We are interested in how the data can inform the best practices of MOOCs - for the teacher and the student.

We are investigating ways to improve delivery of online education with new technologies and tools. With the rise of MOOCs, there is a wealth of behavioral data available that captures how students learn online. Our goal is to develop scalable machine learning algorithms to mine this data to answer questions such as:

  1. What resources are most helpful in gaining knowledge?
  2. Do students learn in different ways or styles?
  3. What is the optimum teaching rate?
  4. Why do students drop out?

MOOC data comes in many forms (forums, quizzes, video, etc) and different models (dynamic bayesian networks, hidden markov models, etc) are needed to answer questions about online learning.

More information to be posted soon

Reports:

  1. MOOCdb Technical Report (PDF)
  2. MOOCEnImages: Examples of analytics based on MOOCdb for 6.002x: Circuits and Electronics (Spring 2012) (PDF)
  3. MOOCEnImages: Examples of analytics based on MOOCdb for 6.002x: Circuits and Electronics (Spring 2012) (Online version)

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