摘要
作为确保和促进高等教育教学质量不断提高的重要手段之一,教学质量评价正在被广泛研究。有效处理和分析高校教学过程中收集到的庞大原始数据,能够为教学质量评估及其相关改进措施的制定提供决策支持。针对原始教学数据多样性且数量大的特点,提出了一种使用深度学习技术的教学质量评估模型,采用MatConvNet构建深度神经网络,对多种原始数据进行有机融合,能够对教学质量实现比较准确的评估,有一定的实用价值。
As one of the key methods for ensuring and improving the teaching quality of university,teaching quality assessment has been widely studied.Effectively processing and analyzing the big original dataset collected during teaching procedure can provide the basis of decision support for teaching quality assessment and policy making.Considering the diversity and huge volume of original teaching data,this paper proposes a teaching quality assessment model based on deep learning technology,in which a deep neural network is implemented by MatConvNet and applied for data fusion.The model achieves good performance in university teaching quality assessment which illustrates its application value.
作者
张钢
Zhang Gang(Guangdong University of Technology, Guangzhou 510006, Guangdong)
出处
《电脑与电信》
2017年第10期6-9,共4页
Computer & Telecommunication
基金
广东省自然科学基金项目
项目编号:2016A030310340
广东工业大学高教研究基金项目
项目编号:2016GJ12
广东省教育评估协会2015年度研究课题
项目编号:G-11
关键词
深度学习
教学质量评价
深度神经网络
数据融合
deep learning
teaching quality assessment
deep neural network
data fusion
