Performance Based Prediction of the Students in the Physics Subject using Traditional and Machine Learning Approach at Higher Education Level

International Journal of Innovation in Teaching and Learning

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Field Value
 
Title Performance Based Prediction of the Students in the Physics Subject using Traditional and Machine Learning Approach at Higher Education Level
 
Creator Ahmad, Sarfraz Ahmad
Hussain , Ishtiaq
Ahmad, Rashid
Naseer Ud Din, Muhammad
 
Description In higher educational institutions, it is not an easy task to judge the performance of the students timely which is becoming more challenging. Although institutions have gathered a lot of data about their students. They do not have some specific methods to extract meanings from it. The main objective of this study was to find out the performance-based prediction of the students using their demographic and academic factors by using traditional and machine learning approaches. Graduates and undergraduate students studying in KUST were the population of the study. The study was delimited to the department of physics. A total of ninety graduate and undergraduate students were selected randomly using a simple random sampling technique as the entire sample.  The result indicated that percentage in matric (Correlation = 0.304), intermediate (Correlation = 0.245) and National Aptitude Test scores (Correlation = 0.480) found the best predictors. Further research was recommended to predict students’ academic performance by taking other aspects of the students like personality, cognitive, psychological, and economic domain for making a dataset of the features which may be used in machine learning approach which is more reliable to judge the academic performance of the students at the higher education level.
Keywords: Performance, Challenging, Demographic, Prediction, Examination
 
Publisher International Islamic University Islamabad, Pakistan
 
Date 2020-07-01
 
Type info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
 
Format application/pdf
 
Identifier http://irigs.iiu.edu.pk/ojs/index.php/IJITL/article/view/997
10.35993/ijitl.v6i1.997
 
Source International Journal of Innovation in Teaching and Learning (IJITL); Vol. 6 No. 1 (2020): International Journal of Innovation in Teaching and Learning; 174-190
2520-0003
2664-2247
 
Language eng
 
Relation http://irigs.iiu.edu.pk/ojs/index.php/IJITL/article/view/997/446
 
Rights Copyright (c) 2020 International Journal of Innovation in Teaching and Learning (IJITL)
http://creativecommons.org/licenses/by-nc/4.0
 

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