Optimizing Causal Inference Approach for Exploring Shallow Reading Behavior with Generative Adversarial Networks

Authors

  • Yu BAI Graduate School of Information Science and Electrical Engineering, Kyushu University Author
  • Fuzheng ZHAO Education Technology Center, Jilin University Author
  • Wenhao WANG Graduate School of System Informatics, Kobe University Author
  • Chengjiu YIN Research Institute for Information Technology, Kyushu University Author

DOI:

https://doi.org/10.58459/icce.2024.4858

Abstract

The prevalence of shallow reading in online digital learning is steadily increasing, which has sparked interest in revealing the mechanisms behind shallow reading behavior, especially analyzing the causal relationship between its constituent features and learning performance. However, current causal analysis methods have many limitations in terms of experimental conditions, data independence assumptions, and analysis costs. Drawing on the application experience of Markov chain theory in the field of causality, this study adopts the structure-agnostic model (SAM) algorithm to design the structure, parameter loss, and learning process, and proposes an evaluation method for causal exploration based on generative adversarial neural networks (GANs). The study shows that the proposed maximum mean diversity (MMD) optimization method improves the stability of the model analysis results and clarifies that reading speed is a key factor in the occurrence of shallow reading behavior.

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Published

2024-11-25

How to Cite

Optimizing Causal Inference Approach for Exploring Shallow Reading Behavior with Generative Adversarial Networks. (2024). International Conference on Computers in Education. https://doi.org/10.58459/icce.2024.4858