统计学习算法在高校教学质量评估中的应用研究

本文分析了机器学习算法在教学质量评估中应用的可行性，并以带权间隔支持向量回归模型WMSVR（Weighed Margin Support Vector Regression）。本文对教学质量评估的众多分量进行训练学习，以建立稳定描述教师教学质量的机器学习模型。该模型输入量化的教学质量评估指标，引入具有可信程度的学生意见信息，并以带权间隔来表示样本的置信度，以WMSVR模型作为训练器，模型输出数量化的教学质量指标。对比专家对教师的教学活动的评价表明：WMSVR模型在教学质量评估中有较高的准确度和泛化能力，在语义上有足够表达教学质量指标体系的能力。

Research of Application of Machine Learning Theory in Teaching Quality Evaluation

In this paper,we discuss the feasibility of application of machine learning algorithm in teaching quality evaluation,and use weighted margin support vector machine(WMSVM) model in learning many metrics of teaching quality evaluation in order to build up a stable model to evaluate teaching quality.Teaching quality metrics are input to the model and confidence value of attitude information of students.In our model,this confidence is expressed as weighted margin between the sample points and the classification hyper plane.The WMSVR model outputs teaching quality metric as scores.Comparing these scores with experts' evaluation,we find that WMSVR model is more accurate and of better generalization capability in the procedure of teaching quality evaluation.