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Automatic grading for Arabic short answer questions using optimized deep learning model.

Mustafa Abdul Salam1,2, Mohamed Abd El-Fatah3, Naglaa Fathy Hassan4

  • 1Artificial intelligence Dept., Faculty of Computers and Artificial Intelligence, Benha University, Banha, Egypt.

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Summary
This summary is machine-generated.

This study introduces an optimized deep learning model using Long Short-Term Memory (LSTM) and the Grey Wolf Optimizer (GWO) for auto-grading science questions. The hybrid model significantly improves accuracy and generalization in grading student answers.

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Area of Science:

  • Educational Technology
  • Natural Language Processing
  • Artificial Intelligence

Background:

  • Automated grading of short answer questions presents a significant challenge in natural language processing.
  • Accurate grading requires systems to comprehend free-text student responses against model answers.

Purpose of the Study:

  • To propose an optimized deep learning model for the automatic grading of short answer questions in science subjects.
  • To enhance the generalization capabilities and reduce overfitting in grading models.

Main Methods:

  • A hybrid approach combining Long Short-Term Memory (LSTM) with the Grey Wolf Optimizer (GWO) was developed.
  • GWO was used to optimize LSTM hyperparameters, specifically dropout and recurrent dropout rates.
  • The model was trained and evaluated on datasets of seventh-grade science answers from Egypt.

Main Results:

  • The hybrid GWO-LSTM model demonstrated superior performance compared to traditional LSTM and other models.
  • It achieved the highest Pearson correlation coefficient and R-square values.
  • The model exhibited the lowest Root Mean Squared Error (RMSE) across all experiments.

Conclusions:

  • The optimized hybrid model effectively addresses the challenges of automated short answer grading.
  • It offers improved accuracy and generalization, outperforming existing methods.
  • While training time is increased, the benefits in grading accuracy and efficiency are substantial.