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Ten caveats of learning analytics in health professions education: A consumer's perspective.

Olle Ten CateSuzan DahdalThomas LambertFlorian NeubauerAnina PlessPhilippe Fabian PohlmannHarold van RijenCorinne Gurtner
Published in: Medical teacher (2020)
A group of 22 medical educators from different European countries, gathered in a meeting in Utrecht in July 2019, discussed the topic of learning analytics (LA) in an open conversation and addressed its definition, its purposes and potential risks for learners and teachers. LA was seen as a significant advance with important potential to improve education, but the group felt that potential drawbacks of using LA may yet be under-exposed in the literature. After transcription and interpretation of the discussion's conclusions, a document was drafted and fed back to the group in two rounds to arrive at a series of 10 caveats educators should be aware of when developing and using LA, including too much standardized learning, with undue consequences of over-efficiency and pressure on learners and teachers, and a decrease of the variety of 'valid' learning resources. Learning analytics may misalign with eventual clinical performance and can run the risk of privacy breaches and inescapability of documented failures. These consequences may not happen, but the authors, on behalf of the full group of educators, felt it worth to signal these caveats from a consumers' perspective.
Keyphrases
  • healthcare
  • big data
  • human health
  • health information
  • public health
  • systematic review
  • mental health
  • quality improvement
  • artificial intelligence
  • climate change
  • deep learning