Machine Learning Reveals Which Personality Traits Drive Grit in Ghanaian Trainee Teachers

In a classroom in the Volta Region of Ghana, future teachers are learning a lesson that has little to do with lesson planning: what actually keeps a student going when resources are scarce, schedules shift without warning, and the path to a career in education is long and demanding. A new study of 288 trainee teachers at Jasikan College of Education suggests that the answer lies in two specific personality traits, and that the usual hierarchy of traits familiar from decades of Western research may need revising for resource constrained settings.

The research, published in Discover Education by Akoto-Baako Hansen of the University of South Africa, set out to answer a deceptively simple question: which of the Big Five personality traits predict grit, the combination of perseverance and sustained interest in long term goals that psychologists have linked to academic attainment, retention and wellbeing. Grit has become one of the most studied noncognitive skills in education, but the evidence for which personality dispositions give rise to it has been inconsistent, particularly outside well resourced Western institutions.

What makes the study methodologically notable is its analytical machinery. Rather than relying on conventional regression, Hansen applied a family of techniques drawn from econometrics known as double machine learning and post double selection lasso. In these procedures, the lasso algorithm, which shrinks weak predictors to zero, is used purely as a variable selection and orthogonalisation device. After the algorithm identifies which covariates matter, the final coefficients and standard errors are estimated through ordinary least squares, preserving the interpretability of classical statistics while guarding against omitted variable bias and overfitting. Three complementary estimators were used, and a genuinely predictive, cross validated lasso model served as a convergent check.

The results were strikingly consistent across every specification. Openness to experience, the trait associated with intellectual curiosity, imagination and willingness to embrace new experiences, emerged as the strongest predictor of grit, with a standardised coefficient of roughly 0.55 across the inferential models. Conscientiousness, the trait of discipline, organisation and goal directed effort, followed closely with coefficients around 0.41. Extraversion, agreeableness and neuroticism showed no meaningful association with grit in any specification, with confidence intervals spanning zero throughout.

The ordering matters. Meta analytic evidence drawn largely from Western populations has consistently identified conscientiousness as the dominant correlate of grit, with openness in a supporting role. In this Ghanaian sample, the hierarchy is reversed. The study offers a theoretical explanation grounded in Trait Activation Theory, which holds that personality traits remain latent until situational cues in the environment prompt their expression. In a teacher training college marked by limited teaching resources, large class sizes and unpredictable institutional conditions, the argument goes, those constraints act as powerful cues that activate adaptability and curiosity rather than routine discipline.

In other words, a highly organised but rigid student may struggle when power outages interrupt study sessions, learning materials arrive late, or timetables change abruptly. A student high in openness can improvise alternative strategies, treat obstacles as puzzles rather than barriers, and sustain motivation under uncertainty. Five Factor Theory complements this reading by treating grit as a characteristic adaptation, a habit shaped jointly by underlying dispositions and environmental demands. In this context, the adaptation draws more heavily on flexible curiosity than on structured self-discipline.

The descriptive statistics reinforce how distinctive this population is. Trainees reported high average grit, around 4.26 on a five point scale, and the distribution was strongly negatively skewed, with most students clustered at the upper end. Grit correlated strongly with openness and conscientiousness, with correlation coefficients of 0.62 and 0.56 respectively, magnitudes that exceed many reported in Western meta analyses. The intercorrelations among the five traits were low to moderate, easing concerns that the findings were an artefact of overlapping measures.

The predictive lasso model added a further layer of confirmation. Trained on a subsample and tuned through tenfold cross validation, the model retained nine of fourteen candidate covariates and explained close to 47 percent of the variance in grit scores on withheld data. Under this stricter predictive criterion, agreeableness and neuroticism were not merely statistically weak but dropped entirely from the model, receiving coefficients of exactly zero. Openness and conscientiousness survived with the largest coefficients, reinforcing the conclusion that these two traits carry the dominant association with grit in this setting.

The null results are themselves informative. The absence of a neuroticism effect departs from meta analytic findings that emotional instability typically undermines perseverance, and from a Turkish study reporting a negative association. The author suggests that the relationship may be nonlinear, or that Ghanaian trainees develop coping strategies that buffer the effects of emotional sensitivity. Similarly, the lack of an extraversion effect may reflect the collectivist cultural context, where the advantages of sociability for personal goal pursuit are less pronounced than in individualist settings.

For practitioners, the implications are concrete. Interventions aimed at building grit among prospective teachers in Ghana should prioritise cultivating openness, through problem based learning, exposure to diverse perspectives and independent exploration, alongside conscientiousness, through goal setting and self regulation training, rather than attempting to alter traits that appear irrelevant to persistence in this context. The study also offers a methodological template for educational psychology, demonstrating that machine learning tools can be harnessed for rigorous causal inference rather than mere prediction. The author cautions that the cross sectional design, the single institution sample and the reliance on self report limit generalisability, and calls for longitudinal and nonparametric follow ups. But the core message stands: the personality foundations of grit are not universal, and in resource constrained classrooms, curiosity may matter more than discipline.

Subject of Research: Predicting grit from Big Five personality traits among Ghanaian college students using regularisation-assisted machine learning methods

Article Title: Machine learning in predicting the effects of personality traits on college students’ grit in Ghana

Article References: Hansen, A.-B. (2026). Machine learning in predicting the effects of personality traits on college students’ grit in Ghana. Discover Education, 5(1), Article 1107. https://doi.org/10.1007/s44217-026-02243-w

Image Credits: AI Generated

DOI: 10.1007/s44217-026-02243-w

Keywords: grit, Big Five personality traits, machine learning, double machine learning, lasso, teacher trainees, Ghana, openness, conscientiousness, educational psychology, Trait Activation Theory, noncognitive skills