Adaptive Learning
Adaptive learning is an instructional design method that adapts to the tendencies and patterns of learners, often through the use of technology. This allows for education to be more personalized to the individual learner, providing extra help and review of areas a learner hasn’t fully mastered yet and enabling learners to dive deeper or further pursue topics of interest in which they excel.
It is possible for teachers and educators to design adaptive learning education without technology but, as Matt Haldeman from McGraw Hill explained (e-Literate TV, 2016), that can be difficult and time-consuming. He was typically spending an additional four hours a day after the school day ended analyzing previous lessons and homework assignments to tailor the next day’s lesson to his students at different levels of mastery. This is commendable but not sustainable, and not even feasible for many educators.
Utilizing technology to synthesize learner data from responses, scores, and patterns of engagement is a much more efficient and sustainable way to create adaptive learning education. Besides the fact that computers can process data more quickly than humans, another benefit arises when a larger learner pool is using the same educational technology because there is more data to draw from to make more accurate predictions for things like recommended learning paths and anticipated areas of difficulty.
Theory in Action: Practical Application Example
An adaptive learning module I would include to support my signature assignment would be to have learners commit to using a meditation app for 30 days, such as Calm, Oak, Breathwrk, Headspace, or similar . One of my signature assignment assessments is for learners to respond to a survey 30 days after they take my training, so I would include some survey questions to get feedback on their experience with using their chosen meditation app and see if they benefitted from it and would continue to use it.
I would not design my own module from scratch because, as discussed above, this can be incredibly time- and resource-demanding for individual instructional designers and there are incredible benefits to utilizing adaptive learning content/technology created by experts that can be continuously improved on by extrapolating data from a larger learner pool.
The meditation apps I listed are great examples of adaptive learning because they follow the four theories that optimize adaptive learning, that Christina Yu (LDT 100x, 2017) covers in this week’s lecture:
- Deliberate Practice: These apps are built on the idea of deliberate practice. Learners are offered bite-sized, focused, and intentional lessons that are presented in a logical order that build on the previous lessons as the learners' skills develop. More difficult lessons and concepts need to be unlocked by completing prerequisite lessons and skills.
- Ebbinghaus Forgetting Curve: The apps encourage repetition to automaticity by sending daily notifications to complete the next lesson and by tracking the learner’s streak, or how many days in a row the learner returns to the app and completes a lesson.
- Metacognition: The apps show learners all of the skills and concepts they can learn from the app, and clearly highlight which ones they have completed and which ones they have yet to learn. This helps learners see what they know and don’t know, and how they are progressing.
- Fun and Gamification: All the apps present learners with badges as they unlock accomplishments and rise in skill level. Some apps offer ways to share these badges with friends who also use the app or through social media, and some also show the learner’s rank in a leaderboard of other app users. Having learners progress through learning paths of increasing difficulty also plays into the fun and gamification theory, similar to the deliberate practice theory.
These meditation apps are great adaptive learning content that already exists, and would be much better than anything I could design myself. On top of following the four theories, these apps continuously tailor themselves to the learners’ needs by encouraging them to progress as they successfully complete lessons and by allowing learners to repeat or revisit concepts they struggle with. The added personalization elements (such as selecting a meditation guide and voice, and choosing accompanying background sounds like rain or wind) also keep learners motivated and engaged by making them feel like their learning was designed specifically for them.
References
e-Literate TV. (2016, November 4). McGraw-Hill Education on Adaptive
Learning and Teaching Practices [Video]. YouTube.
https://youtu.be/gSqoc6Y_4NoLDT 100x. (2017, August 7). Christina Yu Q7 [Video]. YouTube.
https://youtu.be/qBH1OunYtpo
