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Teacher retention remains a critical issue in elementary and high schools, influencing the quality of education and school stability. This research used a random forest machine learning algorithm to predict which teachers are at the highest risk of leaving their positions. Using a dataset of 1192 teachers in Bosnia and Herzegovina, factors such as job satisfaction, workload, salary, professional development opportunities, and school administration are analysed. The results highlight key predictors of teacher attrition and provide actionable insights for developing targeted retention strategies.

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Using Machine Learning for Teacher Retention in Schools

  • 会议论文

  • 发表日期：

    2025年

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  • Zilić Edisa,
  • Zilić Mirdin

摘要

Teacher retention remains a critical issue in elementary and high schools, influencing the quality of education and school stability. This research used a random forest machine learning algorithm to predict which teachers are at the highest risk of leaving their positions. Using a dataset of 1192 teachers in Bosnia and Herzegovina, factors such as job satisfaction, workload, salary, professional development opportunities, and school administration are analysed. The results highlight key predictors of teacher attrition and provide actionable insights for developing targeted retention strategies.