Automatic Evaluation of Open-Ended Mathematics Questions with Machine Learning

The main purpose of this research is to develop an effective and objective scoring model for evaluating open-ended mathematics questions using machine learning algorithms predicting the academic performance of secondary school students in mathematics. Data was collected from 350 fifth-grade secondary school students, and the responses were digitized. For the classification of student answers, state-of-the-art machine learning algorithms, including Support Vector Machines (SVM), Naive Bayes (NB), Decision Tree (DT), and K-Nearest Neighbors (KNN), were employed. The study utilized TF-IDF and BoW techniques to convert the text-based answers into numerical representations. Key features were then extracted from this digitized text data using the Chi-square (Chi2) feature selection method. The classification models were evaluated using performance metrics such as accuracy, F1-score, precision, and recall. Among the algorithms tested, the SVM model combined with the TF-IDF method achieved the highest performance, with 96.57% accuracy and a 96.49% F1 score.