基于评价主体多元化理念和卷积神经网络的线上教学质量评价方法

Method of online teaching quality evaluation based on concept of multiple evaluation subjects and convolutional neural network

受政策因素和技术环境影响,线上教学已成常态化。为改进已有线上教学质量评价体系的不足,提升线上教学质量,文中以高校线上教学为研究对象,利用人工智能技术提出一种线上教学质量评价方法。首先,基于科学的教学评价理念和原则,充分考虑已有的评价指标和线上教学特点,建立包含多元化评价主体的评价指标体系;然后,利用随机森林方法对评价指标进行重要性评估,达到数据降维和教学反馈的目的;再采用卷积神经网络构建以学生评价、教师自评、同行评价、平台数据为输入,以专家评结果为输出的评价模型;最后,将所提出的线上教学质量评价体系和模型应用于教学实践。实践结果表明,所提方法能够有效评价高校线上教学质量并降低评价成本,具有较高的准确性和应用价值。

Online teaching has become the norm due to the influences of policy factors and technological environment.Taking online teaching in colleges as the research object,an online teaching quality evaluation method is proposed by means of the artificial intelligence technology to perfectthe the existing online teaching quality evaluation system and improve the online teaching quality.On the basis of scientific teaching evaluation concept and principles,an evaluation index system containing the diversified evaluation subjects is established in consideration of the existing evaluation indicators and online teaching characteristics.The random forest method is used to evaluate the importance of the evaluation indicators,so as to achieve the purposes of data reduction and teaching feedback.The convolutional neural network is used to construct an evaluation model which takes student evaluation,teacher self⁃evaluation,peer evaluation and platform data as input and expert evaluation results as output.The teaching quality evaluation index system and model have been already applied to teaching practice.The practical results show that the proposed method can effectively evaluate the quality of online teaching in colleges and reduce the evaluation cost,which has high accuracy and application value.