Artificial Neural Networks for Educational Data Mining and Prediction

Neural networks are powerful computational models, inspired by the structure of the human brain, that learn to recognize complex patterns in data. Imagine you are an educational researcher trying to understand why some students become frustrated while using a digital learning platform. While you cannot read their minds, you have access to a rich stream of data detailing their every click, hint request, and response time. How can you use this digital trace to automatically detect these critical, yet often invisible, emotional states? Traditional statistical methods may struggle to capture the complex, non-linear relationships between hundreds of behavioral features and a student’s emotional state. This is where neural networks excel, offering a flexible and powerful approach to modeling such complex data. This ability to model complex, real-world phenomena without pre-specified rules makes neural networks a powerful tool for advancing educational technology research, enabling data-driven insights that can help create more adaptive and supportive learning environments. This chapter serves as a comprehensive guide for educational researchers to understand the theory behind neural networks, build and train predictive models using the R programming language, and interpret their results in a meaningful way.