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Chen, W. Y., Brinton, C. G., Cao, D. et al. (2019). Early Detection Prediction of Learning Outcomes in Online Short-Courses via Learning Behaviors. IEEE Transactions on Learning Technologies, 12, 44-58.
https://doi.org/10.1109/TLT.2018.2793193

has been cited by the following article:

• TITLE: Study on Online Learning Behavior Analysis and Performance Prediction Based on Improved Random Forest Algorithm

  AUTHORS: Dongxu Liu

  KEYWORDS: Online Learning, Behavioral Features, Personalized Learning, Random Forest

  JOURNAL NAME: Creative Education, Vol.14 No.8, August 10, 2023

  ABSTRACT: With the popularity of online learning, the analysis of student learning behavior and the prediction of academic performance have become a hot research topic in the field of education. This study aims to analyze the factors influencing learning outcomes on online platforms, extract the feature variables from online learning behavior, and construct a weighted random forest model to predict students’ online learning performance. Additionally, a method for computing feature importance is designed to analyze online learning behavior based on the significance of different behavioral features. This contributes to gaining early insights into students’ learning progress, proactively identifying learning issues and strategically allocating educational resources, thus providing students with enhanced guidance and support.