Design and Development of Ant Colonies Optimized Deep Neural Networks for enhancing Chinese Online Learning Platform

Accurately modeling and predicting student behavior is fundamental for the advancement of intelligent online education platforms. Traditional e-learning systems often face limitations in identifying complex learning patterns and adapting to dynamic user interactions. This study presents a neural network based framework that incorporates an Long Short-Term Memory (LSTM) model with the Ant Colony Optimization (ACO) algorithm to enhance learning outcome predictions and learner engagement analysis on online education platforms. The LSTM architecture effectively captures sequential learning behaviors over time, while ACO is employed for optimal feature selection, using factors such as time-on-task, content identifiers, and topic level tags to improve model efficiency and performance. The proposed model is rigorously trained and tested using the EdNet dataset, which offers a rich collection of student learning interactions in online environments. Experimental evaluations show that the algorithms attain an outstanding classification accuracy of 99.3%, along with a precision of 99.1%, recall of 99.4%, specificity of 98.9%, and an F1-score of 99.2%, clearly outperforming existing baseline models. These findings highlight the algorithm’s capability to support adaptive learning and personalized content delivery. The study illustrates the efficiency of combining deep sequential models with nature-inspired optimization in the domain of educational data mining, paving the way for more intelligent and responsive online learning environments.