Applying Recent Innovations from NLP to MOOC Student Course Trajectory Modeling
This paper presents several strategies that can improve neural network-based predictive methods for MOOC student course trajectory modeling, applying multiple ideas previously applied to tackle NLP (Natural Language Processing) tasks. In particular, this paper investigates LSTM networks enhanced with two forms of regularization, along with the more recently introduced Transformer architecture.
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Methods
Absolute Position Encodings •
Adam •
BPE •
Dense Connections •
Dropout •
Label Smoothing •
Layer Normalization •
Linear Layer •
LSTM •
Multi-Head Attention •
Position-Wise Feed-Forward Layer •
ReLU •
Residual Connection •
Scaled Dot-Product Attention •
Sigmoid Activation •
Softmax •
Tanh Activation •
Transformer
