Smart Computerized Essay Scoring Using Deep Neural Networks for Universities and Institutions
J. Joshua Thomas, Lim Ting Wei, Y. Bevish Jinila, R. Subhashini · Advances in computational intelligence and robotics book series · 2020
This chapter develops a web-based automated text scoring (ATS) system that can grade essays and check for spelling errors. The main reason behind this work is to alleviate the labour-intensive marking of essays and ensures equality in scoring for high-stakes exams like TOEFL. The researcher had performed a detailed investigation on deep learning techniques used in the field of ATS and developed a recurrent neural network model that can score essays in an end-to-end approach. Using the developed deep learning model, a web application was also developed to showcase the process of ATS by letting the web application to communicate with the trained model. The model was trained using Keras framework and TensorFlow library and the web application was done using the Flask framework. This work is the LSTM network that can capture sequential dependencies. The evaluation metrics chosen to evaluate the model are the quadratic weighted kappa (QWK) score, and the trained model can achieve 0.6 in QWK score.