An Optimized LSTM-Based Augmented Language Model (FLSTM-ALM) Using Fox Algorithm for Automatic Essay Scoring Prediction

Abstract

The computer-based Automated Essay Scoring (AES) system automatically marks or scores student replies by considering relevant criteria. The methodology, which systematically categorizes writing quality, can increase operational effectiveness in academic and major commercial institutions. To study the projected score, AES relies on extracting numerous aspects from the student's response, including grammatical and textural information. However, the recovered features may result in dimensionality reduction and a challenging-to-understand feature selection procedure. As the number of parameters rises, the model also demands a large cost for processing and training the data. However, these problems worsen the accuracy of score prediction as a whole and widen the gap between actual and anticipated results. This study suggested the Fox-optimized Long Short-Term Memory-based Augmented Language Model (FLSTM-ALM) as a solution to these problems for giving successful training to text features; the model uses an augmented learning paradigm. The retrieval score was then analyzed and generated using a neural knowledge encoder and retriever. The neural model successfully classifies the output based on this score. The best features are then chosen using the fox optimization algorithm based on the food-searching category. This choice of parameters solves the exploration and optimization issue with document classification. The performance of the optimized AES system was assessed using the two datasets, ASAP and ETS, and it demonstrated a high accuracy of 98.92% and a low error rate of 0.096%. Dimensionality reduction can thus be fixed by optimizing the FLSTM-ALM model with an appropriate meta-heuristic method, such as the FOX algorithm, which raises the predicted accuracy, recall, and f1 score for the AES model.