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:
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.