Traits versus Grades—The Incremental Predictive Power of Positive Psychological Factors over Pre-Enrollment Achievement Measures on Academic Performance

Beatrix Séllei,Roland Molontay,Nóra Stumphauser

doi:10.3390/app11041744

Abstract

Understanding what (and to what extent) psychological factors affect university performance has attracted a lot of research interest recently. In this paper, we use logistic regression models to study the incremental predictive power of positive psychological factors over pre-enrollment achievement measures on academic performance. The study is based on the data of 302 business and economics undergraduate students from the Budapest University of Technology and Economics. Coping proved to be the most important factor that sheds light on the importance of stress management for students. We also found that using properly chosen psychological factors measuring coping, personality traits, psychological immune system, emotional intelligence, and PERMA (P—positive emotion, E—engagement, R—relationships, M—meaning, A—accomplishments) factors, together with the university entrance score and academic performance can be predicted significantly better than solely relying on pre-enrollment achievement measures.

Highlights

High dropout rates and delayed completion in higher education is a moral and economic problem as well
University entrance score, as a composite score of various pre-enrollment achievement measures, has a strong predictive power on university success measured by the binary outcome variable, derived from the cumulative first-year grade point averages (FGPAs)
We study how the predictive power increases if we supplement the university entrance scores (UESs) with psychological factors

Summary

Introduction

High dropout rates and delayed completion in higher education is a moral and economic problem as well. It represents a cost for the government and society, an unnecessary expense for the family, and an experience of failure for the university student. The majority of the literature investigating university performance and dropout prediction relies on prior achievement indicators and personal details [1,2]. It was demonstrated in several studies that university performance can be predicted efficiently based on preenrollment achievement measures using sophisticated machine learning algorithms [3,4,5]. Nagy and Molontay achieved 73% accuracy in identifying future dropouts of the Budapest University of Technology and Economics [6]

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