Giving Compass' Take:

• From essay grading to teacher assistance, a recent panel from EdSurge opened up a discussion about the role of machine learning in schools.

• The applications of machine learning are still hit or miss (one panelist said much of current edtech "sucks"). How can those in the education sector improve such tools? And how can we ensure that all students have access to what works?

 • Here's why edtech executives need to go back to school.

Gather student data, make predictions about their learning — and perhaps their future. For years education companies have tried to apply technologies to better understand students and tailor their learning experiences, or support instructors who can intervene when human help is needed.

Today the latest buzz revolves around machine learning, which education technologists claim can support more precise tools. But there’s much more to machine learning than theory and hype. And what it takes to make these products effective, and how to boost student learning equitably and ethically, remains an ongoing debate. EdSurge re-opened the conversation recently with a group of educators and education technology entrepreneurs at a meetup in the Big Apple.

Speakers quickly contextualized the technology with the shift in how widely available data is today ...

Panelist Andrew Jones, a data scientist at Knewton, admitted that despite the hype, machine learning is still relatively limited in how it’s been applied, at least in the eyes of some users. “Most of what’s in the market now comes across as fancy homework or fancy textbooks,” said Jones. “To move beyond those labels is a much bigger challenge, one that we on the data science team worry about constantly. It’s the holy grail.”

Read the full article about machine learning in education by Sydney Johnson at EdSurge.