"We use models like this in social gaming to understand motivation. We use them in social networks to recommend people you may know, books you might like. This is the kind of personalization that we could use in education."
Schoettler begins his talk by explaining his frustration with how the United States collects educational data, mentioning that data is only processed two years after it is collected. That is, a chart made in 2011 shows data only up to 2009.
This means that the United States is not using feedback to iterate and make improvements in the education system. This means education isn't improving.
To improve education, it's important to know which data to measure, and the most important measure is the student. We currently measure students by skills and understanding, and don't account for many other elements of the learner's style and background.
One major study concluded that student achievement can be doubled by an increase in feedback, using formative assessments and tailoring instruction to each student's needs.
Clearly, not every teacher can do this well. Ideally, technology could be used to implement these changes. Big data could enable personal learning programs. These tools currently exist, and there could be better personalization and improved results in a scalable system that could improve education throughout the United States.