Leveraging PSO-MLP for intelligent assessment of student learning in remote environments: a multimodal approach.

Abstract

The rapid advancement of artificial intelligence (AI) has catalyzed transformative changes in education, particularly in mobile and online learning environments. While existing deep learning models struggle to efficiently integrate the complexity of remote education data and optimize model performance, this article proposes an intelligent evaluation method for students' learning states based on multimodal data. First, the joint characteristics of the pre-class mental status survey information and the health big data of teachers and students in the online teaching process constitute input data. Then, the multilayer perceptron (MLP) is used to intelligently identify the students' status and classify their enthusiasm for the class. Finally, the particle swarm optimization (PSO) model is used to optimize the model and improve the overall recognition rate. Compared to traditional methods, the PSO-MLP model with combined multimodal data performs well, achieving an accuracy of 0.891. It provides an operational, technical solution for the education system, provides a new AI foundation for personalized teaching and student health management by accurately assessing students' learning status, and helps to improve the effectiveness and efficiency of remote education.