Ismail Olaniyi Muraina, Moses Adeolu Agoi, Benjamin Oghomena Omorojor



Forecasting Students’ Job Placement using Data Science Paradigms

pdf PDF


Literatures revealed that so many sort of analysis available for quantitative and qualitative types of analysis in research. The essential focus of using data science models / approaches is to search for relevant information and detect weak links, which tend to make the model perform poorly. Data science paradigms have its broad relevance in many domains especially in education and more particularly in determining future job placement of students (young graduates) after graduation. The fundamental concepts of data science are by extracting knowledge from data to solve societal problems and by using knowledge of machine learning to extract data from a dataset and transform it into useful structure for further refined use. Students’ job placement after graduation is a major concern of all young graduates; so predicting students’ job placement after graduation through the use of performance in schools has opened different interpretations to researchers and academicians. Many young graduates are finding it difficult to obtain a worthy graduate grade that would provide him/her the job aiming at. The study aims at looking towards ensemble data science techniques or models that predicts students’ future job placement using CGPA or GPA of students while in the institution. The study as well puts forward the best predictive model among others. In order to realize this, previous year's student's historical data in form of gross point average was used as dataset for this research work. Decision tree algorithm, Support Vector Machine, and K- Nearest Neighbours algorithm were used for the study. The models applied showed that students’ future job placement could be predicted based on the previous data of such students. In doing this, the prediction would enhance student directional focus and helpful in making students to adjust to better determination if the predicted results go against the intending job in mind. The results also showed the strength and weakness of students towards the course of study and how they should prepare themselves for the future job placement if they are not yet in the right path. In total, prediction to make decision or to place students well after graduation is a critical issue and must be tackled with a well established model or algorithms using data science paradigms.


Data science, Algorithms, Prediction, Future Job Placement, Paradigms, Models


Cite this paper

Ismail Olaniyi Muraina, Moses Adeolu Agoi, Benjamin Oghomena Omorojor. (2022) Forecasting Students’ Job Placement using Data Science Paradigms. International Journal of Education and Learning Systems, 7, 27-34


Copyright © 2022 Author(s) retain the copyright of this article.
This article is published under the terms of the Creative Commons Attribution License 4.0