TY - GEN
T1 - Work in Progress
T2 - 8th IEEE World Engineering Education Conference, EDUNINE 2024
AU - Shekhar, Prateek
AU - Khan, Md Tarique Hasan
AU - Gajjar, Sanjeet
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - Students' grade point average (GPA) is an important indicator of students' academic success. In our work-in-progress study, we utilized decision tree analysis to investigate patterns in predicting the GPA of engineering students, considering various demographic, socioeconomic, and academic aspects. Our analysis of the dataset consisting of engineering students' academic records revealed several key insights. First, SAT scores emerged as a central factor in GPA prediction, with higher scores predicting better GPAs. Second, socioeconomic status also became evident among students with high SAT scores, reflecting the impact of background characteristics on academic achievements. Lastly, parent education level also stood out as significant, showing that students with highly educated parents generally achieved higher GPAs, underlining family educational background's role in success. Overall, our research adds to the existing literature by illuminating the intricate factors influencing engineering students' GPA and provides an example of utilizing decision tree-based quantitative methods in engineering education research.
AB - Students' grade point average (GPA) is an important indicator of students' academic success. In our work-in-progress study, we utilized decision tree analysis to investigate patterns in predicting the GPA of engineering students, considering various demographic, socioeconomic, and academic aspects. Our analysis of the dataset consisting of engineering students' academic records revealed several key insights. First, SAT scores emerged as a central factor in GPA prediction, with higher scores predicting better GPAs. Second, socioeconomic status also became evident among students with high SAT scores, reflecting the impact of background characteristics on academic achievements. Lastly, parent education level also stood out as significant, showing that students with highly educated parents generally achieved higher GPAs, underlining family educational background's role in success. Overall, our research adds to the existing literature by illuminating the intricate factors influencing engineering students' GPA and provides an example of utilizing decision tree-based quantitative methods in engineering education research.
KW - decision tree analysis
KW - engineering education research
KW - GPA prediction
UR - http://www.scopus.com/inward/record.url?scp=85192014959&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85192014959&partnerID=8YFLogxK
U2 - 10.1109/EDUNINE60625.2024.10500587
DO - 10.1109/EDUNINE60625.2024.10500587
M3 - Conference contribution
AN - SCOPUS:85192014959
T3 - EDUNINE 2024 - 8th IEEE World Engineering Education Conference: Empowering Engineering Education: Breaking Barriers through Research and Innovation, Proceedings
BT - EDUNINE 2024 - 8th IEEE World Engineering Education Conference
A2 - Brito, Claudio da Rocha
A2 - Ciampi, Melany M.
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 10 March 2024 through 13 March 2024
ER -