Analysis of Teaching Effect of Korean Education Course Based on Data Acquisition Technology

Abstract

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.