Abstract: A method and system are introduced for recognizing cognitive engagement in classrooms by utilizing multimodal data. Associated modalities of cognitive engagement are learned from visual and audio data. This approach involves constructing a multidimensional representation model for cognitive engagement that includes behaviors, emotions, and speech. To identify engagement, three distinct deep learning models are employed: You Only Look Once version 8 (Yolov8) for analyzing body posture, Efficient Network (EfficientNet) for facial expressions, and Text Convolution Neural Network (TextCNN) for speech text. These models are trained and refined with the aid of student engagement surveys, leading to a decision-making process that integrates the results from the different modalities. Additionally, a dataset and a data annotation system are developed for engagement recognition. This innovative method aims to achieve detailed engagement recognition, addressing various perception needs in real-world applications.

Abstract: A data processing method includes: generating a service serial number for a target service according to a preset naming rule; obtaining service data of the target service; obtaining a target data table from a plurality of pre-configured data tables, according to the service serial number; and storing the service data to the target data table.

Abstract: A data processing method includes: generating a service serial number for a target service according to a preset naming rule; obtaining service data of the target service; obtaining a target data table from a plurality of pre-configured data tables, according to the service serial number; and storing the service data to the target data table.