The Evaluation of Online Education Course Performance Using Decision Tree Mining Algorithm

Abstract

With the continuous development of “Internet + Education”, online learning has become a hot topic of concern. Decision tree is an important technique for solving classification problems from a set of random and unordered data sets. Decision tree is not only an effective method to generate classifier from data set, but also an active research field in data mining technology. The decision tree mining algorithm can classify the data, grasp the teaching process of the teacher, and analyze the overall performance of the students, so as to realize the dynamic management of the educational administration and help the educational administration personnel to make the right decision, with more reasonable allocation of resources. This paper evaluates students’ academic performance based on the learning behavior data of online learning, so as to intervene in students’ learning in advance, which is the key problem that needs to be solved at present. Taking students’ learning attitude, completion of homework, and attendance as factors, the paper uses decision tree technology to analyze the factors affecting students’ performance, and evaluates students’ performance. Firstly, this paper collects the high-dimensional behavioral characteristic data of students’ online learning and conducts correlation analysis after preprocessing the behavioral characteristic data. Then, the decision tree C4.5 algorithm is used to construct a performance evaluation model. Students’ performance is evaluated by the model, and the evaluation accuracy is about 88% compared with actual performance. Finally, through the model analysis, it is concluded that the video task point completion is the most influential in students’ achievement, followed by chapter test completion and chapter test average score, and the course interaction amount and homework average score are the least influential in students’ achievement, which has a practical reference value for effectively serving online learning and teachers’ teaching.

Educational Data Mining Using Cluster Analysis Methods and Decision Trees based on Log Mining

“…The formula for precision can be seen in equation (10). The higher the value of accuracy, recall, and precision, the better the classification performance of the model [31].…”

Implementation of Data Mining to Predict Student Study Period with Decision Tree Algorithm (C4.5)

“…This stage is the process of initial preparation and data cleaning. In the pre-processing stage, it is important to deal with incomplete data and to ensure the completeness of the data to maintain the integrity and stability of the data [18]. The purpose of this process is to clean the dataset by identifying and addressing potential errors and inconsistencies in the data that may exist.…”

A Perspective on the Current Situation of Children’s Education and Teachers’ Moral Cultivation Combined with Decision Tree Algorithm

“…With the development of information technology, schools have built up various management application platforms with databases as the core, which have accumulated a large amount of data after years of operation [1][2]. However, very often, the utilization rate of these data is not high, and the application of these data is still stuck in the initial system design stage; how to extract and use these big data is a popular direction of current research [3].…”