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Modeling the time to dropout under phase-wise variable stress fixed cohort setup.

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  • 1Department of Statistics, St. Xavier's University, Kolkata, India.

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Summary

Student attrition is influenced by various factors like course content and finances, creating academic stress that varies by program phase. This study models accumulated stress using the Kumaraswamy distribution to understand dropout behavior.

Keywords:
Accelerated life-testingconfidence intervalmaximum likelihood estimationproportional hazard ratestep-stress reliability modeling

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Area of Science:

  • Educational research
  • Statistical modeling
  • Academic program analysis

Background:

  • Student dropout is a complex issue influenced by multiple factors.
  • These factors, including course content, interest changes, and financial issues, interact differently across academic program phases.
  • Understanding the cumulative effect of academic stress is crucial for student retention.

Purpose of the Study:

  • To formulate and analyze an accumulated-stress model for student attrition.
  • To investigate how academic stress accumulates and varies across different phases of an academic program.
  • To apply the Kumaraswamy distribution to model attrition time at each phase.

Main Methods:

  • A hazard-rate based approach was employed to model accumulated stress.
  • The Kumaraswamy distribution was assumed for attrition time at each academic phase.
  • Model parameters were estimated using the frequentist approach, validated by simulation experiments.

Main Results:

  • The developed accumulated-stress model effectively captures dropout dynamics.
  • Simulation experiments demonstrated satisfactory performance of the parameter estimators.
  • The model provides insights into how stress accumulates and impacts student attrition.

Conclusions:

  • The study successfully models student dropout using an accumulated-stress framework.
  • The Kumaraswamy distribution is a suitable choice for modeling attrition times in academic phases.
  • The findings offer a quantitative approach to understanding and potentially mitigating student attrition.