An RNN-attention model with knowledge distillation predicts at-risk students slightly better than standard RNNs using only early course weeks, on four years of one university course.
Constrained-degree percolation in random environment
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abstract
We consider the Constrained-degree percolation model in random environment on the square lattice. In this model, each vertex $v$ has an independent random constraint ${\kappa}_v$ which takes the value $j\in \{0,1,2,3\}$ with probability $\rho_j$. Each edge $e$ attempts to open at a random uniform time $U_e$ in $[0,1]$, independently of all other edges. It succeeds if at time $U_e$ both its end-vertices have degrees strictly smaller than their respectively attached constraints. We show that this model undergoes a non-trivial phase transition when $\rho_3$ is sufficiently large. The proof consists of a decoupling inequality, the continuity of the probability for local events, and a coarse-graining argument.
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Knowledge Distillation in RNN-Attention Models for Early Prediction of Student Performance
An RNN-attention model with knowledge distillation predicts at-risk students slightly better than standard RNNs using only early course weeks, on four years of one university course.