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It has been found out that deeper learner engagement results in higher learning gains.
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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.
In Open-ACRE deep learner models can be based on very simple aggregations to sophisticated machine learning algorithms.
The design of a deep learner model should begin with an explicit hypothesis (e.g. components of motivation).
8 See discussion of deep learner models for examples of domain independent models in the inner loop.
We can extend the deep learner model taxonomy by incorporating a "meta" layer corresponding to cognition, affect, motivation, and learning strategy.
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.
It is suggested that deep learner models will become the vehicle for incorporating theoretical and practical advances in learning science into adaptive learning systems.
As adaptive systems incorporate deep learner models, they will need to evolve architecturally from closed data islands to open systems capable of exchanging data and services residing externally to the adaptive system proper.
10 It should be noted that Bloom's theory of mastery learning anticipates the idea of deep learner models by considering two principal student characteristics: cognitive entry behaviors and affective entry characteristics.
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