Automated College Application Essay Grading

Abstract

In recent years, computer programs used to score essays have been explored extensively, with many different approaches being developed. Most of these approaches use Natural Language Processing (NLP) techniques (Ade-Ibijola et al., 2012), a field of machine learning often used to analyze and understand text. These approaches fall under the name of Automated Essay Scoring (AES), which typically assesses essay quality with a single score (Ke and Ng, 2019). This paper proposes a natural language processing (NLP) model which predicts the quality of a college application essay, which is proximally measured through a college’s acceptance rate. Key essay factors include the number of grammar mistakes, sophistication of writing, repetition, and the text of the essay. Multiple different models were tested. A Random Forest Classifier relying solely on grammar, sophistication of writing, and repetition metrics achieved the best performance, yielding an accuracy of 89.7%. The second-best model was a combination of an LSTM and a logistic regression model. Other models significantly underperformed, yielding accuracies in the range of 40%-60%. Ultimately, our model may help a number of students going through the college application process to understand where their essay may stand compared to other students.