It is, therefore, crucial for carefully selecting a good strategy for learning with multiple labels.
The framework draws on research on professional design processes and on learning with multiple external representations.
This article contributes to a deeper understanding of learning with multiple resources, widely available due to digitalisation.
The learning module consisted of a series of interactive seminars and discussions utilising blended learning with multiple teaching approaches including lectures, online multimedia study, role-playing, and activities.
The DeFT (Design, Functions, Tasks) framework for learning with multiple representations integrates research on learning, the cognitive science of representation and constructivist theories of education.
Central implications are that learning with multiple resources places high demands on the learners' participation, and the need to account for the moment-by-moment actions in a long-term, shared process, spread across participants, materials and contexts.
It proposes that the effectiveness of multiple representations can best be understood by considering three fundamental aspects of learning: the design parameters that are unique to learning with multiple representations; the functions that multiple representations serve in supporting learning and the cognitive tasks that must be undertaken by a learner interacting with multiple representations.
The utility of this framework is proposed to be in identifying a broad range of factors that influence learning, reconciling inconsistent experimental findings, revealing under-explored areas of multi-representational research and pointing forward to potential design heuristics for learning with multiple representations.
Deep learning methods are representation learning methods with multiple levels of representation, obtained by composing simply but nonlinear modules that each transforms the representation at one level (starting with the raw input) into a higher representation slightly more abstract level, with the composition of enough such transformations, and very complex functions can be learned [1, 2].
A case is studied to validate the efficiency of the proposed parameter learning approach with multiple referenced values.
