Kolb asserted that deep learning comes through a sequence of experience, reflection, abstraction, and active testing.
There remains a flawed assumption that deep learning can be acquired and assessed in conveniently bite-size chunks.
Research showed that those with a deep learning style were likely to organise ideas into networks.
Deep learning has powerful implications for machine translation.
Deep learning in musikdidaktik required a level of experience with trainees' musical instrument, which was usually developed during the first year at the institution.
Deep learning was enhanced by the sequencing and integration of musikdidaktik, principal instrument and practical teacher training.
Hence, the possibilities for enhancing deep learning in musikdidaktik also seemed embedded outside the subject, yet within the institutional culture itself.
