QUICK Comments

QUICK-Comments will rely on machine learning and natural language processing (NLP) techniques to suggest responses for teachers to send to their students. This will work in a manner similar to Google Smart Reply, which provides Gmail users with three suggested responses they can send without having to craft their own message. 

The first step in creating the tool will be to collect actual teachers’ comments that we can use as a training set for machine learning. We have already received grant funding to pay a small cohort of teachers to read their students' open-ended responses and write comments. Once this has occurred, the team will apply machine learning in order to anticipate the correct response for future student writing. 

A second group of teachers will be able to choose from three comments; some choices will be teacher-created, while others will be our machine learned sequence-to-sequence generated comments. We call this process the Active Machine Learning Paradigm, as each night we will refit our machine learned models to try to use all the data we gather each day. We will not only learn which suggestions teachers pick, but we can learn a lot each time a teacher decides not to send any of our three suggested comments.