用领导力人格特质预测学生学业表现，准确率达87.5%。
AI-Driven Personalized Learning: Predicting Academic Per-formance Through Leadership Personality Traits
- 基于17项人格特质与领导力评分，结合随机森林模型进行预测。
- 模型准确率最高达87.5%，可提前识别学习短板。
- 适合教育机构开展个性化教学干预，尤其关注非学术能力。
本研究探索人工智能在个性化学习中的应用，通过领导力人格特质与机器学习建模预测学业成功。数据来自环境工程专业129名硕士生，完成包含23个维度的五项领导力测评（包括Personality Insight、Workplace Culture、Motivation at Work、Management Skills、Emotion Control）。测试结果与学业成绩均值结合，经探索性数据分析与相关性分析筛选特征。成绩分为不及格、及格、优秀三类。通过调优七种机器学习算法（SVM、LR、KNN、DT、GB、RF、XGBoost、LightGBM），随机森林（RF）表现最佳：使用17项人格特质+领导力评分时准确率达87.50%，剔除该评分后为85.71%。研究为早期识别学生优势与不足、制定个性化学习策略提供新路径。
原文摘要 · Abstract (English)
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.
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