A Self-Relevant CNN-SVM Model for Problem Classification in K-12 Question-Driven Learning

Eric Hsiao Kuang Wu, Sung En Chen, Jhao Jhong Liu, Yu Yen Ou, Min Te Sun

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

With the development and progress of science and technology, the learning patterns also evolve. In Question-Driven learning, students clarify and validate what they learn by answering questions. Such a large number of questions needs good management. A well-performed management can avoid the situation that learning materials with the same knowledge set are defined into different sections due to ambiguous expressions. In this work, we propose a hybrid classification model using the CNN-SVM that focuses on K-12 learning materials. We combine the Word2Vec feature and the hidden layer feature of CNN. In response to a current question that contains text and image, we also introduce a multi-modal preprocessing approach. The experiment results validate that the preprocessing method and the hybrid model can outperform the the state-of-the-art method and baseline methods.

Original languageEnglish
Article number9265190
Pages (from-to)225822-225830
Number of pages9
JournalIEEE Access
Volume8
DOIs
StatePublished - 2020

Keywords

  • Classification
  • convolutional neural network
  • question-driven learning
  • support vector machine
  • Word2Vec

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