Research on the Factors Determining the Chance of Graduate Admission

Authors

  • Yansong Li

DOI:

https://doi.org/10.54097/564wf468

Keywords:

Linear model; chances of admissions; application for master’s degrees.

Abstract

Nowadays, due to various reasons, more students are beginning to pursue higher academic degrees, such as master's degrees. This article discusses the factors that influence the chances of graduate admissions. The aim is to quantify each determinant by establishing a linear model, thus helping everyone understand how much each factor can affect the admission chance. The dataset used in this article comes from the Kaggle, which includes eight variables and 400 observations. This article establishes several multiple linear models using the smallest AIC selection, the smallest BIC selection, and the LASSO selection. Models are screened based on some indicative values such as R2adj, SSres, and R2. After establishing the final model, assumptions (Normality, homoscedasticity, multicollinearity, linear relationship) and prediction errors are checked to verify the model's effectiveness. The article ultimately finds that every predictor positively correlates with the admission chances. It means that the more achievements an applicant has, the higher the chance of admission. This conclusion is consistent with our initial hypothesis. The final results can help applicants understand the importance of each application material (predictor). By inputting their existing achievements for each predictor into the model, they can predict their chances of admission, identify deficiencies, and work on improvements.

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References

Li F, John M W, Ding X. The expansion of higher education, employment and over-education in China. International Journal of Educational Development, 2008, 28(6): 687–697.

Mowjee B. Are Postgraduate Students ‘Rational Choosers’? An Investigation of Motivation for Graduate Study Amongst International Students in England. Research in Comparative and International Education, 2013, 8(2): 193–213.

Zimmermann J, von Davier A A, Buhmann J M, Heinimann H R. Validity of GRE General Test scores and TOEFL scores for graduate admission to a technical university in Western Europe. European Journal of Engineering Education, 2018, 43(1): 144–165.

Kuncel N R, Kochevar R J, Ones D S. A Meta-analysis of Letters of Recommendation in College and Graduate Admissions: Reasons for hope. International Journal of Selection and Assessment, 2014, 22(1): 101–107.

Kuncel N R, Credé M, Thomas L L. A Meta-Analysis of the Predictive Validity of the Graduate Management Admission Test (GMAT) and Undergraduate Grade Point Average (UGPA) for Graduate Student Academic Performance. Academy of Management Learning & Education, 2007, 6(1): 51–68.

Esmeraldo G, et al. Using Genetic Programming and Linear Regression for Academic Performance Analysis. Industry and Innovation Tracks, Practitioners’ and Doctoral Consortium, 2018, 174–179.

Shawwa L A, et al. Factors potentially influencing academic performance among medical students. Advances in Medical Education and Practice, 2015, 65–75.

Arsad P M, Buniyamin N, Manan J A. Prediction of engineering students’ academic performance using Artificial Neural Network and Linear Regression: A comparison. 2013 IEEE 5th Conference on Engineering Education (ICEED), 2013, 43–48.

Froud R, Hansen S H, Ruud H K, Foss J, Ferguson L, Fredriksen P M. Relative performance of machine learning and linear regression in predicting quality of life and academic performance of school children in Norway: Data analysis of a quasi-experimental study. Journal of Medical Internet Research, 2021, 23(7): e22021–e22021.

Huang S, Fang N. Predicting student academic performance in an engineering dynamics course: A comparison of four types of predictive mathematical models. Computers and Education, 2013, 61(1): 133–145.

Acharya M S. Graduate admission 2. Kaggle, 2018.

Mohan S Acharya, Asfia Armaan, Aneeta S Antony. A Comparison of Regression Models for Prediction of Graduate Admissions. IEEE International Conference on Computational Intelligence in Data Science, 2019.

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Published

29-03-2024

How to Cite

Li, Y. (2024). Research on the Factors Determining the Chance of Graduate Admission. Highlights in Science, Engineering and Technology, 88, 1016-1023. https://doi.org/10.54097/564wf468