Instructional designers engage in light datamining now and again. There’s going into the back-end of a course to pull records about student interactions for value-added course redesign. There’s tapping into the stats from the survey instruments to evaluate the hardiness of an assessment. There are small overlaps with PI work when they evaluate information from their own research databases.

And then there’s watching others mix and match databases to try to surface hidden information.

Deep Linkages

Computing machines have long been used for knowledge discovery. In private industry and some parts of government, people use machines to identify hidden “tribes” or relationships. They mix and match information to see if there are unseen causal relationships. They combine information for “predictive analytics” beyond intuitive sense-making and trend-lining.

Being able to surface valid information without bias from large and mixed datasets is a skill in itself that requires plenty of conceptual skill and statistical savvy. There’s much to be said for eliminating noise and repeated information from data.

Strategic Information Use

People in academia use information in strategic ways all the time. Their research is done in pursuit of previously-unrevealed information and new conceptualizations of known fields.

If I were designing a curriculum for instructional designers, I would add strategic datamining. It seems like an important skill to have for looking at information and for applying to the e-learning context.