By Phil Hill
Josh Kim wrote three predictions at Inside Higher Ed for the EDUCAUSE 2013 conference, and I particularly agree with the basis of #2:
Prediction 2: Adaptive Learning Platforms Will Be the Toast of the Party
Everyone will want to talk to Knewton. The ASU / Pearson / Knewton partnership is a huge deal. Knewton has the technology, relationships, funding, and management team to make a huge impact.
I’ll be looking at EDUCAUSE at the other adaptive learning players. Where are they focusing their platform work? What deals and relationships do they currently have? How big is their market penetration? What is the quality of their leadership team and employees they have a EDUCAUSE?
I’m betting we will see at least one major adaptive learning vendor announcement. A purchase, a big collaboration deal, or a new huge round of funding.
I also expect much of the discussion this year to be on adaptive learning. But one risk of this zeitgeist (if it comes to pass) is that terminology becomes fuzzy and often devoid of meaning. Hey, get your adaptive here. You want to be adaptive, don’t you? We are the adaptive makers… and we are the dreamers of dreams.
What does adaptive learning mean? I don’t believe anyone can thoroughly describe all the concepts accurately and thoroughly in one place, but I did see a video from Knewton that is helpful. They have a “Knerds on the Board” blog series that includes various Knewton staff giving short video explanations of key concepts. In a recent post, Jess Nepom described the differences between differentiated, personalized Learning and adaptive learning, which I have paraphrased below.
- Differentiated Learning describes the case where there are different pathways that students can take within a learning environment, typically organized as pre-set categories.
- Personalized Learning describes the case where there is a different pathway for each individual student, often implemented in a rules-based method with a decision tree. Students might take a diagnostic test on the first day that will be fed into a rules engine to lay out that individual’s path and content.
- Adaptive Learning is data-driven and continually takes data from students and adapts their learning pathway to “change and improve over time for each student”.
In Knewton’s world, these three are steps towards the ideal – Differentiated is step 1, Personalized is step 2, and Adaptive is step 3. I suspect that many other platform vendors share this view of the world.
While this video is helpful for basic clarity on adaptive learning and related concepts, it makes the implicit assumption that the machine should do the selection of learning pathways for the students. Algorithms relying on big data are the way to go. But this is only one version of how to effectively design learning around the student.
Another approach is to empower the student to select their own learning pathway as either a pre-set category (described above as differentiated learning) or even to create their own pathway that adjusts over time based on the learning process and interactions with other learnings. This gets close to the Connectivism model behind cMOOCs.
It will be interesting to see if the various vendor demos and conference sessions include descriptions of what is meant by differentiated, personalized or adaptive learning, and if presenters describe the key issue of who selects the pathway – the instructor, the student, or the machine.