학술논문

On the Design of Student Assessment Model Based on Intelligence Quotient Using Machine Learning
Document Type
Periodical
Source
IEEE Access Access, IEEE. 10:48733-48746 2022
Subject
Aerospace
Bioengineering
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Engineered Materials, Dielectrics and Plasmas
Engineering Profession
Fields, Waves and Electromagnetics
General Topics for Engineers
Geoscience
Nuclear Engineering
Photonics and Electrooptics
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Transportation
Companies
Support vector machines
Standards
Remuneration
Machine learning algorithms
Data mining
Reliability
Intelligence quotient (IQ)
student assessment
academic performance
machine learning
data mining
Language
ISSN
2169-3536
Abstract
The goal of this research is to figure out how to calculate academic achievements and students’ cognitive quotients for placement. This study will attempt to forecast students’ intelligence quotients or academic grades to measure the IQ of a student in a holistic manner using all kinds of parameters, from students’ academic records to input from their professors and even their family background, thus creating a dataset of 9000 instances with all these data. We implemented and trained multiple machine learning algorithms on the data and collected the outcomes to select the best algorithm. Students’ quantitative reasoning ability was selected as a parameter that could be assessed by their performance on aptitude tests. Certifications of the student during their bachelor’s degree have been considered, which would also give us an idea about the student’s critical and logical thinking ability. All the parameters were rated on a scale of 1-10. The driving motivation behind this investigation was to discover what parameters force a student to be placed in a company then the final overall “student score” is calculated to determine a student’s intelligence quotient. The final IQ score of the student-generated was graded on a scale of 0–3 and a suitable salary package range for the student was estimated giving the company a good idea of the student’s capability.