The contribution of rapid automatized naming skills and phonological awareness to Arabic language reading fluency: A path analysis

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The contribution of rapid automatized naming skills and phonological awareness to Arabic language reading fluency: A path analysis

Abdulaziz Alshahrani, Dr. Associate Professor of Applied Linguistics

Abstract

Purpose. The aim of this study was to investigate the contribution of rapid automatized naming skills (RAN) and phonological awareness (PA) to Arabic language reading fluency(RF).

Methods. A quantitative, cross-sectional and descriptive method was employed, with the main focus on the contribution of rapid automatized naming skills and phonological awareness to Arabic language reading fluency. A cross-sectional study was performed for all children in six prim art schools located in Makka. A convenience sampling method was used to recruit children. For children to be included in this study, there were some criteria: (a) to have no academic or developmental disabilities; (b) Both gender (males and females). Questionnaires were distributed to the children with the help of classroom teachers. All questions must be answered and responded to. A total of 360 participants aged 6-10 years took part in this study: 280 males (77.7%, age mean = 8.9, SD = 3.27), and 80 females (22.3%, age mean = 9.2, SD = 2.44).

Results. The first main finding of the current research is that the correlations between RAN, PA and reading fluency were significant. In the final model, PA was directly and positively predictive of RAN (в = 0.664, p = 0.000) and RAN was directly and strongly related to RF (в = 0.623, p = 0.000). PA explained 52.3% of variance of RF. RAN explained 51.8% of variance of RF. PA and RAN together explained 69.9% of variance of RF.

Conclusions. The findings of this study add to our knowledge of the contribution of PA and RAN to RF, as well as the complex intralingual relationship between PA and RAN.

Key words: rapid automatized naming skills, phonological awareness, Arabic language reading fluency, path analysis.

Анотація

Абдулазіз Альшахрані, доктор філософії в галузі прикладної лінгвістики, асистент професора

Мета. Метою цього дослідження було дослідити внесок навичок швидкого автоматизованого називання (RAN) та фонологічної обізнаності (PA) у вільне читання арабською мовою (RF).

Методи. У дослідженні були використані кількісний, перехресний та описовий методи, з основним акцентом на внесок навичок швидкого автоматизованого називання та фонологічної обізнаності у вільне читання арабською мовою.

Перехресне дослідження було проведене для всіх дітей у шести початкових школах мистецтв, розташованих у місті Макка. Для відбору дітей використовувався метод випадкової вибірки. Для включення дітей у це дослідження існували певні критерії: (а) не мати академічних порушень або порушень розвитку; (б) бути обох статей (хлопці й дівчата). Дітям були запропоновані анкети. Необхідно було дати відповідь на всі запитання. Загалом у дослідженні взяли участь 360 учасників віком 6-10 років: 280 хлопців (77.7%, середній вік = 8;9, SD = 3,27) та 80 дівчат (22.3%, середній вік = 9;2, SD = 2,44). Результати. Перший основний висновок цього дослідження полягає в тому, що кореляції між RAN, PA й швидкістю читання були значними. В остаточній моделі PA прямо й позитивно прогнозовано RAN (в = 0.664, p = 0.000), а RAN був прямо й сильно пов'язаний з RF (в = 0.623, p = 0.000). PA пояснював 52.3% дисперсії RF. RAN пояснює 51.8% дисперсії RF. PA і RAN разом пояснюють 69.9% дисперсії RF.

Висновки. Результати цього дослідження доповнюють наші знання про внесок PA і RAN в RF, а також про складні внутрішньомовні зв'язки між PA і RAN.

Ключові слова: навички швидкого автоматизованого називання, фонологічна обізнаність, швидкість читання арабською мовою, аналіз шляху.

Introduction

Language appears in different forms such as speaking, listening, reading and writing, and listening is the most visible form of language, then conversation, after that, experiences accumulate to reach the written language (Altay, 2013). In this aspect, language is a basic foundation for the development of various other skills acquired by the individual in the various stages of his development, and all language skills are intertwined, and any skill acquired by the individual helps him/her to acquire other skills (Uysal & Habip, 2013).

Reading is regarded as a complex process that is likely to require the automatic integration of multiple cognitive and linguistic abilities (Abdul Kader & Eissa, 2016; Elhoweris, 2017; Esam, 2015; Fathalla, 2014). There are ample studies on the processes underlying reading acquisition (Eissa, 2013, 2014, 2017). Reading acquisition is strongly related to phonological awareness (PA), letter-sound knowledge, and reading fluency (Abdul, 2014).

Reading-related skills such as rapid automatized naming (RAN) and phonological awareness can be potential predictors or later reading ability and reading fluency (Al Sawi, 2013).

Phonological Awareness and Reading Fluency

Phonological awareness is one of the linguistic levels most closely related to reading and writing skills (Eissa, 2017). Phonological awareness has been defined as the ability to recognize, discriminate, and manipulate speech sounds of spoken words. It is best understood as a complex construct composed of separate subskills (Eissa, 2013, 2014, 2107). Phonemic dyslexia reflects a defect in the process of forming the alphabetic strategy for reading and writing, which impedes the emergence or development of an effective assembly process, and this defect, in turn, is reflected in the subsequent growth of recognition of written words and speed of naming them (Al Sawi, 2013).

Thus his/her language fluency is affected, and this is what was indicated by (Ramus et al., 2003), where they explained the three main factors responsible for learning to read, and then it became a basis for explaining dyslexia, which is the ability to name stimuli), and the ability to remember through the audio coding of stimuli Simple or complex visual as a reading text, for example, and the ability to employ phonemic awareness (the vocal ability represented in the ability to sense the components of the oral language and mentally analyze them according to different levels (Ramus et al., 2003).

According to the orthographical specifications of different languages, the correlation of phonemic operations with reading abilities may vary based on the nature of the relationship in terms of complexity or clarity between the letter and its sound. Dealing with different levels of phonemic awareness in languages with a complex orthographic character in which it is difficult to link the letter and its sound such as English is inevitable, as awareness of sounds is one of the strongest indicators of the accuracy and speed of reading in the English language, while its predictive ability is less Gradually in other languages with an easy spelling character such as German, Spanish, Greek or Finnish (Holopainen, Ahonen & Lyytinen, 2001; Seymour, Aro & Erskine, 2003; Wimmer, Mayringer & Landerl, 2000).

Rapid Automatic Naming and Reading Fluency

Rapid Automatic Naming emerges as an important predictor of reading in these easy-to-orthographic languages (Michalick-Triginelli & Cardoso-Martins, 2015). RAN consists of tasks that require naming familiar items (objects, colors, letters, or digits). They visually presented as fast as possible (da Silva et al., 2020).

These simple tasks tap different cognitive skills including speed of processing, visual and integration skills, executive function, as well as access to phonological representations (Alves et al., 2016).

RAN correlates with reading accuracy and early reading fluency (Araujo et al., 2015). Araujo et al. (2015) in their study, fond that RAN has been found to be a stronger correlate of reading in school-aged children who had started formal literacy instruction.

A large-scale longitudinal study including different European orthographies showed that both RAN and phonological awareness were reliable predictors of reading skills, with equal relative importance (Caravolas et al., 2012). Intervention studies have found that training RAN can have a positive effect on word-level reading skills (Stappen & Reybroeck, 2018).

Although RAN and PA have been extensively studied in children learning to read in English (da Silva et al., 2020), to my knowledge almost there is not on study has focused on Arabic language. To that end, there are three research questions posed to address the research gap.

RQ 1. Are there correlation between RAN, PA and reading fluency?

RQ 2. How do RAN and PA contribute to reading fluency?

RQ 3. What are the relative contributions of RAN and PA to reading fluency?

Based on the literature review, phonological awareness and rapid automatic naming might affect reading fluency. phonological awareness might mediate the relationship between rapid automatic naming and reading fluency.

Methods

A quantitative, cross-sectional and descriptive method was employed, with the main focus on the contribution of rapid automatized naming skills and phonological awareness to Arabic language reading fluency. The study was conducted in November, 2021.

Participants

(a) to have no academic or developmental disabilities;

(b) Both gender (males and females).

Questionnaires were distributed to the children with the help of classroom teachers. All questions must be answered and responded to. A total of 360 participants aged 6-10 years took part in this study: 280 males (77.7%, age mean = 8.9, SD = 3.27), and 80 females (22.3%, age mean = 9.2, SD = 2.44).

Measures

RAN Scale. This scale was developed particularly for this research study to measure numbers, letters, colors, and objects based on Denckla and Rudel's scale (1974). Of the four tests of RAN consists of a chart containing 3 different items. The items used in RAN digit were 6, 7, 8, 9, 10 which are pronounced in Arabic.

The items used in RAN letter were /s/, /n/, /g/ /f/, /o/ which are pronounced in Arabic. The items used in RAN color were blue, red, yellow, brown, black which are pronounced in Arabic. The items used in RAN object were the pictures of “bus”, “plane”, “lion”, “house”, “school”.

The internal consistency of the survey was measured through Cronbach's alpha estimated at 0.84, 0.86, 0.85, 0.83 for RAN digit, RAN letter, RAN color, and RAN object respectively. A group of 6 experts examined the content validity. They indicated whether questions were, irrelevant, or highly relevant. All items were highly relevant. A content validity index at the item level (I-CVI) = 0.90.

Phonological Awareness Test

This test was developed particularly for this research study. Four aspects of phonological processing (Stampoltzis, Plakida & Peristeri, 2020): sentence awareness, syllabic awareness (analysis and synthesis), phonemic awareness (analysis and synthesis) and rhyme awareness (recognition and production) were developed. Correct answers were scored with 1 while wrong answers with 0. A total PA score was calculated per child by adding the subscores.

The internal consistency of the survey was measured through Cronbach's alpha estimated at 0.87, 0.88, 0.84, 0.81 for sentence awareness, syllabic awareness (analysis and synthesis), phonemic awareness (analysis and synthesis) and rhyme awareness (recognition and production) respectively. A group of 6 experts examined the content validity. They indicated whether questions were, irrelevant, or highly relevant. All items were highly relevant. A content validity index at the item level (I-CVI) = 0.90.

The Arabic Test of Reading Fluency

This test was developed particularly for this research study. It consists of one text which is based on “My Language” Arabic book. The text is accompanied by 10 questions that are used to assess reading comprehension. The child was asked to read aloud the text and his/her reading was tape-recorded so that his/her errors could be analysed through miscue analysis. The total reading time the child needed to read the text from the first to the last word was recorded. The child's oral answers to the ten comprehension questions were also evaluated and counted. A composite score of reading fluency based on the child's reading accuracy, speed and reading comprehension was calculated. The internal consistency of the test was measured through Cronbach's alpha estimated at 0.87. A group of 6 experts examined the content validity. They indicated whether questions were, irrelevant, or highly relevant. All items were highly relevant. A content validity index at the item level (I-CVI) = 0.90.

Procedure. Scales were piloted twice. For the pilot study one, four teachers of Arabic were asked to check whether the test instructions were clear. The second pilot study were for checking how long it took each child to answer the scale items and for checking psychometric properties. All the tests were of paper-and-pencil type. After obtaining written consents from parents and the school principals, children were asked to answer the scale items.

Data screening and Data Analysis

Figure 1. The Hypothesized Partial Mediation Model (Model 1)

Model fits were evaluated using multiple indices: chi-square statistics, comparative fit index (CFI), the Tucker-Lewis index (TLI), room mean square error of approximation (RMSEA) and standard root mean square residuals (SRMR). Hierarchial regression analysis was adopted to examine the relative contribution of PA to RAN and reading fluency.

Results

Descriptive Statistics and Correlations

Table 1. Descriptive Statistics

Table 2 Correlation Among the Three Scales

Path Analysis

Both Model 1 and Model 2 had a satisfactory fitness for the sample data. The model fit statistics for Model 1 (PMM) are X2 = .885 (df = 1, N = 360), p = 0.27, CFI = 0.98, TLI = 0.971, RMSEA = 0.05 (90% CI = 0.00-0.29), SRMR = 0.01. The model fit indices for Model 2 (CMM) are X2 = 2.317 (df =2, N = 360), p = 0.31, CFI = 0.98, TLI = 0.971, RMSEA = 0.031 (90% CI = 0.00 -0.16), SRMR = 0.02. Chi-square difference tests indicated that the partial mediated model (Model 1) and the complete mediation model (Model 2) were not statistically different (A/2 = 0.987 (1), p = 0.421). Therefore, CMM 2 was chosen to be the final model. Standardized path coefficients for the final model are shown in Fig. 3. In the final model, PA was directly and positively predictive of RAN (fi = 0.664, p = 0.000) and RAN was directly and strongly related to RF (fi = 0.623, p = 0.000).

Regression analysis were conducted to test the relative contributions of PA to RAN and RF. The findings show that PA explained 52.3% of variance of RF. RAN explained 51.8% of variance of RF. PA and RAN together explained 69.9% of variance of RF.

Figure 3 Final Path Model of the Contribution of PA and RAN to RF

Table 3 Hierarchical Multiple Regressions Predicating PA, RAN and RF