Effects of personality traits in predicting grade retention of Brazilian students

TL;DR

This study investigates how students' personality traits can predict grade retention in Brazil, demonstrating that personality information improves prediction accuracy beyond socioeconomic and test data.

Contribution

It introduces a machine learning approach that quantifies the predictive power of personality traits for grade retention in a Brazilian student sample.

Findings

Personality traits improve retention prediction accuracy.

Personality traits outperform random classifiers.

Traits contribute alongside socioeconomic and test data.

Abstract

Student's grade retention is a key issue faced by many education systems, especially those in developing countries. In this paper, we seek to gauge the relevance of students' personality traits in predicting grade retention in Brazil. For that, we used data collected in 2012 and 2017, in the city of Sertaozinho, countryside of the state of Sao Paulo, Brazil. The surveys taken in Sertaozinho included several socioeconomic questions, standardized tests, and a personality test. Moreover, students were in grades 4, 5, and 6 in 2012. Our approach was based on training machine learning models on the surveys' data to predict grade retention between 2012 and 2017 using information from 2012 or before, and then using some strategies to quantify personality traits' predictive power. We concluded that, besides proving to be fairly better than a random classifier when isolated, personality traits contribute to prediction even when using socioeconomic variables and standardized tests results.