Project based and applied learning.
Reflection is a process of digesting new information or experience; it helps to understand and then apply learning to structured tasks.
Ensemble methods are widely applied to supervised learning tasks.
Applied during various learning tasks (e.g., motor learning; Narayana et al., 2014 and number processing; Cohen Kadosh, Soskic, Iuculano, Kanai, & Walsh, 2010), TMS increases performance, possibly by stimulating local hebbian learning (i.e. enhancing the connections between cells active together).
These results suggest that the extended HTM approach is a valid bioinspired alternative to existing SLR engines and that it can be successfully applied to other machine learning tasks whose input instances also unfold over time.
Instructional support strategies of friendship and meaningful learning tasks were applied to promote collaboration.
The first described level of abstract thinking (rule inferring, rule applying, rule following or new learning tasks), the second related to mode of representation (verbal, numeral, visual), while the third dimension related to output mode (oral, manual, or pencil and paper).
Over the last years, deep learning has been applied successfully to various recognition and control learning tasks in robotics.
In addition, we also apply the OTL technique to attack the challenging online learning tasks with concept-drifting data streams.
For the didactical design of the learning tasks the method of the »complete action« [4] is applied.
Model-based reinforcement learning is a powerful paradigm for learning tasks in robotics.
