Improving Predictive Accuracy in Writing Assessment Through Advanced Machine Learning Techniques

Abstract

This research investigates the application of the Machine Learning (ML) model for effective and equitable essay scoring in education. Unlike their human counterpart, ML models have the capacity to rapidly analyze scores of essays, providing timely and equitable scores that take into account varying student demographics and styles of writing. This function helps in the identification of classroom problems and supports the design of focused teaching methodologies. For the study, a Light Gradient Boosting Classification (LGBC) model was optimized by three optimizers: Black Widow Optimization (BWO), Zebra Optimization Algorithm (ZOA), and Leader Harris Hawks Optimization (LHHO), for the development of the hybrid models with a focus on improved prediction quality. Comparison of these hybrid models with the base LGBC model was performed through different phases, such as Training, Validation, and Testing. The findings show that the LGLH model exhibited improved performance with an accuracy rate of 0.981, followed by the LGZO model with 0.971 and the LGBW model with 0.963. The lowest rate of accuracy was observed in the base LGBC model, which was 0.946. The results demonstrate the efficacy of hybrid models, which harness the optimality of several optimization techniques and provide more robust results for complicated tasks. The study emphasizes the importance of selecting the appropriate model architecture to achieve optimal performance, providing valuable insights into model efficacy at various stages of evaluation.