I see MOOCs as an answer to this problem and so part of my research focuses on ways we can improve the validity of MOOCs. My current work focuses on understanding how students use edX in order to increase the validity of the learning process in MOOCs. More specifically, I employ a variety of machine learning techniques and algorithms to identify students who use technological exploits to attain certificates. By ensuring that earning a certificate in MOOCs is legitimately achieved, we can validate the worth of MOOCs and its global acceptance. In the most idealistic viewpoint, if my research can promote the validity of MOOC certifications, we step another step closer to the democratization of education.
Success (and plenty of failure), really comes down to a set of basic fundamental mantras that you choose to live by. These are mine.:
As a current PhD candidate in Computer Science at MIT, I use machine learning to make advanced education more accessible. Using new algorithms for noisy supervised learning (many mislabeled training examples) and educational data mining technologies, I work with edX student data to (1) infer user-intent across terabytes of noisy, large interaction datasets and (2) implement prediction, inference, and detection algorithms distributed across 200+ MITx and HarvardX MOOC courses. Put simply, I research ways to improve online courses via the design and implementation of machine learning and data analytics algorithms.
Growing up in rural Kentucky, my peers and I encountered a social glass ceiling of limited human expertise and monetary resources, in juxtaposition with the educational resources of students in more prosperous areas. Fueled by this experience, I am motivated to pursue research that mitigates this glass ceiling for all students. Everyone deserves access to quality educational resources.
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