Open AccessPerformance Based Prediction of the Students in the Physics Subject using Traditional and Machine Learning Approach at Higher Education Level
Author(s) -
Sarfraz Ahmad Ahmad,
Ishtiaq Hussain,
Rashid Ahmad,
Muhammad Naseer Ud Din
Publication year - 2020
Publication title -
international journal of innovation in teaching and learning
Language(s) - English
Resource type - Journals
eISSN - 2664-2247
pISSN - 2520-0003
DOI - 10.35993/ijitl.v6i1.997
Subject(s) - simple random sample , aptitude , personality , mathematics education , test (biology) , sample (material) , task (project management) , population , psychology , correlation , subject (documents) , computer science , artificial intelligence , machine learning , social psychology , mathematics , developmental psychology , engineering , medicine , paleontology , chemistry , geometry , environmental health , systems engineering , chromatography , library science , biology
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