ارسل ملاحظاتك

ارسل ملاحظاتك لنا







Predicting Kurdish Efl University Learners' Oral Reading Fluency Using Support Vector Machine

المصدر: المجلة الدولية للعلوم الإنسانية والاجتماعية
الناشر: كلية العلوم الإنسانية والاجتماعية
المؤلف الرئيسي: Ali, Araz Bashar Mohammed (Author)
مؤلفين آخرين: Ali, Jwan Abdulkhaliq Mohammed (Co-Author), Melhum, Amera Ismail (Co-Author)
المجلد/العدد: ع39
محكمة: نعم
الدولة: لبنان
التاريخ الميلادي: 2022
الشهر: نوفمبر
الصفحات: 246 - 258
ISSN: 2708-5414
رقم MD: 1332206
نوع المحتوى: بحوث ومقالات
اللغة: الإنجليزية
قواعد المعلومات: EduSearch, HumanIndex
مواضيع:
كلمات المؤلف المفتاحية:
Oral Reading Fluency | Support Vector Machine | MDFS | Machine Learning
رابط المحتوى:
صورة الغلاف QR قانون

عدد مرات التحميل

6

حفظ في:
LEADER 02930nam a22002537a 4500
001 2089796
041 |a eng 
044 |b لبنان 
100 |9 699828  |a Ali, Araz Bashar Mohammed  |e Author 
245 |a Predicting Kurdish Efl University Learners' Oral Reading Fluency Using Support Vector Machine 
260 |b كلية العلوم الإنسانية والاجتماعية  |c 2022  |g نوفمبر 
300 |a 246 - 258 
336 |a بحوث ومقالات  |b Article 
520 |b 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 F1-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. 
653 |a القراءة الشفوية  |a اللغة الإنجليزية  |a استراتيجية التعلم  |a التعلم الإلكتروني 
692 |b Oral Reading Fluency  |b Support Vector Machine  |b MDFS  |b Machine Learning 
700 |9 706506  |a Ali, Jwan Abdulkhaliq Mohammed  |e Co-Author 
700 |9 706507  |a Melhum, Amera Ismail  |e Co-Author 
773 |4 العلوم الإنسانية ، متعددة التخصصات  |4 العلوم الاجتماعية ، متعددة التخصصات  |6 Humanities, Multidisciplinary  |6 Social Sciences, Interdisciplinary  |c 016  |e International Journal on Humanities and Social Sciences  |f Al-mağallaẗ al-duwaliyyaẗ li-l-ʿulūm al-insāniyyaẗ wa-al-iğtimāʿiyyaẗ  |l 039  |m ع39  |o 2197  |s المجلة الدولية للعلوم الإنسانية والاجتماعية  |v 000  |x 2708-5414 
856 |u 2197-000-039-016.pdf 
930 |d y  |p y  |q n 
995 |a EduSearch 
995 |a HumanIndex 
999 |c 1332206  |d 1332206