The adaptive learning problem concerns how to create an individualized plan (also referred as a policy) that chooses the most appropriate materials based on learner’s latent traits. In this article, we study important yet less-addressed problem—one assumes continuous Specifically, formulate Markov decision process. We assume traits be with unknown transition model and apply model-free deep rein...

This review explores how Moodle's machine learning capabilities enhance adaptive learning. We analyze five studies using Moodle for predictive and prescriptive support in education. These cover topics like learner classification, early risk detection, predictor comparison, reliability analysis, custom indicators. extract key findings, address challenges while suggesting future research directio...

A novel adaptive scene-based nonuniformity correction technique is presented. The technique simultaneously estimates detector parameters and performs the nonuniformity correction based on the retina-like neural network approach. The proposed method includes the use of an adaptive learning rate rule in the gain and offset parameter estimation process. This learning rate rule, together with a red...

Massive Open Online Courses (MOOCs) as a new shaking educational development provide the scene for achieving social inclusion and dissemination of knowledge. Anyhow, facilitating network learning experiences through creating an adaptive learning environment can pave the way for this open and energetic way to learning. The present study aimed to explore the possible role of Dynamic Assessment (D...

The problem of generalizing deep neural networks from multiple source domains to a target one is studied under two settings: When unlabeled data available, it multi-source unsupervised domain adaptation (UDA) problem, otherwise generalization (DG) problem. We propose unified framework termed adaptive ensemble learning (DAEL) address both problems. A DAEL model composed CNN feature extractor sha...

Estimation of distribution algorithm (EDA) is a kind of EAs, which is based on the technique of probabilistic model and sampling. Large scale optimization problems are a challenge for the conventional EAs. This paper presents an adaptive learning rate elitism EDA combining chaos perturbation (ALREEDA) to improve the performance of traditional EDA to solve high dimensional optimization problems....

With the rapid development of Internet technology, the network now has a large size and high complexity, and consequently the network management is becoming increasing difficult and complexity, so traffic forecast play a more and more role in network management. With a large amount of real traffic data collected from the actual network, an adaptive network traffic prediction algorithm based on ...