Super-fast learning: it’s a thing

With information flying at us faster and faster, wouldn’t it be great to learn faster? Turns out, it could depend on content filtering (to remove the things you’ve already learned), intelligent tutoring (meeting your needs, based on past performance) and gaming (to keep you interested). CySET is designed to use these concepts to accelerate learning in the specific area of cyber security. In the US, there are many open cyber security positions, so getting and keeping employees up to speed as quickly as possible is critical.

Accelerated training and customized feedback

Project Details

Proposal Title:
CySET: Cyber Security Education and Training
National Science Foundation
Contract Number:
Start Date:

This project was designed to explore a cost-effective, templated approach to knowledge management that can be leveraged to customize cyber security content for specific user context. Through a research partnership, it incorporated an experimental study of the effects of content filtering and intelligent tutoring on learning performance and speed to lesson completion. These concepts were incorporated into a prototype cyber security training lesson.

How we did it

The CySET innovation was an exploration of several areas that can be integrated to accelerate learning. These areas included content filtering based on recognition of mastery, increasing the speed of progress by filtering out knowledge that was already mastered. It also includes intelligent tutoring based on the types of errors made on assessments; and enhanced engagement through the inclusion of gaming scenarios.

This solution is an intersection of empirical knowledge and technological expertise. The project was one of the first to experimentally assess the individual and combined impacts of content filtering, intelligent tutoring and physiologically-measured, gaming-induced learner engagement on speed to mastery. Technologically, the project enhanced the functionality of a learning content authoring, delivery, and management system through the development of: 1) a predictive learner modeling subsystem running on a portable, extensible toolset that supports content filtering and intelligent tutoring, and 2) configuration parameters for a scenario-based gaming engine. While designed for improving cyber security education, it can be applied to any realm that includes constantly evolving learning needs.