Advancing Dyslexia Assessment in Children Through Computerized Testing

Dyslexia is a neurodevelopmental disorder characterized by difficulties in accurate and/or fluent word recognition and poor spelling and decoding abilities and is characterized by unexpected and persistent difficulty in acquiring efficient reading skills despite conventional instruction, adequate intelligence, and sociocultural opportunity¹. This neurobiological disorder often manifests as challenges in reading, spelling, and writing, primarily due to phonological deficits^2,3. “The importance of early identification of dyslexia cannot be overstated, as it allows for timely intervention and support^4,5. When a student does not progress beyond Tier-3 in a response-to-intervention model, it becomes essential to conduct a more comprehensive assessment of both domain-general and domain-specific abilities associated with dyslexia, as highlighted by the scientific literature. The development of the technique presented here is grounded in the necessity of conducting thorough evaluations to ensure that appropriate interventions and support are provided. Moreover, previous studies underscore the utility of technology-based screening tools, such as web applications and computer games, in facilitating effective screening processes^6,7. These studies collectively highlight the multifaceted nature of dyslexia, emphasizing the need for comprehensive assessment and intervention strategies to address the diverse cognitive profiles of individuals with dyslexia. Despite the prevalence of dyslexia among school-aged children, most available diagnostic tools lack a framework that comprehensively assesses both domain-general and domain-specific skills. Moreover, there are minimal computerized options, particularly for Spanish-speaking populations. This multimedia battery addresses these gaps by leveraging technology to facilitate a detailed assessment of cognitive skills linked to dyslexia.

Theoretical perspectives and cognitive deficits in Dyslexia
Various theoretical models, including phonological, rapid auditory processing, visual, magnocellular, and cerebellar theories, aim to explain the causes of dyslexia and inform interventions (see for a review)⁸. The phonological theory attributes dyslexia to difficulties in processing language sounds⁹, while the rapid auditory processing theory links dyslexia to deficits in perceiving rapidly changing sounds¹⁰. Visual theory highlights the visual aspects of reading difficulties, and magnocellular theory points to impairments in visual and auditory processing pathways¹¹. The cerebellar theory suggests that dyslexia arises from cerebellar impairments affecting motor control and cognitive functions¹². Nicolson and Fawcett's Delayed Neural Commitment (DNC) framework posits that slower skill acquisition and delayed neural network development are central to dyslexia. Recent models, such as the multiple deficit model, propose that dyslexia is a complex disorder influenced by genetic, cognitive, and environmental factors^13,14,15. For instance, Ring and Black¹⁴ support the multiple deficit model, showing that both phonological and cognitive processing deficits contribute to the heterogeneity of dyslexia. Soriano-Ferrer et al.¹⁵ conducted a study with Spanish-speaking children with developmental dyslexia (DD) and found significant impairments in naming speed, verbal working memory, and phonological awareness (PA). Similarly, Zygouris et al.¹⁶ and Rauschenberger et al.⁶ underscore the importance of cognitive screening tools in identifying these deficits, with dyslexic individuals consistently scoring lower than typically achieving peers.

Examining technological approaches in Dyslexia screening: Insights from research studies
Research on dyslexia screening has evolved with three main approaches: early detection strategies, multifaceted screening methods combining various assessments, and integrating technology for enhanced efficiency¹⁷. Politi-Georgousi's¹⁸ recent systematic review highlights a shift toward more applications for intervening in dyslexia symptoms rather than screening processes, aligning with technology integration to improve reading skills in dyslexic students. Various tools exist, such as the Dyslexia Early Screening Test (DEST) by Fawcett and Nicolson, which assesses speed, phonological skills, motor skills, cerebellar function, and knowledge¹⁹. “Computer-based tools have advanced, including a web application assessing reading and cognitive skills in Greek children²⁰ and tools by Hautala et al.²¹ and Rauschenberg et al.⁶ that use gaming and machine learning for early identification of dyslexia. Ahmad et al. integrated gaming with neural networks, achieving 95% accuracy in detection²². Studies across different orthographies underscore the importance of phonological awareness and rapid automatized naming in dyslexia identification^23,24.

Insights into Dyslexia among Spanish-speaking children
The study of dyslexia in Spanish-speaking children has been significantly advanced through the use of Sicole-R technology. Jiménez et al. demonstrated its effectiveness in assessing dyslexia across age groups, particularly in distinguishing between dyslexic and typically achieving readers based on phonological and syntactic processing during early elementary years²⁵. Guzmán et al. investigated naming speed deficits in dyslexic children with phonological challenges, highlighting interactions between dyslexia and naming speed measured through tasks such as letter-RAN and number-RAN²⁶. Further studies by Jiménez et al. explored phonological awareness deficits across different syllable structures²⁷, while Ortiz et al. investigated speech perception deficits among Spanish children with dyslexia, revealing impairments in speech perception development regardless of phonetic contrast or linguistic unit^28,29. Jiménez et al. investigated the double-deficit hypothesis of dyslexia³⁰, followed by analyses of cognitive processes and gender-related disparities in dyslexia prevalence^31,32. Rodrigo et al. explored lexical access among Spanish dyslexic children³³, and Jiménez et al. scrutinized syntactic processing deficits³⁴. Finally, Jiménez et al. studied phonological and orthographical processes in dyslexic subtypes, highlighting differences in orthographic route efficiency³⁵. These studies collectively enhance our understanding of the cognitive and linguistic challenges of dyslexia in Spanish-speaking populations.

The conducted studies share several common characteristics in terms of the age and background of the participating children. The children included in these studies ranged in age from 7 to 14 years. Most studies focused on primary school children aged between 7 and 12 years, except those that included children up to 14 years old, providing a sample that spans from early school years to preadolescence^31,32. The participating children were primarily from the Canary Islands in Spain. Additionally, some studies included samples from other regions of Spain and Guatemala^31,32. Participants were recruited from both public and private schools whose backgrounds included urban and suburban areas. The socioeconomic levels represented in these studies range from low-middle to working and middle class.

Together, these inquiries significantly advance our understanding of dyslexia's complexities, contributing to the field of dyslexia research. Adapted for use across multiple Ibero-American countries, including Spain, Guatemala, Chile, and Mexico, the tool facilitates the assessment of diagnostic accuracy and precision in a diverse Spanish-speaking sample for this study.

This study aimed to delineate a protocol for diagnosing Spanish-speaking children with reading difficulties using a specialized multimedia battery. The primary goal is to provide a comprehensive assessment tool that evaluates both domain-general and domain-specific skills associated with dyslexia.

Experimental setup overview
The SICOLE-R was programmed in the Java 2 Platform Standard Edition (J2SE). The HSQL database engine is used as a database. The software includes 6 main modules to be evaluated: 1) perceptual processing, which includes the tasks of voicing, placing, and manner of articulation; 2) phonological processing, which includes phoneme isolation, phoneme deletion, phoneme segmentation, and phoneme blending tasks; 3) naming speed, which includes the tasks of naming speed in numbers, letters, colors and pictures; 4) orthographic processing, which includes tasks of morphological comprehension of lexemes and suffixes and homophone comprehension; 5) syntactic processing, including gender, number, function words, and grammatical structure tasks; and 6) semantic processing, which influences reading comprehension tasks through informative and narrative text. Instructions for each task, accompanied by one or two trials (depending on the task) and a demonstration, are delivered by a pedagogical agent prior to the initiation of the testing phase. The application protocol for each task is illustrated here.

Prior to administering the multimedia battery to the study sample, adaptations were made to the Spanish language modality for each country (i.e., Mexico, Guatemala, Ecuador, and Chile), including adjustments to vocabulary, images, and other relevant content. The administration conditions were the same across all Latin American countries. The administration environment had to be quiet within the school and free from noise, distractions, and interruptions. The duration of the multimedia battery administration ranged from 3-4 sessions of 30 min each, depending on the student's ability and age. Due to its database compatibility with most spreadsheet and statistical data processing systems, the evaluator can analyze the results of each child and each task. Concerning the data collection, two distinct task types were employed: 1) tasks where the examiner records students' oral performance, noting successes and errors using an external mouse, and 2) tasks requiring students to independently select options by clicking on them.