Voorbeelden van deep learning

• 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.

• Many search engines use deep learning to optimize results.

• 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.

• One important interest in this school is the relationship between deep learning and valued language in academic disciplines.

• The preconditions for deep learning that were identified as located to the trainees included the areas of trainee identity, learning orientations and musical experience.

• The position of the musikdidaktik subject within music teacher education as a whole was also perceived to influence the possibility of deep learning.

• The preconditions that were perceived as significant for deep learning included professors' attitudes to the trainees' preconditions and professors' approaches to the subject.

• The interviewees particularly emphasised the institution's disposition towards time allocation, and its organisation as being significant for the trainees' deep learning.

• It was held that trainee preconditions for deep learning in musikdidaktik were strongly related to trainees' musical maturity.

• Summing up, three issues that could be connected to the professors emerged as significant for trainees to achieve deep learning.

• Summing up, three time-related issues emerged as significant for the trainees' deep learning.

• The professor-content relation of the didaktik triangle was perceived to include preconditions for the trainees' deep learning in several significant ways.

• Summing up, five issues that concerned the institutions' educational organisation emerged as significant for the trainees' deep learning.

• Another, institutional precondition that seemed to influence trainees' deep learning rather strongly were the relations between the musikdidaktik subject and practical teacher training.

• This made it impossible for trainees to achieve deep learning in all subjects.