When using instructional approach directed toward mastery learning, context is key to achieving superior results. In particular, individualized instruction is key for building strong foundations of knowledge and skill.
Educational technology is broadening the possible applications of mastery learning. For instance, mastery learning has always tended to get better results with smaller groups of students. Now, personalized and adaptive learning technologies are making it feasible to implement mastery learning in larger class sizes.
Mastery learning requires students to master each component unit within a course, before moving on to the next level of learning. Derived from the work of Harold Bloom, mastery learning relies on small learning units, regular assessment, and remediation throughout each unit.
Instead of progressing through a course at a fixed pace, students progress at their own pace. In principle, students build a stronger foundation of learning at each level, leading to better performance over the long term.
In a personalized online course, frequent assessment and remediation (branching scenarios) can be built into the course to satisfy the mastery requirements.
To ensure that a personalized or adaptive course meets mastery learning standards, the concept of a passing grade needs to be modified accordingly. Generally, mastery requires a minimum 80% assessment score to move the learner to the next lesson level.
Looking at other contexts where mastery learning is most effective:
One big advantage of mastery learning is the impact on retaining learner participation from start to finish of a course. The impact can be substantial for courses that continually build on prior knowledge, such as in mathematics.
This video takes a look at an approach using mastery learning in a flipped classroom:
The internet provides us with a wealth of resources for developing improved methods of learning. This includes better ways to assimilate information, and also ways to make use of our learning.
"Transfer learning" is a term that is becoming increasingly popular, primarily due to its role in machine learning. For a technical overview of transfer learning, visit this article by Karl Weiss et al. in the Journal of Big Data: A survey of transfer learning.
The term is starting to show up more frequently in relation to human learning as well. Michael Simmons takes a look at one of the most creative minds in business today, and seeks to explain--How Elon Musk learns faster and better than everyone else.
His answer? First, Musk is an exceptional worker and student. But more important is his drive to learn across the spectrum of disciplines.
Musk is also good at a very specific type of learning that most others aren’t even aware of — learning transfer.
Simmons goes on to say that Musk's genius lies in his ability to deconstruct knowledge into fundamental principles, then reconstruct those principles in other fields.
Looking at possible ways to apply transfer of learning concepts into educational curriculum, Larry Ferlazzo provides us with five strategies in the video below. Mr Ferlazzo does a great job of deconstructing the fundamental principles of learning transfer, and then gives us some practical ideas for student exercises.
Shifting directions a bit, here is an interesting approach to memorization. Mark Shead deconstructs a fundamental principle of the memorization process:
The act of reading something you want to memorize fires different connections than the act of recalling. This is how you learn to memorize–your practice recalling, not repeating. This means that simply reading a particular piece of text over and over again is going to be the long road to memorization. You need to let your brain practice recalling the data so it can strengthen the same pathways that will fire when you need to remember the information later on.
Shead provides a memorization process that is easy to learn, and probably effective for most people. Give it a try!