The Beginner’s Guide To Learning Analytics

We haven’t talked about learning analytics in awhile (at least not specifically), but that doesn’t mean learning analytics have missed the mark (last year, learning analytics were tagged as a ‘top trend’ in education). We talk quite often about blended learning and personalized learning, often times neglecting to consider that learning analytics are what really drives these concepts: without it, we’re just shooting in the dark (or dusk, really, since not everything is data!).

The handy infographic below takes a look at exactly how personalized education is being driven by learning analytics, and more specifically, how learning analytics can take us from ‘traditional’ to ‘personalized’ education. Keep reading to learn more!

Learning Analytics Bring Personalized Education

  • Traditional education brings students to the ‘finish line’ on the same path at the same pace, regardless if this is too fast for some or too slow for others
  • Personalized education students work in their own way at their own pace. They may get additional instruction where and when they need it, or they may move along in their own way.
  • Learning analytics can help to optimize the learning process for all students by measuring, collecting, analyzing and reporting the information about students and their environment that is relevant (grades, responses, difficulty of questions, etc)
  • Learning analytics drives learning in a cyclical pattern: Data is collected, analyzed, and interventions are made based on the data. After interventions, more data is collected and analyzed, and additional (perhaps different) interventions are made. Lather, rinse, repeat.

Four Levels of Learning Analytics

  • Descriptive: What has happened?
  • Diagnostic: Why did it happen?
  • Predictive: What will happen?
  • Prescriptive: What should I do?

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