ML vs Human ModelA common area for learning analytics is to look for ways to support struggling students.  Largely, the focus has been on how to identify students of concern.  In a free and open report, a collaboration of researchers from major Universities have outlined exactly that: how to identify students who may not graduate high school.

Here are the amazing findings of a dataset of 11,000 students:

  • Machine Learning far outperformed the human method for identifying at risk students
  • The Predictive ML Model only got more precise as time when on (and more data collected) as opposed to the human method that became less precise.
  • The ML models aims to rank students by order of predicted need for intervention and to also determine when the intervention is needed
  • The ML model also aims to predict the likely success of a given intervention.

Powerful stuff!  It’s a quick and worthy read for any Education Data aficionado.

Find the entire report here

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