基于离散泊松混合模型的教学评价数据建模

分析学生在教学评价系统中对于教师的评价数据有助于教师了解学生对授课教师的真实态度，总结教学经验，改进后续的教学方式，提高教学质量。但是进行教学评价时，学生中可能会出现随意评价或者恶意评价等问题，导致评价数据中包含大量噪声，造成反馈数据的不理想。因此，提出了一种离散泊松混合模型来对包含噪声的学生的评价数据进行建模，将混合模型中的每一个离散泊松分量对应一类具有相似评价模式的学生，借由离散泊松分布中的模型参数来表示对应评价模式中的评价分数。通过构建对数似然函数来衡量混合模型和评价数据的拟合程度，采用梯度下降的方法求解拟合程度最高的模型参数，找到学生对于教师的真实评价，保证教学评价系统中师生间的有效沟通。大量实验结果表明，模型能够快速准确地从含有噪声的评价数据中识别出具有不同评价模式的学生，掌握学生对于教师的真实评价情况。

Teaching evaluation data modeling based on discrete Poisson mixture model

Analyzing the evaluation data of students to teachers in the teaching evaluation system helps teachers understand the true attitudes of students to teachers, summarize teaching experience, improve subsequent teaching methods, and improve teaching quality. However, when evaluating teaching, random or malicious evaluations may occur among students, resulting in a large amount of noise in the evaluation data, which results in unsatisfactory feedback data. Therefore, this paper proposed a discrete Poisson mixture model to model the evaluation data of students with noise. Each discrete Poisson component in the mixture model corresponds to a class of students with similar evaluation modes. The model parameters in the loose distribution represented the evaluation scores in the corresponding evaluation mode. It constructed the log-likelihood function to measure the degree of fit between the mixed model and the evaluation data, and used the gradient descent method to solve the model parameters with the highest degree of fit, to find the true evaluation of the students to the teacher, and to ensure the teacher-student relationship in the teaching evaluation system communicate effectively. A large number of experimental results show that the proposed model can quickly and accurately identify students with different evaluation modes from the evaluation data containing noise, and grasp the true evaluation of the students to teachers.