基于ANN和Boosting的教学质量评价方法  

Evaluation of Teaching Quality Based on ANN and Boosting

作　　者：崔荣一[1] 赵亚慧[1] 崔旭[1] 尹哲峰[2] 张振国[1] CUI Rongyi;ZHAO Yahui;CUI Xu;YIN Zhefeng;ZHANG Zhenguo(College of Engineering;Educational Technology Center,Yanbian University,Yanji 133002,China)

机构地区：[1]延边大学工学院,吉林延吉133002 [2]延边大学教务处,吉林延吉133002

出　　处：《吉林大学学报（信息科学版）》2018年第5期545-552,共8页Journal of Jilin University（Information Science Edition）

基　　金：吉林省高等教育教学改革立项基金资助项目(吉教高字[2012]45号);吉林省高等教育学会高教科研基金资助项目(吉高学会[2017]9号)

摘　　要：针对学校教学质量传统评价方法中存在的线性化、静态化等缺陷,提出了更加客观、合理的智能评价方法。利用人工神经网络设计了智能评价器的方案,实现了评价因素到结果的非线性映射,采用不同时期发生的评价数据,通过机器学习方法确定当前阶段各评价因素对评价结果的作用参数,使动态权重能够适应不同时期的评价取向。评价器采用提升算法(Boosting)集成3个子评价器形成智能评价器,并通过多评价器集成运行规则进行智能评价。实践证明此方法相比传统的评价方法更能反映因素与结果之间的复杂关系及其动态特征,获得良好的评价结果。To overcome the shortcomings of traditional approach to teaching quality evaluation of university, a more objective and reasonable intelligent evaluation method is proposed. According to the shortcomings of linear and static properties of traditional evaluation model, an implementation scheme of intelligent evaluator is proposed using ANN （Artificial Neural Network ）. The training sample set of the intelligent evaluator is constructed with the data produced by traditional model. The 3 sub-evaluators are integrated with boosting to compose the intelligent evaluator, and the running rules of the evaluator is described. It is verified by practice that the proposed method better reflects the complex and dynamic relation between factors and results, and produces good evaluation results.

关 键 词：人工神经网络 提升算法 教学质量评价 非线性动态模型 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]