DocumentCode
2563008
Title
Academic Performance Predictors
Author
Cheng Lei ; Kin Fun Li
Author_Institution
Dept. of Electr. & Comput. Eng., Univ. of Victoria, Victoria, BC, Canada
fYear
2015
fDate
24-27 March 2015
Firstpage
577
Lastpage
581
Abstract
The ability to predict one´s academic performance is a great asset for both the students and the institution administrators. For the students, they can adjust workload, career direction, etc. If they are aware of their capability. For the administrators and instructors, early warnings would facilitate intervention thus enabling a more successful academic environment. In addition, institutional resources can be utilized in an optimal way thus gaining operation efficiency. This work surveys existing literature in student academic performance prediction. Parameters used for predictions are examined. Useful predictors are identified.
Keywords
educational administrative data processing; educational institutions; academic environment; academic performance predictors; career direction; institution administrators; institutional resources; operation efficiency; student academic performance; Computational modeling; Computers; Conferences; Data mining; Education; Neural networks; Predictive models; academic performance; performance metrics; student classification; success predictors;
fLanguage
English
Publisher
ieee
Conference_Titel
Advanced Information Networking and Applications Workshops (WAINA), 2015 IEEE 29th International Conference on
Conference_Location
Gwangiu
Print_ISBN
978-1-4799-1774-7
Type
conf
DOI
10.1109/WAINA.2015.114
Filename
7096239
Link To Document