Systems for Feedback and Novelty in Deep Learning: A Case Study From Coursera
As industries and occupations are being transformed by AI and other emerging technologies, there is a growing need to retrain existing workers and ensure those entering the labor force have the requisite skills to succeed in the new job market. At Coursera our mission is to provide everyone, anywhere access to the high quality education that will become necessary for this. Scaling in course help that assists learners through difficult course material, keeping them motivated, will require high levels of personalization to make the interventions relevant and timely. Through our work we have created automated feedback loops that utilize deep learning to serve the optimal help messages in our courses at the right time to drive positive learner outcomes. These learnings are relevant to anyone looking to add product value through automated systems powered by deep learning.
Vinod Bakthavachalam is a data scientist working with the Content Strategy and Enterprise teams where his work has recently focused on developing ways to measure the learning outcomes from taking Coursera classes, especially in the context of company sponsored training. Prior to Coursera, he got triple Bachelors degrees from UC Berkeley in Economics, Statistics, and Molecular and Cell Biology, and his Masters degree in Statistics from Stanford.