Yesterday, I gave a talk on educational data mining at the University of Amsterdam (my previous employer). The talk was about how I approach the very difficult issue of trying to understand what learners are doing in learning environments. During the inevitable drinks afterwards, someone exclaimed that's not machine learning. He was right, I actually tried the standard data mining techniques and they don't seem to produce any useful results. The key, I think, is in understanding what you want to discover, and not whether you are using the "correct" algorithms. Educational data mining, and data mining in general, is these days biased by the "Microsoft/Google" of data mining packages (it is called WEKA and I refuse to provide a link). Researchers compress there data such that WEKA can handle it, and then presto one of the algorithms produces some results.
Today, I gave a course to third year psychology students on educational data mining. Explaining what the issues are, and showing the results produced by the new methods we have developed. The students were very responsive, asking good questions, and aligned with the idea that in educational data mining the purpose is to understand the behaviour of the learner which the standard data mining techniques hardly provide an opportunity for.
Last week, Lilia and I had a discussion about a chapter of her thesis. Lilia's little toddler Alexander (1 year and 3 months) was present. He liked emptying my trash bin, putting the trash into the bin again, emptying it and so forth indefinitely. Perhaps, researchers, once they have reached a certain level of maturity, become toddlers again.