Benjamin Bloom is most famous for his theory describing a taxonomy of learning (which has been revised by Anderson and Krathwol (2001) as Revised Bloom’s Taxonomy). While the revised hierarchy is still applicable in a higher education setting, it is actually Bloom’s work on mastery learning that deserves more attention, especially when considering how to leverage advances in intelligent tutoring and other adaptive learning technologies.
What is Mastery Learning?
An article from 2010 by Thomas R. Guskey entitled, “Lessons of Mastery Learning,” outlines the basic components of Bloom’s 1971 work on the topic, focusing on the elements that are common to other modern theories of learning:
- Diagnostic Pre-Assessment with Preteaching
- High-Quality, Group-Based Initial Instruction
- Progress Monitoring Through Regular Formative Assessments
- High-Quality Corrective Instruction
- Second, Parallel Formative Assessments
- Enrichment or Extension Activities
- Sustaining and Extending Success
The use of machine-aided instruction has been a polarizing topic from Sidney Pressey’s Automatic Teacher to B.F. Skinner’s Teaching Machine to the current projects such as Knewton, ALEKS, and Carnegie Mellon’s Open Learning Initiative.
Audrey Watters is a blogger who has spent a good deal of time observing and analyzing the work done in this area. Her posts represent a level-headed introduction to the concepts:
- What Should School Leaders Know About Adaptive Learning?
- The Automatic Teacher
- The First Teaching Machines
- Moving Beyond Personalized Instruction
- Teaching Machines and Turing Machines: The History of the Future of Labor and Learning,
- Learning Networks, Not Teaching Machines
So how do we sort the wheat from the chaff here? Too gullible an approach and you end up with paying for the jet fuel for the CEO of an ed-tech startup as the primary outcome of an expensive endeavor, while too cynical and you end up standing on the side of the road, watching all of the traffic go by.
Let’s get a better understanding of what these tools offer, as well as a better understanding of the problems we hope they will address. Then we can begin the process of experimenting and improving.