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

SUBMITTER: Wang J 

PROVIDER: S-EPMC12453797 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature