… to predict students’ performance based on their historic data in the current course. Therefore,
a novel framework is … In addition, we also compare three recurrent neural network (RNN) …

… NEURAL NETWORK (DFFNN) After training the predictive … a deep learning technique called
Deep Feed Forward Neural … dropout in a MOOC: An evaluation of a deep neural network …

… for the improvement of risky students and to yield a supportive … of deep learning practices to
early predict the students at risk … of recurrent neural networks (RNN) to predict the success of …

… a Deep Neural Network (DNN) framework for binary classification with two hidden layers, for
the early prediction of … The framework was evaluated by using different activation functions (…

… We proposed and applied a minimal recurrent neural network (minimalRNN) model to data
from The Alzheimer's Disease Prediction Of Longitudinal Evolution (TADPOLE) challenge, …

… from students’ campus behaviors, we first introduce an attention-based Hybrid Recurrent
Neural Network (… weight to the ones that are related to the students’ last action. Then, to conduct …

… Therefore, the at-risk students in this study were defined as those who might get a final mark
… LSTM network is a subset of recurrent neural networks (RNNs), which are neural networks (…

… LSTM is a gated recurrent neural network, which consists of forget gate and input gate. The
forget gate decides which part of the information to forget from the cell state. This is useful …

… In order to use these data, we need to transform them into a format that can be fed into
deep neural networks. To this end, we propose a data transformation algorithm based on time …

… -to-sequence vessel trajectory prediction models based on encoder–decoder recurrent
neural networks (RNNs) that are trained on historical trajectory data to predict future trajectory …