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

Abstract

Auto-grading of short answer questions is considered a challenging problem in the processing of natural language. It requires a system to comprehend the free text answers to automatically assign a grade for a student answer compared to one or more model answers. This paper suggests an optimized deep learning model for grading short-answer questions automatically by using various sizes of datasets collected in the Science subject for students in seventh grade in Egypt. The proposed system is a hybrid approach that optimizes a deep learning technique called LSTM (Long Short Term Memory) with a recent optimization algorithm called a Grey Wolf Optimizer (GWO). The GWO is employed to optimize the LSTM by selecting the best dropout and recurrent dropout rates of LSTM hyperparameters rather than manual choice. Using GWO makes the LSTM model more generalized and can also avoid the problem of overfitting in forecasting the students’ scores to improve the learning process and save instructors’ time and effort. The model’s performance is measured in terms of the Root Mean Squared Error (RMSE), the Pearson correlation coefficient, and R-Square. According to the simulation results, the hybrid GWO with the LSTM model ensured the best performance and outperformed the classical LSTM model and other compared models such that it had the highest Pearson correlation coefficient value, the lowest RMSE value, and the best R square value in all experiments, but higher training time than the traditional deep learning model.

The Analysis of Deep Learning Recurrent Neural Network in English Grading Under the Internet of Things

“…The results demonstrated that the system using bidirectional encoder representations from transformer model and fine-tuning methods could enhance the accuracy of shortanswer grading [15]. Abdul Salam et al (2022) proposed a hybrid approach that combined long short-term memory (LSTM) networks and grey wolf optimizer to automatically grade short-answer questions. Simulation results indicated better performance of this hybrid model, although the training time was longer [16].…”

“…Abdul Salam et al (2022) proposed a hybrid approach that combined long short-term memory (LSTM) networks and grey wolf optimizer to automatically grade short-answer questions. Simulation results indicated better performance of this hybrid model, although the training time was longer [16]. Filighera et al (2023) designed a blackbox adversarial attack specifically for educational shortanswer grading scenarios to study the robustness of grading models.…”

Automatic Arabic Grading System for Short Answer Questions

“…The model was applied on dataset with 330 student's answers. It achieved 0.81 value as a Root Mean Square Error (RMSE) and 0.94 value as a Pearson correlation r. In [15], the authors proposed an automatic grading for Arabic short answer questions using optimized deep learning model. They used a hybrid LSTM and GWO model to predict the short answer grade questions for the students in science.…”

Software System for Automatic Grading of Paper Tests

“…Matching Python, YOLO PMFU [12] 2019. Matching Python, MATLAB, CNN FCAI-BU [13] 2022. Short answer Python, LSTM with GWO ITD-AATSU [14] 2021.…”

“…The chosen systems satisfying the previously given criteria were developed at the School of Electrical Engineering, University of Belgrade (SEE-UB) [8], School of Engineering, Edith Cowan University (SE-ECU) [9], Department of Telematic Engineering, University Carlos III of Madrid (DTE-UCM) [10], School of Electronic and Information Engineering, Foshan University (SEIE-FU) [11], Prince Mohammad bin Fahd University (PMFU) [12], Artificial intelligence Department, Faculty of Computers and Artificial Intelligence, Benha University (FCAI-BU) [13], Information Technologies Division, Adana Alparslan Turkes Science and Technology University (ITD-AATSU) [14], School of Software South China, University of Technology Guangzhou and College of Medical Information Engineering, Guangzhou University of Chinese Medicine (SSSC-UTG/CMIE-GUCM) [15].…”