A Framework of an Intelligent Education System for Higher Education Based on Deep Learning

Xiuli Zhang,Zhongqiu Cao

doi:10.3991/ijet.v16i07.22123

Abstract

Intelligent learning platforms and education information application platforms are gaining ground, owing to the wide application of modern technologies such as the Internet of Things, big data analysis, artificial intelligence, and cloud computing. However, the current platforms cannot solve specific teaching problems, and the relevant research mostly focuses on primary and secondary education. Therefore, this paper constructs and analyzes a framework of intelligent education system for higher education based on the deep learning. Firstly, the functional block diagram of the system was built up. Next, a face detection algorithm was proposed based on the multi-task convolutional neural network, a face recognition algorithm was developed based on the improved deep convolutional neural network, and the knowledge learning status of students was tracked based on the memory augmented neural network. Finally, the proposed framework was proved effective and swift through experiments. The research results expand the application scope of the deep learning in education.

Highlights

With the rapid development of the Internet technology and the popularization of smart terminals, the digital and information-based educational methods are constantly improving
Considering the intelligent education system for higher education is applied in college classrooms, in order to ensure the face monitoring accuracy of the system under various interference factors such as different lighting or students lowering their heads or blocking out each other, the experimental data set for the face detection module consisted of randomly shot videos by surveillance cameras in 40 ordinary college and university classrooms, with the video of each classroom lasting for 60 seconds
The training, validation and test image sets used by the face recognition module consisted of about 26,000 facial images obtained through cropping of the students’ facial images detected by the face detection module, of which 18,000 formed the training set, while the other 8,000 formed the validation and test sets

Summary

Introduction

With the rapid development of the Internet technology and the popularization of smart terminals, the digital and information-based educational methods are constantly improving. Through review of the existing research results, it can be found that scholars at home and abroad seldom consider the particularity and functional advantages of the intelligent education platform system framework They have not built any application platform that can solve specific teaching problems, nor have they developed corresponding application models [18,19,20,21,22]. The server performs student face detection, pose recognition and identity recognition on the framed pictures and uploads the results to the database It analyzes the knowledge status based on the log data of the interactions between students and the intelligent education system like learning and exercise data, and completes the performance statistics and curve plotting of participants in daily teaching

Face detection module

Face recognition module

Experimental Results and Analysis

Conclusion