An Application of a decision tree and a knowledge map in guidance preparation of study and employment for bioengineering students

Abstract

According to China's national statistics, the number of college graduates in 2021, 2022, and 2023 are 9.09 million, 10.76 million, and 11.58 million, respectively. By 2024, the number of college graduates will reach an estimated 11.87 million. And in recent years, the employment market has been decelerated with layoffs in major companies. The supply of job seekers in the job market is far greater than the demand for jobs. College students encounter difficulty in employment, which aroused widespread concern from all walks of life. Bioengineering students are also facing a serious employment situation, and a large number of graduates in biology choose not to continue their further studies. A survey shows that the number of further study abroad and graduate students has accounted for 40% of the number of graduates. Many of them have entered the influx of research institutions in terms of talent demand and are also close to saturation. This means that the delayed graduation of biology graduates will face a more serious employment problem. In this situation, many scholars have studied the application of decision tree technology in university employment. More and more scholars design decision trees by decision algorithms to analyze and predict the employment situation of college graduates. In this study, first designed a knowledge map by acquiring knowledge through a survey of employment websites. Then design a questionnaire based on the literature by synthesizing the required attributes such as GPA, lab work, and scholarship. Then the C4.5 decision tree algorithm is used to analyze the collected graduate employment data and design a decision tree. Finally, the combination of the knowledge map and decision tree provides guidance preparation for bioengineering students' study and employment choices. With the knowledge map and decision tree, students' learning paths and countermeasures for future employment can be guided in a more targeted and timely manner to improve the employment rate of college students.