Perceptual Learning Method (PLM)
Concurrent Session 3
Perceptual Learning - create and experience intuitive learning. Perceptual Learning is NOT flashcards. It is much BIGGER than that. It IS a tool that allows instructors and teachers to create rich learning experiences for students. SHOW your students what a concept or idea or thing is without explaining it.
The purpose of the Perceptual Learning Method/Module is to introduce and reinforce concepts, ideas, or meaning without detailed explanation --or “any” explanation for that matter. The aim is to SHOW learners a concept, idea, or meaning and allow them to experience, see, and learn what IT is.
Largely inspired by Chapter Nine of How We Learn by Benedict Carey and backed by research and trials in Tom’s courses, the Perceptual Learning Method/Module (PLM) is a way of “learning without thinking” according to Carey. By rapidly working thru a series of images, learners receive feedback with every image they encounter. After approximately 50 minutes, significant perceptual learning will have occurred. What makes this different from flash cards is that rather than a one-to-one association, PLMs have a one-to-many association.
For example, in a PLM for surgical technology, rather than simply connecting an image of a scalpel to the correct answer “scalpel”, the PLM might include images of every conceivable type of scalpel, as well as scalpels in various contexts (during surgery, on a tray with other tools, etc.). These images would cycle through the module randomly and the learner would be required to correctly associate them to the correct answer of “scalpel” each time. All the while, random images of forceps, sponges, clamps, etc. would be cycling through the module and must also be associated with correct answers. Imagine the applications for Biology, Geography, Automotive Technology, Physics, Nursing, Chemistry, various forms of corporate learning--any form of learning in which visual learning is required.
In this session, we will introduce perceptual learning; participants will register and create accounts on the PLM site and leave with the ability to build PLMs for use at their various institutions.**
While built for use at a Northwestern Michigan College, the PLM generator could have application in any type of training which requires that learners visually identify items. It is probable that any age group with dexterity enough to make selections on a computer keyboard would stand to benefit from this tool. Learning to generate PLMs is appropriate and accessible to all audience levels--beginner through expert.
In How We Learn, Carey describes his own learning experience using a PLM to identify various schools of artistic painting styles (without prior knowledge) and achieving 80% accuracy. He also detailed a case in which fledgling ground school students used a PLM to simulate zero visibility flight (night or fog) as indicated by various indicator dials. These ground school students then performed as well as experienced pilots with 1,000 hours of flight time. In both cases, Carey and the ground school students each invested only 50 minutes. Carey expresses his opinion at the end of the chapter that perceptual learning will radically impact education. After reading the chapter, Tom Gordon was inclined to agree and scheduled a meeting with Mark DeLonge.
We realized that this wasn't sustainable and that we needed a PLM creation tool (PLM Generator) if we were going to do our part to “radically impact education.” Ideally, we would create a system with which a single user could create a high-quality PLM in a matter of hours. By this point, Jeff Straw had retired and John Velis became involved by giving his Computer Information Technology capstone project course the option to tackle our project. They agreed and built what we believe to be the world’s first and only* PLM Generator in Spring semester 2016.
The system was rather “buggy” and several of the students chose to spend the summer of 2016 on an internship to continue working on the project. At the end of summer, the internship ended though one of our interns continued through fall of 2016. A new intern finished “cleaning up” during Spring of 2017. The “cleaning up” process was quite extensive and involved sorting and correcting a good deal of troublesome coding. The final project contains its own image editor, source attribution for images, a leader board and several other bells and whistles. One user can now build a high quality PLM in hours rather than weeks.
*--So far as we can tell.
**--This is a beta-testing environment and may require patience at times.