Applying Ensemble Classifier, K-Nearest Neighbor and Decision Tree for Predicting Oral Reading Rate Levels

For many years, reading rate as word correct per minute (WCPM) has been investigated by researchers an indicator of learners’ level oral speed, accuracy, and comprehension. The aim the study is to predict levels WCPM using three machine learning algorithms which are Ensemble Classifier (EC), Decision Tree (DT), K- Nearest Neighbor (KNN). data this were collected from 100 Kurdish EFL students in 2nd-year, English language department, at University Duhok 2021. outcomes showed that ensemble classifier (EC) obtained highest accuracy testing results with a value 94%. Also, EC recorded precision, recall, F1 scores values 0.92 for performance measures. Receiver Operating Character curve (ROC curve) also got than other classification algorithms. Accordingly, it can be concluded best most accurate model predicting (accuracy) WCPM.