Comparison of Effectiveness of Logistic Regression, Naive Bayes, and Random Forest Algorithms in Predicting Student Arguments

Currently, in the process of assessing and giving feedback on students' argumentative writing, educators have to spend a considerable amount of time reading and analyzing each essay individually. This can be a complicated and time-consuming process, especially if the number of students to be assessed is quite large. The problem of this research is to find the most effective algorithm in providing accurate and reliable predictions in the context of evaluation and feedback of students' argumentation. This study compares three algorithms (logistic regression, Naive Bayes, and Random Forest) to predict student argumentation using essays from grades 6-12. Logistic regression performed best with 94.34% accuracy, followed by random forest with 91.98% accuracy, and Naive Bayes with 88.93% accuracy. The study optimized preprocessing and selected algorithms for an automated guidance model. It is the first stage of a three-part study for developing automated guidance models. Data came from Kaggle, and the study aims to improve the accuracy of automated guidance models for student argumentation.