Anomaly detection for English MOOC teaching platform data based on improved random forest

Key Points

• This research aims to enhance anomaly detection methods for data derived from English MOOCs using an improved random forest algorithm.
• Implemented an advanced random forest algorithm for anomaly detection
• Analyzed data from multiple educational MOOCs
• Compared results to traditional anomaly detection techniques
• Improved accuracy of anomaly detection was observed
• Reduction in false positives was noted
• Enhanced performance metrics compared to existing methods