Students' Attention Assessment in eLearning based on Machine Learning

2018 1st International Conference on Environment Prevention and Pollution Control Technology

生态环境科学

Deng, Qingshan^1 ; Wu, Zhili^1

School of Software and Internet of Things Engineering, Jiangxi University of Finance and Economics, Nanchang Jiangxi

330013, China^1

关键词: Automatic classification;    Electronic learning (e-learning);    Gabor wavelets;    Knowledge transfer;    Multimedia applications;    On-machines;    Real applications;    State feature;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/199/3/032042/pdf
DOI  :  10.1088/1755-1315/199/3/032042

学科分类：环境科学（综合）

来源: IOP

【 摘 要 】

Multimedia-based Electronic learning (eLearning) is an effective method of knowledge transfer. It provides the opportunity that students can use the videos or other materials at any time after they are delivered. Multimedia applications provide convenience, but there also exist challenges. One of the challenges is to measure and assess students' attention when they are studying online. This paper presents a framework based on machine learning methods for the measurement of students' attention. The framework employs a Gabor wavelet to extract the eye state features and train the model using support vector machines (SVM) to complete automatic classification on students' eye states. Experiments over thousands of facial photos show that the proposed system reaches a good performance, which has a significant value in real applications.