Performance evaluation of random forest algorithm for automating classification of mathematics question items

Automated classification of mathematics question items based on the Table of Specifications is crucial in developing well-defined assessment content, significantly reducing teachers’ workload. This study presents a performance evaluation of a Random Forest model designed to classify mathematics question items based on the content standards of the first quarter of tenth grade stipulated by the Philippines’ Department of Education Curriculum Guide. The model uses an algorithm that extracts mathematical expressions as tokens for the Bag-of-words Model. The evaluation was conducted using precision, recall, F-1 score, and overall accuracy metrics, and the confusion matrix was used to assess the Random Forest model’s performance. The results showed that the Random Forest model achieved 95% in precision, 95% in recall, 95% in F-1 score, and 95% in overall accuracy, demonstrating its effectiveness in classifying mathematics question items.