Yantai Nanshan University, Yantai, Shandong 265713, China.
J Environ Public Health. 2022 Aug 31;2022:2541576. doi: 10.1155/2022/2541576. eCollection 2022.
Teaching evaluation is a comprehensive judgment of teachers' teaching effect and students' learning outcomes. It is an essential basis for comprehensive curriculum reform. There are many teaching evaluation systems for Korean majors, generally based on teachers' behavior discrimination and ignoring students' learning process and effect. The existing teaching evaluation system has problems such as heavy workload, slow calculation speed, and intense subjectivity. Based on the characteristics of Korean courses, this study constructs a teaching rating system for Korean courses in universities centered on language learning through data collection, correlation analysis, association rules, and other methods to optimize the student teaching evaluation index. At the same time, the machine learning algorithm is introduced into the teaching evaluation process to construct the teaching evaluation model and realize the automation of the teaching evaluation process. The weighted Bayesian incremental learning method is used to solve the cumulative problem of data acquisition samples. The experimental results show that the accuracy rate of classification using the weighted naive Bayesian algorithm to construct the model can reach 75%. Obviously, due to the traditional Bayesian algorithm and BP neural network algorithm, it is suitable for the teaching evaluation model of Korean majors. It provides a theoretical basis for the development of language education informatization.
教学评价是对教师教学效果和学生学习成果的综合判断，是全面课程改革的重要依据。韩国专业有许多教学评价体系，一般基于教师的行为甄别，而忽略了学生的学习过程和效果。现有的教学评价体系存在工作量大、计算速度慢、主观性强等问题。本研究基于韩语课程的特点，通过数据采集、相关分析、关联规则等方法，构建以语言学习为中心的高校韩语课程教学评价体系，优化学生教学评价指标。同时，将机器学习算法引入教学评价过程，构建教学评价模型，实现教学评价过程的自动化。采用加权贝叶斯增量学习方法解决数据采集样本的累积问题。实验结果表明，采用加权朴素贝叶斯算法构建模型的分类准确率可达到 75%。显然，由于传统的贝叶斯算法和 BP 神经网络算法，它适用于韩语专业的教学评价模型，为语言教育信息化的发展提供了理论依据。
