This is a system that automatically grades essays built using Machine Learning (ML), Deep Learning (DL), Natural Language Processing (NLP) techniques with the help of many libraries and tools e.g. scikit-learn, Keras, Seaborn, Gensim, NLTK, Ploty, Numpy, Pandas.
For more details refer to unpublished-conference-paper.pdf, please.
Abstract- The task of human-based grading essays has been big trouble due to the long time it needs, subjectiveness in its nature, difficulty it involves, and focus it requires to minimize the possibility of errors as much as possible. As well as, the requirement of numerous teachers as in most of the situations, a single essay is being graded at least twice by distinct people. These reasons push us toward investigating and exploring more in this scope. We make use of the essays dataset obtained from Kaggle.com. These essays are divided into 8 sets based on context and grade of students arranging from 7 to 10. We first, clean the data and preprocess it using NLP techniques then, we convert the textual data into numeric by using the word-embedding approach. After that, we feed those input vectors into regression-based models to predict the grades. In the training and testing phases, we use the KFold methodology. Lastly, we evaluate the performance of the used models using many measurement matrices depending on the model. In the case of LSTM, we use Quadratic Weighted Kappa QWK to figure out the agreement between predicted and original grade.