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Related Experiment Video

Updated: May 7, 2026

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
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Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

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A hybrid framework of statistical, machine learning, and explainable AI methods for school dropout prediction.

Mst Rokeya Khatun1, Mithila Akter Mim1, Md Mahadi Tasin2

  • 1Department of Computer Science and Engineering, Bangladesh University, Dhaka, Bangladesh.

Plos One
|September 10, 2025
PubMed
Summary

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Avoidance Learning and Learned Helplessness01:14

Avoidance Learning and Learned Helplessness

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Avoidance learning and learned helplessness are critical concepts in understanding behavioral responses to negative stimuli.
Avoidance learning occurs when an organism learns that a specific behavior can prevent an unpleasant outcome. For example, a student who receives a bad grade may start studying harder to avoid future poor grades. This behavior persists even when the negative outcome is no longer present. Avoidance learning is powerful because it maintains behavior in the absence of the...
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Student dropout in Bangladesh is influenced by factors like age, sex, and parental education. Machine learning models identified key predictors, aiding policy development for improved educational outcomes.

Area of Science:

  • Educational research
  • Data science
  • Public policy

Background:

  • Student dropout poses significant challenges in Bangladesh, impacting educational attainment and socio-economic development.
  • Addressing student attrition is crucial for improving overall educational system effectiveness.

Purpose of the Study:

  • To investigate the multifaceted factors contributing to school dropout among Bangladeshi students aged 6-24.
  • To develop and validate predictive models for student dropout using advanced analytical techniques.
  • To enhance the interpretability of predictive models for transparent policy recommendations.

Main Methods:

  • Utilized data from the 2019 Multiple Indicator Cluster Survey (MICS) for Bangladesh.
  • Employed a hybrid approach combining statistical analysis (logistic regression) with machine learning (Random Forest, XGBoost).

Related Experiment Videos

Last Updated: May 7, 2026

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

4.5K
  • Applied explainable AI techniques (SHAP, LIME) for model interpretability.

    • Main Results:

    • The Extreme Gradient Boosting (XGBoost) model demonstrated superior predictive performance with 94.4% accuracy.
    • Key predictors identified include age, sex, completed grade, division, wealth index, and parents' educational background.
    • Statistical and machine learning analyses successfully identified significant factors influencing student dropout.

    Conclusions:

    • The study provides a data-driven foundation for understanding and mitigating student dropout in Bangladesh.
    • Findings can inform the development of targeted intervention strategies for policymakers.
    • Reducing student dropout rates is essential for enhancing educational outcomes and national development.