AI-Driven Personalized Learning: Predicting Academic Per-formance Through Leadership Personality Traits

TL;DR

This paper demonstrates that machine learning models, especially Random Forest, can accurately predict students' academic performance based on leadership personality traits, enabling personalized educational strategies.

Contribution

It introduces a novel approach combining leadership personality traits and ML algorithms to predict academic success, with the highest accuracy achieved by RF models.

Findings

RF classifier achieved 87.50% accuracy with 17 traits.

Personality traits significantly correlate with academic performance.

Model can identify student strengths and weaknesses early.

Abstract

The study explores the potential of AI technologies in personalized learning, suggesting the prediction of academic success through leadership personality traits and machine learning modelling. The primary data were obtained from 129 master's students in the Environmental Engineering Department, who underwent five leadership personality tests with 23 characteristics. Students used self-assessment tools that included Personality Insight, Workplace Culture, Motivation at Work, Management Skills, and Emotion Control tests. The test results were combined with the average grade obtained from academic reports. The study employed exploratory data analysis and correlation analysis. Feature selection utilized Pearson correlation coefficients of personality traits. The average grades were separated into three categories: fail, pass, and excellent. The modelling process was performed by tuning seven ML algorithms, such as SVM, LR, KNN, DT, GB, RF, XGBoost and LightGBM. The highest predictive performance was achieved with the RF classifier, which yielded an accuracy of 87.50% for the model incorporating 17 personality trait features and the leadership mark feature, and an accuracy of 85.71% for the model excluding this feature. In this way, the study offers an additional opportunity to identify students' strengths and weaknesses at an early stage of their education process and select the most suitable strategies for personalized learning.