An Empirical Study of Data Mining Technology in English Learning Outcome Prediction

Abstract

This study employs data mining to construct a predictive model for English learning outcomes, aiming to identify crucial factors affecting student performance and inform teaching enhancements. Through extensive data collection and meticulous preprocessing, a robust predictive model is established. Detailing the theoretical framework and methodologies such as machine learning, data preprocessing, feature selection, and model assessment, the study ensures model accuracy and reliability. Data sourcing and processing steps transform raw data into a model-ready feature set. Model validation reveals high prediction accuracy and generalizability, with key features significantly impacting achievement predictions, deepening insight into learning determinants and guiding targeted teaching strategies. The research concludes with a summary of findings, acknowledging limitations, and suggesting future avenues. This data-driven English learning achievement prediction model offers promising educational applications, enhancing decision-making and personalized learning.