Neural-Network Architecture Approach: An Automated Essay Scoring Using Bayesian Linear Ridge Regression Algorithm

Most people are relying on technology in various industries, such as education, where people are more likely to use technology as a platform for knowledge acquisition. In this study, the researchers proposed an Automated Essay Scoring or AES to help the teachers minimize the time in grading essay work of the student, prevent biased ratings and provide a feedback mechanism for the students. The study proposed a Neural-Network Approach Architecture and Bayesian Linear Ridge Regression Algorithm to improve the correctness and reliability of the AES system and dimensions we are going to use. We also use the new Hybrid Model or Hybrid Agile to have a detailed approach in developing our study. We used the dataset from the Hewlett Foundation, the Automated Student Assessment Prize (ASAP) from Kaggle. We also gather information in reliable sources and collect data to test the accuracy and reliability of our AES system.