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An Efficient Bidirectional Gated Recurrent Unit Approach for Student Study Duration Modeling and Timely Graduation Forecasting

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The model predicts graduation timelines, achieving a Mean Absolute Percentage Error of 9.5%, indicating strong accuracy.
Students in higher tuition fee tiers consistently exhibit better academic performance, reflected in their GPA.
The integration of academic performance and financial data enhances the accuracy of graduation forecasts.
Findings suggest deep learning models like BiGRU can effectively inform academic interventions to reduce dropout rates.

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Predictive model estimates graduation year using academic and financial indicators, highlighting BiGRU's effectiveness.

Purnama et al. (2025) studied this question.

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