The idea for the current project emerged from a workshop with our advisory board of neuropsychologists. We discussed how to adapt textbook content to support different cognitive styles. We expected them to suggest a selection of tests, but they suggested a different approach. As we have unique access to data, they challenged us to use this data to identify learning enhancements.
The theories behind them limit the quality of learning style assessments. We can get around these theories because of our unique access to data. By using machine learning, our adaptive books may collect user data from thousands of students every week. We have a rare opportunity to redefine the practice of learning style assessments. It is also an opportunity to use machine learning to a more meaningful purpose than just increasing sales. Instead of utilizing personal data for marketing and sales, we help individuals grow through increased knowledge.
To stimulate the entire learning process, we must further develop abooks so that they can also be used for active experimentation and decision support in the field of practice. The solution to this is the integration of immersive learning in abooks. VR (Virtual Reality) and AR (Augmented Reality) are examples of technologies supporting immersive learning. However, VR is too resource-intensive to meet our requirements to make knowledge attainable. AR, however, is available on regular smartphones and can be used without additional investment.
Both VR and AR are specific solutions that work well in some areas but do not work in others. Our adaptive technology makes it possible to use immersive learning when appropriate, and other learning resources when it works better.