Journal of Korea Multimedia Society (한국멀티미디어학회논문지)

Korea Multimedia Society (한국멀티미디어학회)
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Variational Auto-Encoder Based Semi-supervised Learning Scheme for Learner Classification in Intelligent Tutoring System

지능형 교육 시스템의 학습자 분류를 위한 Variational Auto-Encoder 기반 준지도학습 기법

  • Jung, Seungwon (School of Electrical Engineering, Korea University) ;
  • Son, Minjae (School of Electrical Engineering, Korea University) ;
  • Hwang, Eenjun (School of Electrical Engineering, Korea University)
  • Received : 2019.09.10
  • Accepted : 2019.11.11
  • Published : 2019.11.30
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Abstract

Intelligent tutoring system enables users to effectively learn by utilizing various artificial intelligence techniques. For instance, it can recommend a proper curriculum or learning method to individual users based on their learning history. To do this effectively, user's characteristics need to be analyzed and classified based on various aspects such as interest, learning ability, and personality. Even though data labeled by the characteristics are required for more accurate classification, it is not easy to acquire enough amount of labeled data due to the labeling cost. On the other hand, unlabeled data should not need labeling process to make a large number of unlabeled data be collected and utilized. In this paper, we propose a semi-supervised learning method based on feedback variational auto-encoder(FVAE), which uses both labeled data and unlabeled data. FVAE is a variation of variational auto-encoder(VAE), where a multi-layer perceptron is added for giving feedback. Using unlabeled data, we train FVAE and fetch the encoder of FVAE. And then, we extract features from labeled data by using the encoder and train classifiers with the extracted features. In the experiments, we proved that FVAE-based semi-supervised learning was superior to VAE-based method in terms with accuracy and F1 score.

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