Abstract The teaching of English at colleges encounters difficulties because student achievement ranges widely and students learn through various factors, while teaching outcomes cannot be assessed without bias. The conventional analysis methods, which include descriptive statistics and linear regression, and experience-based teacher assessment, fail to reveal hidden patterns within extensive learning datasets. The research introduces an artificial intelligence framework that evaluates college English performance through its two main components: a multilayer perceptron (MLP) neural network and a random forest algorithm, which processed performance data from 583 undergraduate students. The academic year data set contains anonymous student learning behavior and assessment records, which were collected through a university academic affairs management system and an online learning platform. The analysis used Pearson correlation analysis to select twelve important feature variables. K-means clustering was applied as an unsupervised learning method to divide students into four learning categories: excellent, good, average, and in need of improvement. An MLP regression m
📖 افتح في inklap 🔗 DOI 📮 اطلب بحثاً