A back-propagation algorithm is a multilayer feed-forward ANN, which can train and then evaluate the system performance using an adaptive gradient learning rule [ 23– 25].
Starting form research results of these fields we defined a methodology and developed a software prototype able to recommend learning goals and to generate learning experiences for learners using an adaptive e-learning system.
This paper reports on differences in learning experience from the learner's perspectives when using an adaptive e-learning system, where the learner's knowledge or skill level is used to configure the learning path.
Having worked for nearly two decades to manage natural resources and rare species: using an adaptive management, or active learning approach, assessment, and iterative improvements to policy and practice, are standard operating procedure for this professional.
This requirement calls for the use of an adaptive learning system such as artificial neural networks (ANN) (Abraham 2005)[6].
But, to make the training faster, a modified version of this algorithm is used, which incorporates an adaptive learning rate and momentum [25].
The neural network (NN) is trained on-line using the backpropagation optimization algorithm with an adaptive learning rate.
In this example and others, digital games use adaptive learning principles, where the learning environment is re-configured or modified in response to the actions and needs of students (Bower, 2016).
By constructing a differential-difference-type learning law and an adaptive learning control law, and using Lyapunov-Krasovskii-like composite energy functional method, a novel sufficient condition is derived to ensure adaptive asymptotical synchronization in the mean square sense for the addressed system.
Behavioral comparisons showed that participants with higher IQ used a more adaptive learning strategy after receiving positive feedback.
