Automated essay scoring system

Author
Abstract

Essay Questions nowadays are one of the essential tools to evaluate students at all educational levels because it measures high-level skills such as linking ideas and using complex semantic structure. Essay assessment is time-consuming, challenging, and requires long focus time to understand, find mistakes, and grade. These questions are mainly characterized as a subjective evaluation since there is usually a wide range of answers. Designing and developing efficient and accurate machine-learning models will help both students enhance their writing skills and teachers evaluate essays.

In this thesis, I examine the manual process of grading essays and develop a system that can grade them automatically for English essays. The process contains feature extraction, applying different machine learning techniques, and comparing the results of different models. I rely on the Natural Language Toolkit (NLTK) to extract features from the data set. In terms of evaluating the models, I used mean square error (MSE) and root mean square error (RMSE) as of measurement metrics.

Ultimately, a further study could enhance the model by expanding the training set and applying more machine learning techniques to reduce the gap between the system and the human assessment.

Attachments
Master Thesis (2.39 MB)
Publication Year
Department
Computer engineering and computer science
Subject
Computer Science