AUTOMATED SHORT ANSWER GRADING USING LONG SHORT-TERM MEMORY OPTIMIZED WITH PARTICLE SWARM OPTIMIZATION

Abstract

Automated Short Answer Grading (ASAG) systems contribute immensely to providing prompt feedback to students, which eases the workload of instructors. This research focuses on the development of an optimized ASAG model using LSTM model and particle swarm optimization techniques to prevent model overfitting. The popular ASAG dataset by Mohler was utilized for the experiment. The dataset contains training samples from Computer Science department of the Federal University of Technology, Minna, Nigeria, with grades between 0 and 5. In order to effectively optimize the LSTM model parameters, which are learning rate and number of neurons in the LSTM layers, four experiments were performed, each with different particle population sizes (5, 10, 15 and 20). The results show that PS5 model produced the lowest RMSE and MAPE of 0.77697 and 44.5356%, respectively. The PS15 model, however, produced the highest RMSE and MAE of 0.80985 and 56.6192%, respectively. In order to validate the developed PSO-LSTM ASAG model, normal LSTM model for ASAG was implemented and tested. The PSO-LSTM has an RMSE value of 0.77687 and MAPE of 44.5356%, as compared with LSTM, which has an RMSE value of 0.9423 and MAPE of 85.73%. The results clearly show the superiority of the developed hybrid model in predicting the scores of short answer grading. The model's performance can be further improved by increasing the sample size and using other optimization algorithms, such as genetic algorithms or ant colony optimization. Further research can also investigate the effect of other variables, such as question complexity and student writing style, on the model's performance.