Thanks for the question buss! Duplication and measurement of learning strategies comes from a few different metrics, with a combination of data and meta data.

Thanks for the question buss! Duplication and measurement of learning strategies comes from a few different metrics, with a combination of data and meta data. Mainly being individual student metrics (grades, attendance, assignments turned in on time, ect). We then use a training group of assignment variables that teachers can include into there class structure. (group assignments, instead of asking students to make flash cards give them flash cards to study with, timed in class assignments, ect). Its using additional variables to measure stability, failure likelihood, and projected difficulty levels. Our goal is within the first 6 months after launch we can provide the teachers with tools to create assignment convertors based on learning styles. We hope to improve on indv student grades by getting micro with it and measuring the output variables. (difficulty, stability, indv student metrics(grades)). Teacher feedback is macro class view, with micro error markers for "problem students." Our AI will be using predictive analytics to project future assignment scores and the aggregate assignment connection weight. Tailoring learning style to the testing group mean (class). Pointing to problem areas for the class that are critical for future assignments/quizes/tests.