李育阳

摘  要： 高校教学质量是一项重要的高校水平评价指标，但是高校教学质量与多种影响因素相关，变化规律十分复杂，使得当前模型无法准确对高校教学质量进行评估。为了解决当前高校教学质量评估过程中存在的不足，以提高高校教学质量评估正确率，设计了基于数据挖掘算法的高校教学质量评估模型。该模型首先对当前高校教学质量评估的相关文献进行研究和分析，建立高校教学质量评估的影响因素;然后，采集高校教学质量影响因素数据，并通过专家确定高校教学质量等级，建立高校教学质量评估的学习样本;最后，引入数据挖掘技术的BP神经网络对学习样本进行训练，形成高校教学质量评估模型，并通过具体实例分析高校教学质量模型的优越性。结果表明，数据挖掘算法可以描述高校教学质量等级之间的差别，获得高精度的高校教学质量评估结果，而且高校教学质量评估误差要远小于当前典型的高校教学质量评估方法，优越性十分显著。

关键词： 高校教学管理; 质量评估; 影响因素; 数据挖掘算法; 学习样本; 模型通用性

中图分类号： TN02?34; TP391                     文献标识码： A                      文章编号： 1004?373X（2020）17?0119?04

Abstract： The university teaching quality is an important index of university level evaluation. It is related to many influencing factors， so its change rules are very complex， which makes the existing model unable to accurately evaluate the university teaching quality. In view of this， a teaching quality evaluation model based on data mining algorithm is designed to cope with the shortcoming existing in the process of university teaching quality evaluation and improve the evaluation accuracy. The relevant literature of current university teaching quality evaluation is analyzed， the influencing factors of university teaching quality evaluation is established， and the data of influencing factors of university teaching quality is collected. In addition， the teaching quality grade of universities are determined by experts to establish the learning samples of teaching quality evaluation. Finally， the BP neural network based on data mining technology is introduced to train the learning samples and generate the university teaching quality evaluation model. The advantages of the university teaching quality evaluation model are analyzed by specific examples. The results show that the data mining algorithm can be used to describe the differences among the teaching quality grades of universities and get accurate evaluation results. Moreover， the evaluation error of university teaching quality of the designed methods is far less than that of the current typical methods， so the designed model is of significant advantages.

Keywords： college teaching management; quality evaluation; influencing factor; data mining algorithm; learning sample; model commonality

0  引  言

隨着大学生数量的不断增加，教学质量在高校教育质量中的地位越来越明显，成为一个衡量高校教学效果和人才评价的指标，提高教学质量成为现在众多高校的共识[1?3]。在一所高校中，由于学科相当多，学科之间存在一定的交叉，教学手段灵活多样，高校教学质量评估变得相当复杂，是一个颇具挑战性的问题[4]。……