Open Access

ARTICLE

An Analysis Model of Learners’ Online Learning Status Based on Deep Neural Network and Multi-Dimensional Information Fusion

Mingyong Li¹, Lirong Tang¹, Longfei Ma¹, Honggang Zhao¹, Jinyu Hu¹, Yan Wei^1,2,*

1 College of Computer and Information Science, Chongqing Normal University, Chongqing, 401331, China
2 Chongqing Engineering Research Center of Educational Big Data Intelligent Perception and Application, Chongqing, 401331, China

* Corresponding Author: Yan Wei. Email:

(This article belongs to the Special Issue: Enabled and Human-centric Computational Intelligence Solutions for Visual Understanding and Application)

Computer Modeling in Engineering & Sciences 2023, 135(3), 2349-2371. https://doi.org/10.32604/cmes.2023.022604

Received 17 March 2022; Accepted 12 July 2022; Issue published 23 November 2022

Abstract

The learning status of learners directly affects the quality of learning. Compared with offline teachers, it is difficult for online teachers to capture the learning status of students in the whole class, and it is even more difficult to continue to pay attention to students while teaching. Therefore, this paper proposes an online learning state analysis model based on a convolutional neural network and multi-dimensional information fusion. Specifically, a facial expression recognition model and an eye state recognition model are constructed to detect students’ emotions and fatigue, respectively. By integrating the detected data with the homework test score data after online learning, an analysis model of students’ online learning status is constructed. According to the PAD model, the learning state is expressed as three dimensions of students’ understanding, engagement and interest, and then analyzed from multiple perspectives. Finally, the proposed model is applied to actual teaching, and procedural analysis of 5 different types of online classroom learners is carried out, and the validity of the model is verified by comparing with the results of the manual analysis.

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Keywords

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