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We also assume that learner feedback is dynamically generated from multiple deep learner models.
'Deep learner models' section introduces the concept of a deep learner model, which is a 360° perspective of each learner based on multiple dimensions including cognition, affect, motivation, and meta-cognition.
8 See discussion of deep learner models for examples of domain independent models in the inner loop.
The design of a deep learner model should begin with an explicit hypothesis (e.g. components of motivation).
In Open-ACRE deep learner models can be based on very simple aggregations to sophisticated machine learning algorithms.
Drawing on the work developed by du Boulay and colleagues (2010) we can classify deep learner models in terms of the taxonomy in Table 1.
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Debate prep for the deep learners.
Thirdly, deep learners may be effective learners in any setting.
Furthermore, Eizenberg (1988) warns that sheer volume will discourage students from adopting deep, holistic approaches to learning, making deep learners become surface learners [ 9].
Newble and Entwistle (1986) believe that under such pressure, even deep learners, who usually come to terms with new information by understanding and making personal meaning of the experience, may become surface learners [ 3].
To this end, if assessment is largely in the form of multiple choice questions, testing volumes of factual knowledge, then even those students who are usually deep learners may be forced to become surface learners [ 3].
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com