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

Jwan Abdulkhaliq Mohammed
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引用次数: 0

Abstract

For many years, reading rate as word correct per minute (WCPM) has been investigated by many researchers as an indicator of learners’ level of oral reading speed, accuracy, and comprehension. The aim of the study is to predict the levels of WCPM using three machine learning algorithms which are Ensemble Classifier (EC), Decision Tree (DT), and K- Nearest Neighbor (KNN). The data of this study were collected from 100 Kurdish EFL students in the 2nd-year, English language department, at the University of Duhok in 2021. The outcomes showed that the ensemble classifier (EC) obtained the highest accuracy of testing results with a value of 94%. Also, EC recorded the highest precision, recall, and F1 scores with values of 0.92 for the three performance measures. The Receiver Operating Character curve (ROC curve) also got the highest results than other classification algorithms. Accordingly, it can be concluded that the ensemble classifier is the best and most accurate model for predicting reading rate (accuracy) WCPM.    
应用集成分类器、k近邻和决策树预测口语阅读速度水平
多年来,许多研究者将阅读率作为每分钟正确单词数(WCPM)作为学习者口语阅读速度、准确性和理解水平的指标进行了研究。该研究的目的是使用三种机器学习算法来预测WCPM的水平,这三种算法是集成分类器(EC)、决策树(DT)和K-最近邻(KNN)。本研究的数据是在2021年从杜胡克大学英语系二年级的100名库尔德语学生中收集的。结果表明,集成分类器(EC)的检测结果准确率最高,达到94%。此外,EC在三个绩效指标上的准确率、召回率和F1得分最高,为0.92。受试者工作特征曲线(Receiver Operating Character curve, ROC曲线)的分类效果也优于其他分类算法。综上所述,集成分类器是预测阅读率(准确率)WCPM的最佳和最准确的模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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