TY - GEN
T1 - Research on the relationship between undergraduate learning and employment
AU - Qu, Shaojie
AU - Qin, Huidong
AU - Yue, Jiaqi
AU - Xu, Fangyao
AU - Yang, Yi
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021/8/17
Y1 - 2021/8/17
N2 - 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.
AB - 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.
KW - Association analysis
KW - Classification algorithms
KW - Education data mining
UR - http://www.scopus.com/inward/record.url?scp=85118920174&partnerID=8YFLogxK
U2 - 10.1109/ICCSE51940.2021.9569534
DO - 10.1109/ICCSE51940.2021.9569534
M3 - Conference contribution
AN - SCOPUS:85118920174
T3 - ICCSE 2021 - IEEE 16th International Conference on Computer Science and Education
SP - 37
EP - 41
BT - ICCSE 2021 - IEEE 16th International Conference on Computer Science and Education
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 16th IEEE International Conference on Computer Science and Education, ICCSE 2021
Y2 - 17 August 2021 through 21 August 2021
ER -