1 Information Technology Department, Pamantasan ng Lungsod ng Maynila, Manila, Philippines.
2 Computer Science Department, Pamantasan ng Lungsod ng Maynila, Manila, Philippines.
Research Article
World Journal of Advanced Research and Reviews, 2023, 18(02), 034-043
Article DOI: 10.30574/wjarr.2023.18.2.0762
Received on 05 March 2023; revised on 27 April 2023; accepted on 29 April 2023
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.
Random Forest Algorithm; Bag-of-words; Confusion Matrix; Mathematical Information Retrieval; Table of Specification.
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Jubeile Mark Baladjay, Nisce Riva, Ladine Ashley Santos, Dan Michael Cortez, Criselle Centeno and Ariel Antwaun Rolando Sison. Performance evaluation of random forest algorithm for automating classification of mathematics question items. World Journal of Advanced Research and Reviews, 2023, 18(2), 034-043. Article DOI: https://doi.org/10.30574/wjarr.2023.18.2.0762