
Learning Cognitive Models Using Neural Networks [pdf] - stablemap
https://arxiv.org/abs/1806.08065
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cs702
"As shown in Figure 1, the neural architecture, which is domain-specific, is
connected to a fixed size pre-output layer, which will serve as the
representations for corresponding problems. The preoutput layer is in turn
connected to the output layer which predicts the correct answer for the given
input problem. After training the architecture on the problems in the tutor,
we use the trained model to compute the representations vectors in the pre-
output layer for each problem. These representations are thresholded at 0.95
and used as columns of the estimated Q-matrix. In other words, each dimension
of the learned representation constitutes a Knowledge Component in the
predicted cognitive model. This cognitive model is evaluated by fitting an
Additive Factors Model using the student performance data."

In other words, a fixed-size preoutput layer added to a range of different
domain-specific architectures constructs a task-specific Q-matrix as good as
or better than those hand-designed by human experts (from human learning
data), simply by training the neural net to learn to perform the task. A
"Q-matrix" is a matrix describing relations of questions and concepts required
for their understanding in a domain of human knowledge.

We can now use neural nets to create Q-matrices in domains where an accurate
human-authored cognitive model is unavailable or authoring a cognitive model
is difficult.

Neat.

