20. Nobody has really addressed how to handle this, yet.
The personalization may refer to the content used to deliver the lesson. For instance, everyone in the class may be working on reading for context clues, but Pat gets a reading selection about dinosaurs and Chris gets one about opera, because those are things that Pat and Chris care about. This will require a large library of materials.
Netflix is one of many companies that has had success in personalizing pitches. In other words, they take a chunk of content, and they create tailored trailers that are aimed at particular groups. That's how you end up with a trailer for Lost In Spaceaimed at Canadians who like comedies. In education terms, this will come out as "we will find the ways to tap into student motivation" aka "we will make this lesson appear to be about something that interests the students." But all the students will still get the same lesson.
Chris and Pat take a pre-test about parts of speech. Pat does poorly with adverbs and Chris does poorly with pronouns, so for their next assignment, Chris and Pat get different worksheets. Each gets one geared to the weaknesses they displayed on the previous test. Again, a large and varied bank of materials will be needed.
The most important thing to know about learning styles is that the whole concept has been repeatedly debunked. Nevertheless, you may find personalization based on this popular but discredited theory. So for a unit about the Civil War, Pat may be assigned a chapter of reading, while Chris is told to watch an instructional video. Be prepared for complaints about how someone got the "easy" assignment.
The idea here is to use a mode that best allows the student to display her level of achievement. That might be an objective test or an essay or some sort of project. It may include more than one attempt in more than one mode. Does such a system allow us to consider Pat and Chris's grades comparable? Nobody has really answered this.
One distinguishing feature of different personalization models is the degree of student choice. In a model that's strictly about pace, the student really has no choice except when to move ahead. Other models may give a student a choice of columns A, B or C. The extreme version would be a system that allows the student to make all the choices-- what will be studied, how it will be studied, and how the student will ultimately be assessed.
Everything we've discussed so far could be (and often is, because teachers have been personalizing instruction since the invention of dirt) handled by a human teacher. But much of the recent push for personalization comes from the edtech world, where there's a belief that A) computer software can handle many of the complex tasks involved and B) there is money to be made selling that software. The software may be billed as Artificial Intelligence, claiming that it can "learn" the student's style and strengths and therefor generate just the right materials. There are many issues to consider with computerized delivery-by-algorithm, not the least of which is having your educational experience designed and written by software engineers.
Edtech folks like to talk about personalization as anytime, anywhere learning. If all the learning and the assessment of mastery is done via computer, then it could happen any place that the student can hook up to the internet. The issue here becomes the monitoring of these various learning events. Who decides whether or not helping pick up trash earns a student a micro-credential in environmental science.
Questions to ask.
A personalized learning system can include any or all of these features, and yet few come with clear explanations of which features are involved and how they are managed. Personalized learning advocates have generally steered away from discussing the delivery aspect, perhaps because "Let a computer teach your child," is not a great sales pitch. Pitches are also often vague about just how deep and wide their library of materials is; it's worth asking whether the personalized materials are being newly generated or simply plucked from a pre-existing bank of materials, and how large that bank is.
Another good line of inquiry is to ask about the outliers. If you have a student who is socially withdrawn, low-achieving, very interested in Edwardian England, tends to work slowly, but has a very large vocabulary and excellent reading skills, will the program really deliver personalized lessons for that student, will it only come close, or will the student just get basically the same lessons as the rest of the class. In other words, how broad a spectrum of personalization can this system really cover?
Finally, make no assumptions. "Personalized learning" can be a legitimate descriptive educational term, but these days it is just as likely to be used as a marketing term, and like any good marketing term, it is used to encourage the customer to make assumptions about the product that may or may not be related to reality. Don't assume that just because your idea of personalized learning includes a certain feature, so does someone else's. Ask.