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 :
بازگشت