Research on the Relationship Between Undergraduate Learning and Employment

With the development of information technology, educational data mining is increasingly widely used in colleges and universities. We explored the relationship between students’ personal situations, course performance, and final graduation destination and examined the relationship between various course performances. We analyzed the data of 1572 software college students from 2011 to 2014. Firstly, based on the students’ academic performance and personal information, we predicted the students’ future after graduation. Secondly, we used random forest to mine features or courses that influenced employment. Thirdly, we used the Apriori algorithm to investigate the implicit relationship between students’ scores in different courses. Experiments showed that students’ employment direction can be predicted based on student information, and there was a correlation between course scores, which could provide reasonable guidance for students’ learning and employment and assist university educators.