Comparing logistic regression and extreme gradient boosting on student arguments

Abstract

Identifying the effectiveness level and quality of students' arguments poses a challenge for teachers. This is due to the lack of techniques that can accurately assist in identifying the effectiveness and quality of students' arguments. This research aims to develop a model that can identify effectiveness categories in students' arguments. The method employed involves the logistic regression+XGBoost algorithm combined with separate implementations of term frequency-inverse document frequency (TF-IDF) and CountVectorizer. Student argument data were collected and processed using natural language processing techniques. The research results indicate that TF-IDF outperforms in identifying effectiveness classes in student arguments with an accuracy of 66.20%. The multi-output classification yielded an accuracy of 89.32% in the initial testing, which further improved to 92.34% after implementing one-hot encoding. A novel finding in this research is the superiority of TF-IDF as a technique for identifying effectiveness classes in student arguments compared to CountVectorizer. The implications of this research include the development of a model that can assist teachers in identifying the effectiveness level of students' arguments, thereby improving the quality of learning and enhancing students' argumentative competence.