By Jose Ferreira, Knewton founder & CEO
Big data in education is a hot topic, and getting hotter. Proponents tout its potential for reform. Detractors raise privacy concerns. Skeptics don’t see the point of it all.
Few people seem to have a clear understanding of what big data in education means, its scope, what will inevitably result, or even the differences between fundamental types of data. The responsibility for clarifying and communicating this understanding starts with the organizations building data platforms or applications.
Take a recent example. The Gates-funded initiative inBloom recently received scathing critiques that it would share confidential information without parental permission, along with other security concerns. InBloom’s mistake, in my opinion, is that it holds personally identifiable information (PII) but didn’t communicate a transparent payoff to users. For an education company to get big data right, it needs to be on the opposite side of both of those issues: avoid holding unnecessary PII and communicate clearly how its service makes transparent good use of users’ data.
(For the record: Knewton doesn’t hold any PII unless a user is able to consent and wants us to use the information for a specific reason: to create a private learning profile that can be carried by that user from app to app.)
Education has always had the capacity to produce a tremendous amount of data, more than maybe any other industry. First, academic study requires many hours of schoolwork and homework, 5+ days per week, for years. These extended interactions with materials produce a huge quantity of information. Second, education content is tailor-made for big data, generating cascade effects of insights thanks to the high correlation between concepts.
Only recently have advances in technology and data science made it possible to unlock these vast data sets. The benefits range from more effective self-paced learning to tools that enable instructors to pinpoint interventions, create productive peer groups, and free up class time for creativity and problem solving.
At Knewton, we divide educational data into five types: one pertaining to student identity and onboarding, and four student activity-based data sets that have the potential to improve learning outcomes. They’re listed below in order of how difficult they are to attain.
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