Predicting Kurdish EFL university learners' oral reading fluency using support vector machine
Joint Authors
Ali, Araz Bashar Mohammed
Ali, Jwan Abdulkhaliq Mohammed
Milhim, Amirah Ismail
Issue
Vol. 2022, Issue 39 (30 Nov. 2022), pp.246-258, 13 p.
Publisher
College of Humanities and Social Sciences
Publication Date
2022-11-30
Country of Publication
Lebanon
No. of Pages
13
Main Subjects
Information Technology and Computer Science
Educational Leadership and Foundations
Abstract EN
Investigating learners' English Oral Reading Fluency (ORF) in the contexts where English is used as a foreign language (EFL) or second language (ESL) has recently become a trending subject.
This study was carried out to predict the ORF of 100 Kurdish EFL university students of the English language department, college of Basic Education, university of Duhok, Iraq in 2020 during covid-19 using the support vector machine (SVM) technique.
This technique is one of the supervised machine learning techniques, and it is considered the most powerful algorithm in machine learning in terms of high accuracy; therefore, it was employed in this study.
Participants' ORF was measured by two experienced human raters using the four dimensions of the Multidimensional Fluency Scale (MDFS) including expression & volume, phrasing, smoothness, and pacing, which were used as the input variables to predict the ORF as an output.
Six kernels of the SVM were used in the prediction process.
The results indicated that the highest accuracy of testing result was obtained on the use of SVM Linear kernel with a value of 96.2%.
Confusion matrix was utilized to assess the outcomes of data classification.
The results of precision, recall, and FI-score were the highest for the SVM linear kernel and their values were the same for all performance metrics with a value of 96.1%.
Accordingly, it can be concluded that the performance of the SVM is considerably accurate in predicting the oral reading fluency.
American Psychological Association (APA)
Ali, Araz Bashar Mohammed& Ali, Jwan Abdulkhaliq Mohammed& Milhim, Amirah Ismail. 2022. Predicting Kurdish EFL university learners' oral reading fluency using support vector machine. Vol. 2022, no. 39, pp.246-258.
https://search.emarefa.net/detail/BIM-1492113
Modern Language Association (MLA)
Ali, Araz Bashar Mohammed…[et al.]. Predicting Kurdish EFL university learners' oral reading fluency using support vector machine. No. 39 (Nov. 2022), pp.246-258.
https://search.emarefa.net/detail/BIM-1492113
American Medical Association (AMA)
Ali, Araz Bashar Mohammed& Ali, Jwan Abdulkhaliq Mohammed& Milhim, Amirah Ismail. Predicting Kurdish EFL university learners' oral reading fluency using support vector machine. . 2022. Vol. 2022, no. 39, pp.246-258.
https://search.emarefa.net/detail/BIM-1492113
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references : p. 257-258
Record ID
BIM-1492113