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This paper investigates the application of the snap-drift neural network (SDNN) to the provision of guided student learning in formative assessments. SDNN is able to adapt rapidly by performing a combination of fast, convergent, minimal intersection learning (snap) and learning vector quantization (drift) to capture both precise sub-features in the data and more general holistic features. Snap and...
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