8 Ways Machine Learning Will Improve Education

8 Ways Machine Learning Will Improve Education

Source: Getting Smart

Teacher Match Machine LearningIt was the constant stories about self-driving cars that made machine learning the breakthrough tech topic of 2015. Five years ago news stories talked about robots that could do repetitive tasks but they said real complex tasks requiring seasoned judgement–like driving a car– were years away. Then cars started driving themselves. It turns out that computers are getting smart faster than most predicted.

Machine learning, a subset of artificial intelligence, is an effort to program computers to identify patterns in data to inform algorithms that can make data-driven predictions or decisions. As we interact with computers, we’re continuously teaching them what we are like. The more data, the smarter the algorithms become.

Pedro Domingos, author of the The Master Algorithm, said machine learning is the new switchboard for HigherEd. Machine learning is the new weapon attacking cancer, climate change, and terrorism. It’s the new infrastructure for everything.

In the spring of 2014 data privacy (and over-testing) concerns rose to the forefront of the US K-12 dialog. By October more than 100 EdTech vendors had signed a data privacy pledge.

In 2015, our SmartParents series argued that data is key to personalized learning and that parents should have access to student data and should be able to decide with whom to share portions of that data–requiring policymakers to embrace personalization and privacy.

This year it became apparent that machine learning and other big data strategies are quietly improving formal and informal learning in many ways:

  1. Content analytics that organize and optimize content modules:
  1. Learning analytics that track student knowledge and recommend next steps:
  1. Dynamic scheduling matches students that need help with teachers that have time:
    • NewClassrooms uses learning analytics to schedule personalized math learning experiences.
  1. Grading systems that assess and score student responses to assessments and computer assignments at large scale, either automatically or via peer grading:
  1. Process intelligence tools analyze large amounts of structured and unstructured data, visualize workflows and identifying new opportunities:
    • BrightBytes Clarity reviews research and best practices, creates evidence-based frameworks, and provides a strength gap analysis.
    • Enterprise Resource Planning (ERP) systems like Jenzabar and IBM SPSS helps HigherEd institutions predict enrollment, improve financial aid, boost retention, and enhancing campus security.
  1. Matching teachers and schools:
  1. Predictive analytics and data mining to learn from expertise to:
  1. Lots of back office stuff:

Learning will remain highly relational for most of us, but those relationships will increasingly be informed by data. Students parents and advisors will make more decisions about learning pathways but those decisions will be nudged and guided by informed recommendations.

In the coming year, every faculty should discuss the coming impact of big data–and ask students to do the same.