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Developing Data Standards and Systems for MOOC Data Science

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. In our domain, and others like it that require behavioral analyses of an online setting, a great majority of the effort (in our case approximately 70%) is spent assembling the data and formulating the features, while, rather ironically, the model building exercise takes relatively less time. As we advance to analyzing cross-course data, it has become apparent that our algorithms which deal with data assembly and feature engineering lack cross-course generality. This is not a fault of our software design. The lack of generality reflects the diverse, ad hoc data schemas we have adopted for each course. These schemas partially result because some of the courses are being offered for the first time and it is the first time behavioral data has been collected. As well, they arise from initial investigations taking a local perspective on each course rather than a global one extending across multiple courses. In this position paper, we advocate harmonizing and unifying disparate “raw” data formats by establishing an open-ended standard data description to be adopted by the entire education science MOOC oriented community. The concept requires a schema and an encompassing standard which avoid any assumption of data sharing. It needs to support a means of sharing how the data is extracted, conditioned and analyzed. Sharing scripts which prepare data for models, rather than data itself, will not only help mitigate privacy concerns but it will also provide a means of facilitating intra and inter-platform collaboration. For example, two researchers, one with data from a MOOC course on one platform and another with data from another platform, should be able to decide upon a set of variables, share scripts that can extract them, each independently derive results on their own data, and then compare and iterate to reach conclusions that are cross-platform as well as cross-course. In a practical sense, our goal is a standard facilitating insights