Using Convolutional Neural Network to Recognize Learning Images for Early Warning of At-Risk Students

Zongkai Yang, Juan Yang, Kerry Rice, Jui Long Hung, Xu Du

Research output: Contribution to journalArticlepeer-review

38 Scopus citations

Abstract

This article proposes two innovative approaches, the one-channel learning image recognition and the three-channel learning image recognition, to convert student's course involvements into images for early warning predictive analysis. Multiple experiments with 5235 students and 576 absolute/1728 relative input variables were conducted to verify their effectiveness. The results indicate that both methods can significantly capture more at-risk students (the highest average recall rate is equal to 77.26%) than the following machine learning algorithms-support vector machine, random forest, and deep neural network-in the middle of the semester. In addition, the innovative approaches allow minor subtypes of at-risk student identification and provide visual insights for personalized interventions. Implications and future directions are also discussed in this article.

Original languageEnglish
Article number9072352
Pages (from-to)617-630
Number of pages14
JournalIEEE Transactions on Learning Technologies
Volume13
Issue number3
DOIs
StatePublished - 1 Jul 2020

Keywords

  • At-risk
  • convolutional neural networks (CNNs)
  • distance learning
  • early warning
  • image recognition
  • machine learning
  • prediction

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