Presentation used by Tinne De Laet, KU Leuven, for a keynote presentation during an event: organised by Leiden University, Erasmus University Rotterdam, and Delft University of Technology.
The presentations presents the results of two case studies from the Erasmus+ project ABLE and STELA, and provides 9 recommendations regarding learning analytics
Recommender systems provide users with content they might be interested in. Conventionally, recommender systems are evaluated mostly by using prediction accuracy metrics only. But, the ultimate goal of a recommender system is to increase user satisfaction.
Now that the “the only constant is change” in society, our capacity to engage with novel challenges is of first order importance. What are the personal dispositions that authentic learning needs to cultivate, and can we make these assessable and visible to learners and educators?
An interesting question arose at a recent xAPI Camp hosted by The eLearning Guild: “What happened to objectives in xAPI?” We should be able to use xAPI to document successful completion of eLearning, but without statements of learning objectives in the content, this is not possible.
The Experience API (xAPI) allows us to collect data about any type of learning experience or activity, but does that mean we should? Should we generate massive amounts of xAPI data for every possible type of interaction and then expect to make sense of it all later? This approach can be costly in terms of data storage, but also in terms of your time.