☆ 3.8 Proceedings Paper

Capturing Fairness and Uncertainty in Student Dropout Prediction - A Comparison Study

ARTIFICIAL INTELLIGENCE IN EDUCATION (AIED 2021), PT II (2021)

MOOC student dropout prediction model based on learning behavior features and parameter optimization

Cong Jin

Summary: This paper proposes a method to predict students' dropout status using their learning behavior data. By designing a feature extraction method and an intelligently optimized SVR model, this method outperforms other benchmark models in predictive performance.

INTERACTIVE LEARNING ENVIRONMENTS (2023)

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Article Engineering, Multidisciplinary

MOOC Dropout Prediction Using a Hybrid Algorithm Based on Decision Tree and Extreme Learning Machine

Jing Chen et al.

MATHEMATICAL PROBLEMS IN ENGINEERING (2019)

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Proceedings Paper Computer Science, Interdisciplinary Applications

Longitudinal Engagement, Performance, and Social Connectivity: a MOOC Case Study Using Exponential Random Graph Models

Mengxiao Zhu et al.

LAK '16 CONFERENCE PROCEEDINGS: THE SIXTH INTERNATIONAL LEARNING ANALYTICS & KNOWLEDGE CONFERENCE, (2016)

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Capturing Fairness and Uncertainty in Student Dropout Prediction - A Comparison Study

相关参考文献

MOOC student dropout prediction model based on learning behavior features and parameter optimization

MOOC Dropout Prediction Using a Hybrid Algorithm Based on Decision Tree and Extreme Learning Machine

Longitudinal Engagement, Performance, and Social Connectivity: a MOOC Case Study Using Exponential Random Graph Models

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