MOOCs Use Data Science for Self-Reflection

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MOOCApparently the MOOC (Massive Open Online Course) ecosystem is practicing what they preach. As part of the Artificial Intelligence in Education (AIED 2013) conference this past July, a special workshop was held – the “moocshop” – that included a number of sessions by representatives from the high-flying MOOCs Coursera, edX, as well as by researchers from top universities. The group surveyed the rapidly expanding MOOC industry. The sessions fostered a cross-institutional and cross-platform dialogue in order to articulate and synthesize the plurality of challenges that arise when evaluating and designing MOOCs. While the forms and functions of MOOCs are currently evolving, the aim was to develop a shared foundation for an interdisciplinary field of inquiry moving forward.

Participating in the program were researchers, technologists, and course designers from universities and industry to share their approaches and perspectives on key topics, including analytics and data mining, assessment, credentialing, pedagogy, platform design, data standards, and privacy.

One of my favorite papers presented at the workshop is “MOOCdb: Developing Data Standards for MOOC Data Science.” Here, researchers analyze the results from the first course offered by edX:

Our team has been conducting research related to mining information, building models, and interpreting data from the inaugural course offered by edX, 6.002x: Circuits and Electronics, since the Fall of 2012. This involves a set of steps, undertaken in most data science studies, which entails positing a hypothesis, assembling data and features (aka properties, covariates, explanatory variables, decision variables), identifying response variables, building a statistical model then validating, inspecting and interpreting the model.

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