K-12 Data Collection: You’re Probably Not Ready For AI

AI Will Boost Education, But There’s A Catch

I keep hearing about Artificial Intelligence (AI). Hundreds of articles explain that we are on the verge of something big. Soon, AI will take care of mundane tasks and help your students grow. But there’s a catch. You can’t build AI from scratch, you need to train it with data. Data about your students: their grades, demographics, and so on. Learn about the K-12 industry standards in data collection, and get one step ahead of your competitors.

Predictive AI, Not Generative AI

There are two main types of AI in education:

Generative AI Doesn’t Need Student Data

This is the most hyped AI today. It’s ChatGPT that creates text, or Midjourney that generates images. Hundreds of millions of people use these models daily, for fun or work. To generate content, you don’t need to know much about students. Students can open ChatGPT and start asking questions. ChatGPT doesn’t care about student grades or attendance.

Predictive AI Gets Trained With Specific Data

These models analyze student data and build predictions. Predictive models are not consumer-grade, they are less universal. Instead such models are specific to their task. As a result, we don’t hear about predictive AI as much.

In education, predictive AI can be very effective. With it, you can optimize operations, locate at-risk students, or work out the school budget. You can learn more about AI in K-12, and how it all works, but here’s the point. Predictive models are very efficient, but they need specific data. Without good data, AI will have nothing to work with.

Let’s see what kinds of data we can have. All the basic data is stored in the School Information System (SIS). Here are some examples of it:

• Enrollments
• Grades
• Attendance
• Rosters
• Schedules
• Demographic data
• Parent data
• Teacher and staff records
• Extracurricular activities
• Health records

These data points provide valuable insights into student performance, behavior, and demographics. Additionally, external data sources such as standardized test scores, socioeconomic data, and health records can further enhance the predictive capabilities of AI in K-12 education. This comprehensive data allows AI systems to analyze patterns and make informed predictions or recommendations for student success and personalized learning experiences.

Track Student Actions With xAPI

xAPI (Tin Can) is a protocol for tracking learning experiences. If you want to analyze learner actions, you’ll need xAPI. With it, you can track any user actions:

• When do students pause lectures?
• Which quiz questions get the lowest scores?
• What decisions do users make in learning games?

Data like this is much harder to analyze. But AI can find correlations where the human mind is helpless. If you leverage xAPI, you’ll get very powerful AI models with implicit data. These models can predict SAT scores based on how often a student is late.

Teachers can have a better picture of student engagement, even during online classes. They can’t see if a student is scrolling social media, but they can track if students pause videos or open new browser tabs.

Avoid Lawsuits: Secure Student Data

Although xAPI has security measures to protect sensitive data and ensure privacy, you will still need to stay on guard. K-12 is very regulated. When handling this much data, you can accidentally break one of hundreds of K-12 data protection laws. I’m not trying to give you legal advice, but here are some basic rules on avoiding data breaches:

• Don’t collect data
  The first rule of safe data collection is don’t collect data. Avoid personal information whenever possible. Remember, you can’t leak the data that you never store.
• Anonymize data
  You can’t avoid data collection forever. What you can do is anonymize it. Focus on collecting usage patterns rather than data about individual users. Keep only what is necessary.
• Remove unused data
  Don’t pile up old data “just in case.” If you need it for analytics, anonymize it.
• Secure personal data
  Isolate sensitive information; use separate datasets for it. Encrypt it all, at rest and in transit.
• Follow OWASP top ten
  This is a list of critical security concerns for web applications. If you’re not from the IT department, you don’t have to stuff your head with all the intricacies. Just make sure that the developers do.