Sentence examples for learning section for from inspiring English sources

The Nick Jr. Web site, nickjr.com, will have a learning section for parents, including the curriculum and activities to help preschoolers master the concepts, as well as games and videos.

Based on the requirements identified in Computerized Adaptive Testing for online learning section, we propose an architecture for a computerized adaptive testing system that can be used in the course of online learning activities.

The requirements identified in Computerized Adaptive Testing for online learning section have been met by the platform in its current implementation as follows: R1 and R3 are met via the exchangeable test engine that can be implemented to handle arbitrary item pools, conduct different testing strategies and check diverse stopping criteria.

Section "Learning environments for communication skills" discusses virtual learning environments that offer scenario based training or assessment.

We can experiment if a hybrid approach leads to more variability during game-play while preserving the consistency and other advantages of a scripted approach as described in Section "Learning environments for communication skills".

'Learning objects' section defines the concept of modular learning objects as flexible building blocks for adaptive systems.

The first column of Table 2 shows the gene prioritization methods and their main associated learning parameters (see Section  2.5 for details).

Using a simple Rescorla Wagner learning rule, with two learning rates (for CS+ and CS−), we fitted each participant's dataset and computed the explained variance under an optimal learning rate (see Section 2 for details).

"Transfer learning application" section provides examples of transfer learning applications.

Then, we construct a distributed subcarrier allocation algorithm based on no-regret procedure in Section "Distributed learning algorithm for joint strategy selection" and prove that the algorithm converges to a set of correlated equilibria.

The method presented in this article is based on three core algorithmic ideas: mean shift clustering to detect soma centers (Section 2.2), supervised semantic deconvolution by means of neural networks for image enhancement (Section 2.3) and manifold learning for filtering false positives (FPs) (Section 2.4).