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Each theme contained three categories: a) learning activities, b) learning outcomes, and c) conditions for learning.
Intervention: Participants each completed 5 weeks of clinical placement, utilising a peer-assisted learning model (a standardised series of learning activities undertaken by student pairs and educators to facilitate peer interaction using guided strategies) and a traditional model (usual clinical supervision and learning activities led by clinical educators supervising pairs of students).
For each learning activity, a percentage was given of the total amount of learning activities used.
Supervised learning is a type of machine learning based on training data, which consists of pairs of input objects, and desired outputs.
Such models are most of the time supervised, thus requiring a learning phase.
Supervised learning is a process of creating predictive models using a set of historical data and produce predictive results.
Machine Learning based proposals fall into two categories, supervised learning [24, 56] and unsupervised learning [38].
Traditionally, supervised classifier learning has required a lot of labeled data.
Supervised statistical learning has become a critical means to design and learn visual concepts (e.g., faces, foliage, buildings, etc). in content-based indexing systems.
Supervised learning requires a large amount of labeled data, but the data labeling process can be expensive and time consuming, as it requires the efforts of human experts.
Supervised learning requires an explicit error function between outcome and the taught examples for controlling the learning progress.
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