Student academic performance using artificial intelligence
Joint Authors
Rashid, Tariq K.
Aziz, Nian Kh.
Source
ZANCO Journal of Pure and Applied Sciences
Issue
Vol. 28, Issue 2 (30 Jun. 2016), pp.56-69, 14 p.
Publisher
Salahaddin University-Erbil Department of Scientific Publications
Publication Date
2016-06-30
Country of Publication
Iraq
No. of Pages
14
Main Subjects
Information Technology and Computer Science
Abstract EN
This study is designed to find relationship between student's outcome of a particular course and their social backgrounds, previous achievements and the academic environments by using Artificial Intelligence.
Five hundred students from six departments at the Engineering College were participated in the research.
Firstly, the students have been tested before starting and after the completion of the course.
Then data about student's parental socio-economic status, tutors category; former high school scores, high school type and teaching languages have been collected.
The collected data has been pre-processed, cleaned, filtered, normalized and classified using Artificial Neural Network technique.
Initially, a 14 neuron neural network structure is proposed.
Then based on the classification and learning process, a modified model with 9 neurons is designed.
Each proposed methods are implemented and each is capable of performance predicting successfully.
American Psychological Association (APA)
Rashid, Tariq K.& Aziz, Nian Kh.. 2016. Student academic performance using artificial intelligence. ZANCO Journal of Pure and Applied Sciences،Vol. 28, no. 2, pp.56-69.
https://search.emarefa.net/detail/BIM-693376
Modern Language Association (MLA)
Rashid, Tariq K.& Aziz, Nian Kh.. Student academic performance using artificial intelligence. ZANCO Journal of Pure and Applied Sciences Vol. 28, no. 2 (2016), pp.56-69.
https://search.emarefa.net/detail/BIM-693376
American Medical Association (AMA)
Rashid, Tariq K.& Aziz, Nian Kh.. Student academic performance using artificial intelligence. ZANCO Journal of Pure and Applied Sciences. 2016. Vol. 28, no. 2, pp.56-69.
https://search.emarefa.net/detail/BIM-693376
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references : p. 68-69
Record ID
BIM-693376