Improving Prediction Through Machine Learning

Authors:
Publisher website
Google Scholar
Abstract
In psychological assessment, gauging the impact of personality traits on academic outcomes is vital. Many studies explore the relation between academic achievement and traits like conscientiousness but prioritize description over prediction. Addressing this gap by focusing on actual prediction can refine assessment methodologies and deepen theoretical understanding. Our study focuses on predicting the influence of conscientiousness facets on standardized test scores using various machine learning strategies. Data from N = 7,949 Luxembourgish Grade 9 students showed a gradient boosting model with item-level predictors outperformed traditional linear regression ( R2 = .123 vs. R2 = .077). This model revealed both linear and nonlinear ties between conscientiousness facets and achievement. Our findings accentuate conscientiousness’s underestimated predictive power for academic success and advocate for machine learning as a pivotal tool in psychological testing, particularly for outcome prediction.

This publication has 30 references indexed in Scilit: