We take in information all the time. Most of it never reaches consciousness. All of it affects us. We’re also constantly expressing ourselves. Some of this expression is conscious—speech, voluntary movement, eye tracking and focus, etc.—the rest is unconscious—micro facial expressions, non verbal sounds, involuntary movements, etc. (Of course, the line between conscious and unconscious—let’s call this intrapersonal awareness—differs from person to person and depends on time of day/year and environment).

At Transport Learning, we’ve applied this understanding to users. We understand that users affect and are affected by their environments. We understand that performance on one task isn’t necessarily consistent over time, among environments, and/or emotional states. We understand that learning—long-term retention and comprehension—is dependent upon much more than content.

We think there’s a sweet spot: ample ability and desire to focus uplifted with encouragement, motivated with challenge, educated with feedback, and maintained through forgiveness.

The learning system presents the stimuli to ensure that each user achieves and maintains this sweet spot.

How? We correlate conscious and unconscious stimuli through time, map the patterns (facial features vs content changes, ambient noise vs focus, pupil size vs. accuracy, etc.), predict behavior and iterate. It’s truly adaptive learning.

Importantly, this system can be stand-alone and is constantly retrained. This means that both on and off-line the system adjusts to and betters the learning of each user. Additionally, the rules/logic of the system are not predetermined, but rather self-generated: there are no pre-determined decision trees or probability models. Rather, the system autogenerates the learning patterns of each user and chooses the most appropriate content (note the content is processed/tagged semantically, thematically and visually).

What’s this mean? It means that all types of learning styles and difficulties are addressed: the student who is two standard deviations above the ‘norm’ is served just as well as the student two standard deviations below. It also  means that we have a tool that allows us to understand which educational materials are best for each learner or group of learners and therefore helps us create new, learning style specific materials in the future.