改进AlexNet网络模型在课堂教学质量监控评估中的应用  

Application of improved alexnet network model in classroom teaching quality monitoring and evaluation

作　　者：张明文 马启龙 赵国军 王建华[2] Zhang Mingwen;Ma Qilong;Zhao Guojun;Wang Jianhua(Gansu Normal University for Nationalities Department of Educational Science,Gansu Hezuo,747000,China;Shannxi Baoji Vocational&Technical College Fengxiang Normal College,Shannxi Baoji 721000,China)

机构地区：[1]甘肃省民族师范学院教育科学系,甘肃合作747000 [2]陕西省宝鸡职业技术学院凤翔师范学院,陕西宝鸡721000

出　　处：《现代科学仪器》2020年第6期177-182,共6页Modern Scientific Instruments

基　　金：陕西省体育局常规课题,编号2018059。

摘　　要：构建高校教学质量监控体系是高等教育改革的切实有效手段,课堂教学监控是高校教学质量监控体系的重要组成部分。运用改进AlexNet网络人工智能技术,应用于高校课堂教学监控,从课前考勤到课堂状态,利用信息化手段对学生以及教师进行全方位监控。实际应用研究结果显示:对96名学生、1200条记录数据的关联分析,学生上课低头次数超3次,上课玩手机,其期末成绩不及格的概率分别约为85.7%,75%。利用机器学习相关算法对考勤及课堂状态数据与期末成绩进行分析研讨,为提高课堂教学质量提供思路。The construction of teaching quality monitoring system in Colleges and universities is a practical and effective means of higher education reform,and classroom teaching monitoring is an important part of teaching quality monitoring system in Colleges and universities.Using the improved alexnet network artificial intelligence technology,it is applied to the classroom teaching monitoring in Colleges and universities.From the attendance before class to the classroom state,it uses information technology to monitor students and teachers in an all-round way.The results of practical application research show that:for the correlation analysis of 96 students and 1200 recorded data,the probability of students bowing their heads for more than three times in class and playing mobile phones in class is about 85.7%and 75%respectively.Using machine learning algorithm to analyze and discuss the data of attendance and classroom state and final grade,in order to improve the quality of classroom teaching.

关 键 词：改进AlexNet 教学质量监控 关联规则分析 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]