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[3M1-GS-12-03] Feature Extraction of Students and Problems via Exam Result Analysis using Variational Autoencoder
Keywords:Students' exam result analysis, Autoencoder, Feature extraction
In this paper, we propose a novel examination-result analysis method based on latent variables gained from
Variational AutoEncoder (VAE) specially designed for this purpose. We train our VAE so that the range of
latent variables are within 0 and 1 and also monotonical concerning output of VAE’s decoder, while minimizing
reconstruction loss between input and output like existing VAEs. Using the latent variables, we report a detailed
analysis of both the problems and the examinee.
Variational AutoEncoder (VAE) specially designed for this purpose. We train our VAE so that the range of
latent variables are within 0 and 1 and also monotonical concerning output of VAE’s decoder, while minimizing
reconstruction loss between input and output like existing VAEs. Using the latent variables, we report a detailed
analysis of both the problems and the examinee.
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