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Learning Analytics

Eine gelungene kurze Darstellung des neuen Forschungsfeldes: Zuerst verorten die Autoren „Learning Analytics“ zwischen „quantified self“ und „big data“, zwischen „educational data mining“ und „information visualization“; dann stellen sie eine Reihe von Perspektiven und Fragen vor, die mit dem Thema einhergehen, z.B.:

„A really tough problem is figuring out what are meaningful learning traces: this is all the more problematic in learning, where there seems to be less consensus on what are relevant criteria… Maybe time of day or location are relevant. And maybe they are not. Maybe whom the learner is with or what device she is using is relevant. Maybe not. Maybe what the teacher has had for lunch or the background noise level is important. Maybe not. As mobile devices proliferate, and as these devices integrate more sensors, there are more and more characteristics that we can measure. Still, it is not because we can measure them that they are relevant. However, it is often difficult to figure out beforehand what will be relevant, why or how…“
Erik Duval und Katrien Verbert, eleed, Vol. 8., Juni 2012

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