Reading Accuracy Research Articles

Dyslexia is a highly prevalent learning disability characterized by deficits in specific cognitive and linguistic skills which impair accurate and fluent reading of written words. Intensive, comprehensive, multicomponent interventions are effective in improving outcomes for students with dyslexia, but effective curriculum delivery has traditionally required an educator with substantial training. Many school systems lack sufficient staff with this level of expertise to successfully meet the needs of all their struggling readers. Towards this end, a technology-assisted dyslexia intervention was developed to provide teacher support through a virtual human avatar, resulting in significantly reduced training time while maintaining the comprehensive scope and structure of a traditional intervention model. This paper evaluates the comparative efficacy of the tech-assisted delivery model and the traditional model across two independent substudies. Results from quasi-experimental observational substudies in both laboratory school (n = 82) and public-school (n = 157) samples demonstrate non-inferiority, i.e., comparable student progress in reading and spelling, of the tech-assisted instruction compared to the traditional delivery model. Furthermore, despite differences in the educator training model, implementation fidelity was equivalently strong (>90%) in both groups. Findings provide encouraging evidence towards the scalability of effective dyslexia intervention by providing technology-based support at the level of the teacher. Implications for practice and questions for future research are discussed.

This study examines how language skills impact the oral reading abilities of the learners. It underscores the critical role of early language development in fostering literacy. The study investigates the relationships between listening, speaking, vocabulary, and comprehension skills and their influence on reading fluency and accuracy. Various data collection methods were employed, including assessments, reading evaluations, and teacher observations. The sample consisted of 30 purposively selected parents of Grade One students. Data were analyzed using frequency, percentage, weighted mean, and Pearson Correlation Coefficient (r). The findings revealed that learners with stronger language skills generally demonstrated better oral reading abilities, emphasizing the strong link between language development and literacy. Conversely, deficits in vocabulary and comprehension were found to impede reading performance. The study highlights the need for targeted language development interventions to promote early reading success. It recommends incorporating language enrichment activities into the curriculum and offering specific support for students who require additional assistance. By exploring the connection between language skills and reading proficiency, the research provides valuable insights for educators, curriculum designers, and policymakers focused on improving literacy outcomes for young learners.

Machine learning approaches for improving dosimeter reading accuracy conform with new operational quantities recommended by the ICRU 95.

This study examines the management of Quran education through bibliometric analysis and case studies, utilizing Scopus data from 2011 to 2025. Bibliometric findings reveal three key trend keywords that present opportunities for future research. The first is the Learning Management System (LMS), as the management of Quran education through LMS has gained significant attention in recent years. The second keyword is e-learning, particularly in the context of managing Quranic learning, and the third is computer-assisted instruction, which is becoming increasingly relevant due to the growing integration of technology in Quranic memorization. The process of managing Quran learning management aims to enhance student memorization, understanding, and engagement, with technological advancements, particularly online learning platforms and mobile applications, playing a crucial role in supporting Quran memorization. This study also explores the Qiroati method, a structured approach to Quran education that emphasizes reading accuracy and applying Tajwid rules. The research findings indicate that the Qiroati method is highly effective and sustainable, attributed to four key elements: a rigorous teacher recruitment and selection process, optimal preparation and organization of facilities and infrastructure, the implementation of high-quality learning strategies, and the use of measurable evaluation and supervision. In addition, the study discusses the integration of systematic planning, organizing, implementing, and supervising in the management of Qur'an education. The results underscore the effectiveness of these strategies in improving Qur'an learning outcomes and demonstrate that a comprehensive management framework can foster the growth and sustainability of educational institutions.

The early detection and diagnosis of diseases are of paramount importance for effective healthcare outcomes. Serum albumin is an important biomarker for many diseases. A reduction in albumin can be noted in various diseases, rendering it an important marker for assessing disease progression and treatment efficacy. To get accurate readings of these biomarker levels, we need methods that are very sensitive, reliable, quick, and non-invasive. Optical phase detection techniques exhibit high sensitivity, convenient readout strategies, and low limit of detection (LOD) in comparison to other detection methods. Here, we demonstrate a new phase detection mechanism employing a Fresnel biprism in conjunction with resonantly enhanced phase sensitivity achieved by using guided mode resonance (GMR). These elements are combined into a compact and bench-top common-path phase-detection system that can be realized with very few low-cost components. The common-path configuration effectively mitigates mechanical vibrations typically associated with two-beam interferometry used for phase detection. To demonstrate the capability of our approach, we designed and fabricated a GMR-based instrument and used it to detect bovine serum albumin (BSA) at different concentrations. The stability of the setup was instrumental in detecting BSA concentrations as low as 1 pg/mL.

Glucose is one of the most important biomolecules, playing a critical role in biological systems, food products, and clinical diagnostics. Conventional methods for glucose concentration measurement, such as spectrophotometry and enzymatic assays, although accurate, are often costly, time-consuming, and require skilled personnel. This study presents the development of a low-cost Arduino-based optical detection system for measuring glucose concentration using RGB LEDs as light sources and a photodiode sensor as the detector. The Arduino microcontroller processes voltage signals corresponding to light intensity and calculates absorbance values based on Beer-Lambert’s law. Calibration was performed using glucose solutions of varying concentrations, and the results indicated that the red LED source provided the most accurate and stable absorbance readings, with an average relative error below 5%. The proposed system offers an affordable, portable, and reliable alternative to conventional spectrophotometers, making it suitable for educational laboratories, small-scale food industries, and preliminary biomedical testing. Its implementation demonstrates the potential of microcontroller-based systems in enhancing accessibility to analytical tools in resource-limited environments.

Body Mass Index (BMI) is a parameter often used to evaluate the health status based on someone"™s height and weight ratio. However, the current existing BMI measurement and calculation are done manually. This research aims to develop an automatic BMI measuring tool called Obesity Meter (OBEmeter), by displaying the BMI score and body weight status. OBEMeter utilizes two sensors, namely ultrasonic (HC-SR04) to measure body height and load cell (HX711) to measure body weight. The use of ultrasonic sensors (HCSR04) and load cells (HX711) provides consistent height and weight measurements under various user conditions compared to other types of sensors. The height and weight data are then processed by Arduino in which the BMI calculation algorithm is already embedded. The BMI value is then converted into five-level body weight statuses, i.e. "ceking (underweight), kurus (slim), normal, kelebihan (overweight), and obese". This instrument is equipped with buzzer as an indicator if the BMI value is within the obese category. Our approach improves the measurement and calculation process by performing body and height measurement, then displaying the BMI in very short time automatically. Experimental results show that the reading accuracy of the ultrasonic sensor and load cell sensor are above 95%. Then, test results on 15 users show that the OBEmeter can produce the BMI measurement with 95,79% accuracy, which indicates that it can be utilized as a self-assist device to monitor the user"™s health status practically.

Early assessment of prereading abilities is important for ensuring long-term reading, academic, and career-related success. Speech-language pathologists and educators commonly use prereading assessment tools to identify and support school-aged children's future reading abilities. However, most bilingual children, including Urdu-English bilinguals, do not have access to appropriate early prereading assessments within the school/educational system. This is due to the lack of language-appropriate prereading assessment tools. The current longitudinal study examines the predictive validity of the linguistically and culturally responsive Urdu Phonological Tele-Assessment (U-PASS) tool, including subtests for phonological awareness and rapid automatized naming (RAN), commonly assessed reading precursors. Specifically, we investigated whether kindergarten-level Urdu phonological awareness and RAN skills predict the future Grade 1 Urdu reading accuracy and fluency skills of Urdu-English simultaneous bilinguals in two language contexts: in Pakistan (where Urdu is spoken as a national language; n = 104; Country Context 1) and in our exploratory study in Canada (where Urdu is spoken as a heritage language; n = 50; Country Context 2). Hierarchical linear regression analyses demonstrate predictive validity of the U-PASS tool across the two country contexts. Particularly, Urdu phonological awareness emerged as a consistent longitudinal predictor of Urdu word/nonword reading accuracy and fluency, while RAN was a reliable predictor of the reading fluency measures. The U-PASS tool provides access to linguistically and culturally responsive early prereading assessment and enables speech-language pathologists and educators to examine prereading skills in the heritage language of Urdu-speaking children across classrooms globally.

Auditory processing underlies phonological representation and presents neural oscillation lateralization in the brain. Atypical lateralization in auditory processing has been widely accepted as associated with impaired reading skills in alphabetic languages. However, whether Chinese adults with a reading difficulty (RD) history present atypical lateralization in auditory processing similar to that in alphabetic languages remains unknown. The purpose of this study was to investigate whether Chinese adults with poor reading would show atypical lateralization of neural oscillations during auditory sampling. Thirty-two adults with self-reported RD history and 44 adults without RD history were screened using the Chinese Adult Reading History Questionnaire. Reading accuracy, phonological accuracy, and rapid automatized naming (RAN) were assessed in all the participants. Auditory steady-state responses modulated at 10 to 80 Hz were recorded during a 5.4-sec white noise. Time-frequency power and phase synchrony indices were used to measure induced oscillatory power and synchrony of beta and gamma oscillations related to phonemic processing. Adults with a RD history performed worse than adults without RD history in reading accuracy and phonological accuracy. Adults with a RD history showed atypical rightward lateralization in the gamma band oscillation, whereas the adults without a RD history showed leftward lateralization. Adults with a RD history also demostrated reduced left-hemisphere oscillatory power and weaker bilateral synchrony. Event-related spectral perturbation in the left hemisphere correlated with reading accuracy in adults with RD history, while left-hemisphere lateralization of event-related spectral perturbation correlated with phonological accuracy in adults without RD history. In adults with RD history, the inter-trial phase synchrony in the left hemisphere correlated with RAN, and inter-trial phase synchrony in the right hemisphere correlated with reading accuracy and RAN, respectively. Adults with RD history demonstrated atypical rightward gamma band lateralization compared with adults without RD history, alongside reduced left-hemisphere oscillatory power and weaker bilateral synchrony. These neural patterns correlated with reading accuracy and phonological skills, supporting the hypothesis that auditory lateralization deficits underlie phonological processing challenges in Chinese, mirroring mechanisms observed in alphabetic languages.

The Qur’an was revealed in the Arabian Peninsula using the Arabic language; therefore, learning Arabic is essential to read, understand, and apply its teachings effectively. This study explores the use of the Ummi Method in Qur’anic learning at Sekolah Rendah Islam Al-Falah Bestari, Malaysia. The Ummi Method is a structured approach to reading the Qur’an that emphasizes tartil (measured recitation) based on tajwid principles and adopts a mother-tongue approach rooted in affection and empathy. It employs a combination of classical read-listen activities and a quality assurance system involving tashih (correction), tahsin (refinement), and teacher certification. The study focuses on three key quality aspects: qualified methodology, competent teachers, and a reliable system. Using a participatory observation method, the researcher directly engaged in classroom learning within a short research period. The findings revealed that many students initially struggled with correct recitation and memorization despite recognizing Arabic letters. However, after applying the Ummi Method, there was a noticeable improvement in their reading accuracy, fluency, and memorization of short surahs. This research contributes to strengthening Qur’anic pedagogy by demonstrating that the Ummi Method effectively enhances students’ motivation, comprehension, and spiritual connection through a systematic, affectionate, and quality-based learning framework

Jakarta faces escalating environmental challenges, including heightened flood risk and deteriorating air quality, driven by rising rainfall intensity and increasing pollution levels. Conventional monitoring systems for these hazards often operate in isolation, lacking the integration, realtime capability, and accessibility offered by modern Internet of Things (IoT) technology. To address this gap, this study designed and developed a unified, dual-microcontroller IoT platform for the simultaneous and integrated monitoring of flood potential and air pollution. The research followed an Experimental Development methodology, involving systematic hardware design, firmware development, system integration, and rigorous performance testing. The prototype hardware architecture strategically separates data acquisition and network communication by utilizing an Arduino Uno for data acquisition and an ESP32 microcontroller for network communication, respectively. The system incorporates an HC-SR04 ultrasonic sensor for water level detection, a DHT22 sensor for temperature and humidity measurement, and an MQ-135 gas sensor for assessing air quality. Data is displayed locally on a 20×4 LCD and transmitted to a cloud server. A critical finding from the integration phase was a 2.3% data loss rate attributable to serial communication instability between the microcontrollers, highlighting a significant challenge in multi-processor IoT architectures and underscoring the necessity for robust inter-processor protocols. During a comprehensive 24-hour endurance test with measurements taken at ten-minute intervals, the system demonstrated high accuracy in individual sensor readings. It successfully transmitted 97.7% of the data in realtime to a web application built on the Firebase platform. The study concludes that while the integrated dual-microcontroller approach is highly viable for holistic environmental monitoring, future iterations must prioritize enhanced communication reliability through hardware flow control and error-checking mechanisms to achieve the robustness required for mission-critical deployments.

Dyslexia is a neurobiological disorder characterized by reading difficulties, yet its causes remain unclear. Neuroimaging and behavioral studies found anomalous responses in tasks requiring phonological processing, motion perception, and implicit learning, and showed gray and white matter abnormalities in dyslexics compared to controls, indicating that dyslexia is highly heterogeneous and promoting a multifactorial approach. To evaluate whether combining behavioral and multimodal MRI improves sensitivity in identifying dyslexia neurocognitive traits compared to monocomponential approaches, 19 dyslexic and 19 control subjects underwent cognitive assessments, multiple (phonological, visual motion, rhythmic) mismatch-response functional MRI tasks, structural diffusion-weighted imaging (DWI) and T1-weighted imaging. Between group differences in the neurocognitive measures were tested with univariate and multivariate approaches. Results showed that dyslexics performed worse than controls in phonological tasks and presented reduced cerebellar responses to mismatching rhythmic stimuli, as well as structural disorganization in white matter tracts and cortical regions. Most importantly, a machine learning model trained with features from all three MRI modalities discriminated between dyslexics and controls with greater accuracy than single-modality models. The individual classification scores in the multimodal machine learning model correlated with behavioral reading accuracy. These results characterize dyslexia as a composite condition with multiple distinctive cognitive and brain traits.

This collective case study investigates how dyslexia affects reading ability in the first and second language among six Brazilian students with dyslexia and a control group without dyslexia in grades 3 to 5 at a bilingual primary school. Two reading-aloud tests, one with real words and one with pseudowords, were administered in English (L2) and Brazilian Portuguese (L1). Dyslexic students made more errors than their typically developing peers across all conditions of language, word frequency, word length, error type, and syllabic position. Some error types decreased with age among dyslexics, suggesting improvement over time. Both groups encountered greater difficulty with pseudowords, especially in Brazilian Portuguese (BP). In BP, dyslexics showed more rhyme and multisyllabic errors, which further illustrates how error patterns differed between languages.

Flood disasters in river basins often occur due to high rainfall, poor river management, and poor land use. To minimize the impact, this study designed a LoRa (Long Range)-based early warning system in watersheds for efficient long-distance data transmission and reception. This system integrates ultrasonic sensors to monitor water level, Arduino Uno microcontrollers and NodeMCU ESP8266 for data processing, LoRa as a communication medium, and a web server for real-time data display, on the web server the water level is divided into 3 statuses, namely Safe (0-20CM), Beware (21-30CM), and Danger (>31CM). An email-based early warning feature is also used to alert the Hazard status when the water level >31 CM. Tests show that this system has a high level of accuracy in water level readings of up to 50 cm and data transmission up to a distance of 1,000 meters in optimal conditions. With the efficiency of communication and real-time analysis, this system can improve community preparedness for flood disasters.

Deviant functional connectivity patterns in the EEG related to developmental dyslexia and their potential use for screening.

The Android-based GCS (Ground Control System) has been designed to operate the wireless prototype tractor. This research aims to design the GCS system as a controller for the prototype tractor. The research method employed is the sequential linear method. The conducted tests include data transmission time testing from the application, GPS testing, and prototype response testing. The testing involves comparing the command data transmission time from the application with the time it arrives at Firebase. Based on the obtained data, it can be seen that the data transmission speed and reception have no gaps. This is due to a stable network connection, enabling real-time communication between the application and Firebase. GPS testing is conducted to assess the accuracy of coordinate readings for determining the prototype's position. The prototype's position can be determined using the obtained coordinates, namely latitude and longitude, which are read from the GPS module installed on the device. The test results indicate a difference between the prototype's coordinate point (reference coordinate) and the coordinate point obtained from the timestamp application. The difference in coordinate readings between the prototype and the average plotted result is 0.2 meters. Prototype response testing is carried out to evaluate the device's responsiveness to commands from the control system on the smartphone. This test involves sending motion direction commands that the device needs to follow, followed by measuring how quickly the device responds to those motion commands. Based on the obtained data, the time required for the device to respond to the forward command provided is 01.61 seconds and 01.88 seconds. Keyword : Ground Control Station (GCS), Android, Waypoint, Wireless

Introduction. In modern industrial processes, pneumatic actuators with long-stroke movements play an important role. However, their use is limited by low accuracy resulting from the difficulties of controlling air flows. These limitations are caused by the compressibility of air and thermodynamic processes, which makes it urgent to improve the accuracy of such systems. The conducted analysis of scientific literature shows that modern research is mainly focused on the use of systems with standard cylinders with a working stroke limited to three meters. At the same time, the issues of development and research of long-stroke systems of rodless pneumatic drives, capable of having a stroke length of up to six meters, remain insufficiently studied. The introduction of advanced control systems in this type of drives involves significant investments in a high-tech electronic base and additional structural elements. In this regard, the development of fundamentally new technical solutions that allow for the efficient operation of mechanisms with a working stroke of more than three meters while maintaining the required technical parameters and economic efficiency, is of particular relevance. In the framework of previous studies, the author proposed a design of a pneumatic drive for long-stroke movements, equipped with a unique control system based on a jet sensor and an external brake mechanism. Its mathematical modeling and theoretical analysis were also performed, which made it possible to identify key factors affecting the accuracy of positioning. To validate the mathematical model and the hypotheses put forward, the objective of this research is to experimentally verify the results of mathematical modeling of a positioning long-stroke rodless pneumatic actuator, as well as to confirm the degree of influence of key factors on positioning accuracy.Materials and Methods. The work involved a stand that was a technical model of a pneumatic drive with an original control system, including a jet sensor and an external brake device. To verify the operability and accuracy of the jet sensor readings, the spillage method was applied using the Camozzi MF4008-10-R-BV-A flow sensor after the element under study, and Camozzi SWCN-P10-P3-2 pressure sensors placed before and after the considered element. The tests conducted on the developed jet sensor showed high reliability and stability of operation in various operating modes. The experimental study of a long-stroke rodless pneumatic actuator included evaluation of the actuator's technical capabilities, analysis of positional cycles, study on the effect of external factors, and comparison of the results of computational and full-scale experiments. The results of computational and full-scale experiments were processed using the Mathcad and MATLAB software packages. The dependences of positioning accuracy on mass and stroke length were constructed.Results. The reliability of the model was established at the level of the maximum discrepancy between the experimental data and the results of mathematical modeling, which amounted to 18%. That confirmed the adequacy of the developed model for engineering calculations. The effect of the load mass on the accuracy of positioning was experimentally established. With an increase in mass from 10 to 30 kg, the accuracy decreased by 1.47 times, and with a mass of 60 kg, the accuracy deteriorated by another 1.37 times relative to the base mass of 10 kg. In addition, the effect of stop coordinates was studied: the dependence of positioning accuracy on the position of the actuator was established. When moving from 0.1 m to 0.22 m, the accuracy deteriorated by 3.2 times, but with further movement to 0.35 m, it improved by 2.2 times.Discussion. The conducted experimental studies allowed achieving good results in the development of long-stroke pneumatic drives. Successful verification of the mathematical model confirmed the correctness of both the model itself and the theoretical studies conducted in the author's previous works. The positioning accuracy of the drive of 77 microns at a distance of over three meters was reached. This indicator significantly exceeds the results presented in the studies of other authors, which shows the high potential of the developed design. The economic efficiency of the proposed solution is due to the absence of an electronic component base in the control system. This not only reduces initial production costs, but also significantly simplifies maintenance of the drive under operation. The comparative analysis with existing developments confirms the superiority of the proposed system in terms of cost criteria.Conclusion. The conducted studies confirmed the efficiency of the developed solutions for a long-stroke rodless pneumatic actuator. Practical significance of the study is determined by the possibility of using the obtained results in creating high-precision long-stroke mechanisms in various industries. The developed design can be used in automated production lines, robotic complexes, and other areas where precise positioning over significant distances is required. Promising areas for further research are the optimization of the control system parameters to reach even higher positioning accuracy, and the development of calculation methods for positioning long-stroke pneumatic drives.

Introduction. The implementation of automated weather stations facilitated by Internet of Things (IoT) technologies represents a significant advancement in smart agriculture. Modern web services and applications using the data of IoT-based automated weather stations provide users with representative meteorological data on climatic conditions in real time to enhance the field operations management and reduce risks from changing in meteorological conditions. An important aspect of collecting and analyzing up-to-date meteorological information is the development of user-friendly interfaces. Aim of the Study. The study is aimed at developing and testing a hardware-software complex with a web interface for displaying local meteorological monitoring data with the frequency of data displaying at least once per hour. Materials and Methods. The study employs modern web development tool – ASP.NET Core MVC platform to achieve the target tasks. There has been developed an experimental prototype of the low-cost IoT automated weather station to collect data on local weather conditions during the growing season. There has been also developed the hardware and software architecture of the IoT automated weather station. The Raspberry Pi Zero microcomputer provides execution of scripts for polling a multi-channel combined weather sensor in the Java programming language for recording in a local and remote PostgreSQL database management system. The graphs of local weather parameters dynamics have been implemented based on the ApexCharts JavaScript library. In the field conditions, there have been studied the energy consumption and battery charge dynamics of the IoT automated weather station from a solar panel. Results. The algorithm for information retrieval from a database and displaying graphs and tables on a website using a web application has been developed and tested. The key code blocks with comments are presented, and an algorithm for deploying a web application on the Internet is described. A frontend for a web application for visualizing IoT automated weather station data has been developed. The dynamics of meteorological conditions obtained by the IoT automated weather station are presented, and the results of comparing individual indicators with data from open sources are presented. The web application has been tested and deployed on a hardware server with Internet access. This paper presents a comprehensive review of recent advancements in smart weather stations; a comparative analysis of software technologies for real-time weather monitoring data visualization is conducted. Discussion and Conclusion. The results of field tests of the IoT automated weather station and web application in 2024 showed high system performance, minimal delays under adverse environmental conditions (heavy rain, wind, low and high temperatures), stable database filling and display of weather conditions graphs on the website. The developed IoT automated weather station, built on a modular principle, with a combined weather sensor, is more compact and cost-effective compared to ready-made solutions existing on the market. Continuous information flow and open hardware architecture ensures the autonomy of the system due to battery recharging from a solar panel and a sleep mode algorithm. In the future, it is planned to calibrate the combined sensor in laboratory conditions to improve the accuracy of readings, or replace the combined sensor with classic mechanical sensors with minor changes in the hardware and software platform. To work with dynamic models of the production process, it is planned to add an interface and field sensors to the system.

Introduction. A study of world experience in the field of diagnostics of reading difficulties for adults shows that there are a number of standardized tests. However, their transfer to domestic practice is a methodological problem due to the variable understanding of the term «dyslexia»; the focus of most foreign tests on reading words, not texts; the uniqueness of the linguistic characteristics of different languages. The relevance of developing standardized text tasks for a valid quantitative assessment of the reading capabilities of adults in domestic practice is due to the need to create scientifically based diagnostic methods. The purpose of this article is to analyze international diagnostic tests for assessing adult reading and to describe the main characteristics of diagnostic tools for studying reading in the Russian-speaking segment of the population.Materials and methods. Validation of the results of a pilot study of written and speech activities of students of a pedagogical university (2015-2023) and analysis of the content of foreign reading tests for adults TOWRE, GORT, YAA-R, CC2A, BDT, DAST, MDDDT-A, TIL, SLS-Berlin, NART, and the Programme for the International Assessment of Adult Competencies (PIAAC) for people aged 16-65 years made it possible to determine the main provisions of the protocol for conducting a full-scale study of reading among Russian-speaking adults.Results. A protocol for a sample study of reading characteristics of Russian-speaking people aged 16-65 was developed, inclusion and exclusion criteria were defined. We constructed a diagnostic instrument «CHITOPUS: reading test for adults». It includes four texts for reading aloud and silently. Comparison of test material by parameters: style, volume, structural and lexical complexity, readability, as well as additional comparison of propositions in texts with implicit meaning showed a balance of lexical tasks by linguostylistic characteristics.Discussion and conclusions. Insufficient development of materials in domestic practice that allow for reliable assessment of various aspects of reading in adults actualizes the need to prepare diagnostic tools for studying reading. The key aspect of diagnostics will be the definition of normative reading parameters. This requires a systematic study of various reading characteristics, including speed, accuracy, method, expressiveness and understanding of the text read. It is important to establish reference indicators, deviation from which can be interpreted as a manifestation of a disorder, possibly developmental dyslexia. The study of reading characteristics will allow replenishing data on the profiles for Russian-speaking adults with dyslexia.

According to Heroman and Jones (2004), Phonological awareness is one of the strongest predictors of reading. Phonological awareness is the ability to detect, discern, and utilize any phonological structure of a language regardless of the size of the word unit. Although phonological awareness has been found to correlate with reading in cross-linguistic studies of Romance languages, it is still unknown how it is manifested in linguistically complex countries such as Morocco. Thus, this study seeks to examine the extent to which phonological awareness correlates with word reading in Modern Standard Arabic. Phoneme and syllable awareness tasks were administered to assess children’s phonological awareness and real and pseudowords were administered to examine children’s reading accuracy. The results indicated a moderate positive correlation between phonological awareness and word reading (r (43) = .667, p < .01) and a stronger correlation observed between phonological awareness and real word reading (r = .75) compared to pseudoword reading (r = .62). The results underscore the role of phonological awareness in reading acquisition, particularly in the context of Arabic orthography.