A Vision sensing-based automatic evaluation method for teaching effect based on deep residual network

Author:

Sun Meijuan12

Affiliation:

1. Kansas International School, Sias University, Zhengzhou 451100, Henan, China

2. Center of Research on Sino-American Cooperation in Running Schools of Sias University, China

Abstract

<abstract><p>The automatic evaluation of the teaching effect has been a technical problem for many years. Because only video frames are available for it, and the information extraction from such dynamic scenes still remains challenging. In recent years, the progress of deep learning has boosted the application of computer vision in many areas, which can provide much insight into the above issue. As a consequence, this paper proposes a vision sensing-based automatic evaluation method for teaching effects based on deep residual network (DRN). The DRN is utilized to construct a backbone network for sensing from visual features such as attending status, taking notes, playing phones, looking outside, etc. The extracted visual features are further selected as the basis for the evaluation of the teaching effect. We have also collected some realistic course images to establish a real-world dataset for the performance assessment of the proposal. The proposed method is implemented on collected datasets via computer programming-based simulation experiments, so as to obtain accuracy assessment results as measurement. The obtained results show that the proposal can well perceive typical visual features from video frames of courses and realize automatic evaluation of the teaching effect.</p></abstract>

Publisher

American Institute of Mathematical Sciences (AIMS)

Subject

Applied Mathematics,Computational Mathematics,General Agricultural and Biological Sciences,Modeling and Simulation,General Medicine

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3