This is very true – if MOOCs are to be truly successful for on campus students then any decisions we make based on data for educational enhancements, needs to be judged against a wider criteria. Question is, how can we attract participants from a broader base? Maybe more collaborations with colleagues in other institutions or even better community connections.
Computing Ed Research - Guzdial's Take
Important article that gets at some of my concerns about using MOOCs to inform education research. The sampling bias mentioned in the article below is one of my responses to the claim that we can inform education research by analyzing the results of MOOCs. We can only learn from the data of participants. If 90% of the students go away, we can’t learn about them. Making claims about computing education based on the 10% who complete a CS MOOC (and mostly white/Asian, male, wealthy, and well-educated at that) is bad science.
Cheerleaders for big data have made four exciting claims, each one reflected in the success of Google Flu Trends: that data analysis produces uncannily accurate results; that every single data point can be captured, making old statistical sampling techniques obsolete; that it is passé to fret about what causes what, because statistical correlation tells us what we need to know; and…
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