Learning performance prediction via convolutional GRU and explainable neural networks in e-learning environments

Computing ◽  
2019 ◽  
Vol 101 (6) ◽  
pp. 587-604 ◽  
Author(s):  
Xizhe Wang ◽  
Pengze Wu ◽  
Guang Liu ◽  
Qionghao Huang ◽  
Xiaoling Hu ◽  
...  
Keyword(s):  
Neural Networks ◽  
Learning Environments ◽  
Performance Prediction ◽  
Learning Performance ◽  
E Learning

E-Learning Performance Prediction Based on Attention Mechanism

2021 ◽  
Author(s):  
Xiao Ma ◽  
Jian-Hua Qu ◽  
Hui-Min Xu ◽  
Yu-Ting Ling
Keyword(s):  
Performance Prediction ◽  
Attention Mechanism ◽  
Learning Performance ◽  
E Learning

Culturally-Sensitive E-Learning Practices for Library Education

2013 ◽  
Author(s):  
Leslie Farmer
Keyword(s):  
Online Learning ◽  
Learning Environments ◽  
Culturally Sensitive ◽  
Cultural Dimensions ◽  
Educational Model ◽  
Cultural Impact ◽  
Online Learning Environments ◽  
Library Education ◽  
Learning Practices ◽  
E Learning

With globalization, library educators should address culturally-sensitive instruction design and curriculum, particularly in online learning environments. Hofstede’s cultural dimensions and Bigg’s educational model provide frameworks for addressing cultural impact on library education. Specific techniques are suggested for handling language and online learning issues.Avec la mondialisation, les professeurs de bibliothéconomie devraient incorporer les différences culturelles dans leurs cours ainsi que dans le cursus, notamment en milieu d'apprentissage en ligne. Les dimensions culturelles de Hofstede et le modèle éducatif de Bigg offrent un cadre permettant de traiter de l'impact culturel sur l'éducation. Seront présentées différentes techniques pour aborder les questions de langue et d'apprentissage en ligne.

scholarly journals Exploiting heterogeneity in operational neural networks by synaptic plasticity

2021 ◽  
Author(s):  
Serkan Kiranyaz ◽  
Junaid Malik ◽  
Habib Ben Abdallah ◽  
Turker Ince ◽  
Alexandros Iosifidis ◽  
...  
Keyword(s):  
Neural Networks ◽  
Synaptic Plasticity ◽  
Network Model ◽  
Neuron Model ◽  
Linear Operators ◽  
Training Data ◽  
Learning Performance ◽  
Minimal Network ◽  
Hidden Layer ◽  
Hidden Neurons

AbstractThe recently proposed network model, Operational Neural Networks (ONNs), can generalize the conventional Convolutional Neural Networks (CNNs) that are homogenous only with a linear neuron model. As a heterogenous network model, ONNs are based on a generalized neuron model that can encapsulate any set of non-linear operators to boost diversity and to learn highly complex and multi-modal functions or spaces with minimal network complexity and training data. However, the default search method to find optimal operators in ONNs, the so-called Greedy Iterative Search (GIS) method, usually takes several training sessions to find a single operator set per layer. This is not only computationally demanding, also the network heterogeneity is limited since the same set of operators will then be used for all neurons in each layer. To address this deficiency and exploit a superior level of heterogeneity, in this study the focus is drawn on searching the best-possible operator set(s) for the hidden neurons of the network based on the “Synaptic Plasticity” paradigm that poses the essential learning theory in biological neurons. During training, each operator set in the library can be evaluated by their synaptic plasticity level, ranked from the worst to the best, and an “elite” ONN can then be configured using the top-ranked operator sets found at each hidden layer. Experimental results over highly challenging problems demonstrate that the elite ONNs even with few neurons and layers can achieve a superior learning performance than GIS-based ONNs and as a result, the performance gap over the CNNs further widens.

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