Discriminant Analysis of Cognitive Linguistic Indicators for Early Identification of Dyslexia in Nigerian Primary School Pupils

Discriminant Analysis of Cognitive Linguistic Indicators for Early Identification of Dyslexia in Nigerian Primary School Pupils | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Discriminant Analysis of Cognitive Linguistic Indicators for Early Identification of Dyslexia in Nigerian Primary School Pupils Kirfi-Aliyu Bello, Irfan Nufal Umar This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8499892/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract There is a reasonable number of pupils suffering from dyslexia in Nigeria; however, very little has been done to identify and provide targeted support for them. The absence of reliable, context-specific diagnostic tools has made early detection challenging, leaving many children to struggle with persistent reading and learning difficulties. This study applied the Primary Reading Inventory (PRI) model to predict and identify dyslexic pupils using three cognitive-linguistic indicators: Phonemic Awareness (PA), Rapid Naming and Vocabulary (RNV), and Word Recognition and Meaning (WRM). Discriminant analysis was employed to determine the capacity of these indicators to differentiate between dyslexic and non-dyslexic pupils. The results revealed a strong canonical correlation coefficient (0.88) and a low Wilks’ Lambda value (0.224), indicating a high level of discrimination between the groups. The analysis further showed that Phonemic Awareness was the most influential predictor, followed by RNV and WRM, with the overall model achieving a classification accuracy of 97.2%. These findings demonstrate the reliability of the PRI model as an effective and practical approach for identifying dyslexia within school contexts. The study underscores the importance of integrating statistical approaches with established cognitive-linguistic models to identify dyslexic children across schools in Nigeria and other African cities, thereby promoting inclusive education, facilitating targeted interventions, and fostering equitable learning outcomes for pupils with reading difficulties. 1.0 Introduction Dyslexia is a learning disability that presents a significant challenge for individuals termed as having specific reading disabilities (Agu & Omenyi, 2020 ; Eyo & Nkanga, 2020 ). This group encounters obstacles in excelling within a competitive society due to mental, social, or physical impairments (Ihekwoaba et al., 2023 ). Dyslexia, defined as a reading disability, profoundly restricts an individual's ability to read accurately, swiftly, and comprehend written content (Chitsa & Mpofu, 2016 ; Leseyane et al., 2018 ). To be considered substantial, this limitation extends beyond mere difficulty and must impact both academic performance and daily activities. Dyslexia in children is a neurodevelopmental disorder primarily affecting reading skills (Altin et al., 2023 ). Other challenges associated with dyslexia encompass difficulties in accurate word recognition, spelling, decoding, and mathematical calculation (Treiman et al., 2015 ; Uzoekwe Helen & Ezeani Nneka, 2018). These challenges are distinct from factors like intellectual disabilities, visual, or auditory impairments (Adekola & Olarewaju, 2017 ; Altin et al., 2023 ; Sturm et al., 2021 ). Despite average to above-average cognitive abilities observed in many individuals with dyslexia (Kandel & Perret, 2015 ), they encounter significant difficulties in segmenting words into distinct sounds and recognizing words, leading to reading struggles (Agu & Omenyi, 2020 ). Globally, dyslexia is recognized as a prevalent learning disability, impacting 15–20% of the population (Ebere, 2016 ); International Dyslexia Association (IDA), 2019; Skiada, Soroniati, Gardeli, & Zissis, 2014). This developmental issue affects schoolchildren irrespective of gender or age, underscoring its significance in the educational landscape (Skiada et al., 2014). The high prevalence of dyslexia, affecting as many as one in five children, highlights the urgent need for comprehensive research and understanding in this field. With 80 to 90 percent of children with learning disorders experiencing dyslexia, the impact of this neurodevelopmental condition on the educational landscape is substantial. However, despite dyslexia being acknowledged as a primary learning disorder among children, it has received very few scholarly attentions in developing nations, including Nigeria (Makgato et al., 2022 ). Consequently, this led to notable scarcity of readily available statistics and widespread awareness regarding dyslexia in Nigeria. This knowledge gap hinders the implementation of effective interventions and support systems for children grappling with dyslexia. The lack of awareness about dyslexia within Nigeria, as highlighted by the Dyslexia Foundation of Nigeria (Okechukwu et al., 2023 ), has profound implications. This knowledge gap contributes to a harsh reality for children contending with dyslexia, subjecting them to physical punishment, derogatory name-calling, bullying, and shaming in the class (Ngwu & Nuhu, 2022). A report from The Guardian in 2018 sheds light on the distressing reality that these young individuals encounter, pushing them to develop a sense of inferiority due to the lack of understanding and empathy surrounding dyslexia within Nigerian society. In Nigeria, despite the global advancements in e-learning, substantial challenges hinder its efficacy, particularly for individuals grappling with dyslexia (Haroun et al., 2024 ). The traditional e-learning programs often necessitate a certain level of computer proficiency and familiarity with graphical user interfaces (GUI) like WINDOWS or IOS, thereby creating a digital divide for those lacking such skills. Consequently, the existing challenges in e-learning technology in Nigeria underscore the imperative for a more inclusive approach that explicitly addresses the unique learning needs of individuals with dyslexia. Additionally, children with dyslexia often face persistent challenges in learning, particularly in reading, spelling, and writing, which significantly affect their academic progress and self-esteem (Cheng-Lai et al., 2013 ; Chitsa & Mpofu, 2016 ). In many Nigerian schools, these difficulties are frequently misunderstood as signs of laziness or lack of intelligence rather than as indicators of a specific learning difference (Fletcher et al., 1998 ; Gus & Samuelsson, 1999 ). Teachers often struggle to identify dyslexic pupils due to limited training, inadequate diagnostic tools, and the absence of structured screening frameworks within the educational system (Chitsa & Mpofu, 2016 ; Okechukwu et al., 2023 ). Consequently, many children with dyslexia remain undiagnosed and fail to receive the targeted instructional support they need to succeed (Cassidy et al., 2023 ). Therefore, the first and most critical step in supporting pupils with dyslexia is proper identification (Germano et al., 2017 ). Early and accurate identification enables the implementation of targeted interventions that address specific learning deficits rather than relying on generalized teaching methods that may not meet individual needs (Javed et al., 2024 ). Recognizing dyslexic pupils allows teachers to adopt differentiated instructional strategies, such as multisensory learning techniques and phonics-based reading interventions, which have been shown to improve literacy outcomes (Adubasim, 2018 ; Daniel et al., 2024 ; Malekian & Askari, 2013 ). It also empowers teachers to be more patient and responsive to the unique challenges faced by such pupils, fostering an inclusive classroom environment. Moreover, early identification provides parents with valuable insight into their child’s learning difficulties, helping them to offer emotional support, collaborate effectively with teachers, and seek specialized assistance when necessary (Ngwu & Nuhu, 2022). Identifying dyslexic children has been enhanced through the use of various techniques, each providing distinct insights into the cognitive and linguistic characteristics linked to dyslexia (Colenbrander et al., 2018 ; Daniel et al., 2024 ). However, there remains considerable variability in the approaches employed for detection across studies and contexts. For instance, (Parmar & Paunwala ( 2023 ) utilized EEG recordings to identify neural signatures of dyslexia at an early age, demonstrating the potential of neurophysiological methods in detecting subtle brain activity differences linked to reading and language processing. In contrast, Acheampong et al. ( 2019 ) adopted descriptive statistical methods to analyze behavioral and performance- based indicators of dyslexia, highlighting the value of quantitative assessment tools in classroom-based evaluations. Similarly, (Novita et al. ( 2019 ) applied Multivariate Analysis of Variance (MANOVA) and Discriminant Analysis to identify and compare dyslexic children across three countries, finding these statistical techniques effective in distinguishing dyslexic from non-dyslexic learners based on cognitive- linguistic variables. Building on this approach, the present study employs discriminant analysis to predict and identify dyslexic pupils among primary school children in Bauchi city. This method provides a systematic, data-driven means of classifying pupils based on measurable test performance, thereby offering a reliable framework for early detection and targeted educational intervention. 2.0 Theoretical Framework Dyslexia is widely understood to originate from two principal domains: language processing deficits (cognitive–linguistic disorder) and visual processing deficits (cognitive–visual disorder) (Adanna, 2020 ; Loftin, 2022 ). These deficiencies are conceptualized as cognitive disorders that affect the acquisition and processing of written language rather than a reflection of intellectual impairment (Adlof & Hogan, 2018 ; Hamidi & Beygmohammadloo, 2025 ). Early researchers investigating dyslexia explored multiple approaches in an effort to understand its origin and establish reliable identification models (Berninger et al., 2008 ; Denckla & Rudel, 1976 ). Initial studies were largely exploratory, relying on surveys, observational data, and hypotheses aimed at determining whether visual deficits were the primary source of dyslexic symptoms (Willows et al., 2012 ). This line of inquiry reflected the early belief that difficulties in visual perception or eye movement were responsible for reading impairments (Daniel et al., 2024 ). As research evolved, attention shifted toward cognitive explanations, particularly the relationship between intelligence and reading ability. The IQ–reading discrepancy model subsequently emerged as a dominant framework throughout much of the twentieth century (Daniel et al., 2024 ; Ferrer et al., 2010 ). This model posited that dyslexia could be identified when a learner’s reading achievement was significantly lower than what would be expected based on their measured intelligence quotient (IQ) (Shipley and Jones, 1969; Mason, 1967 ). However, later empirical studies questioned the validity of this approach. Scholars such as Iovino et al. ( 1998 ) and Fletcher et al. ( 1998 ) argued that the discrepancy model lacked reliability and consistency in accurately identifying dyslexic individuals. They emphasized that reading difficulties occur across the full spectrum of intellectual ability and that reliance on IQ discrepancy often excluded struggling readers who required intervention but did not meet the “discrepancy” threshold. Similarly, Benson et al. (2020) reported that school psychologists in the United States continue to employ a range of frameworks for identifying dyslexia, some of which remain outdated, such as the intelligence– achievement discrepancy model. This approach assumes that dyslexia can only be diagnosed when there is a marked gap between a pupil’s intellectual ability and academic performance, and when all other possible causes of reading difficulties have been ruled out (Ebere, 2016 ). Likewise, Cheng-Lai et al. ( 2013 ) examined the relationship between writing-to-dictation, handwriting, orthographic, and perceptual–motor skills among Chinese children with dyslexia. Dyslexic pupils were identified through dictation and writing tasks, and the results of stepwise multiple regression analyses revealed that Chinese character naming was the only significant predictor of word dictation performance. In light of these limitations, contemporary research has shifted toward multifactorial and processing-based models that emphasize phonological awareness, rapid naming, working memory, and other cognitive– linguistic variables as more valid indicators of dyslexia than IQ-based comparisons (Badian, 2001 ; Treiman et al., 2019 ; Wagner et al., 1999 ). In response to the inadequacies of traditional models, frameworks such as the Response to Intervention (RTI) and the Primary Reading Inventory (PRI) have emerged as more comprehensive and effective approaches (Novita et al., 2019 ). The RTI model identifies reading difficulties through continuous progress monitoring, emphasizing early intervention and instructional responsiveness rather than static ability measures. Similarly, the PRI model offers a structured and diagnostic framework that directly assesses the core components of reading — Word Recognition and Meaning, Rapid Naming and Vocabulary, and Phonemic Awareness — allowing for more accurate differentiation between dyslexic and non-dyslexic pupils. Together, these models address the weaknesses of earlier methods by focusing on measurable cognitive and linguistic processes that underpin reading development and disability. 2.1 Research Question How effectively does the Primary Reading Inventory (PRI) model distinguish between dyslexic and non- dyslexic pupils based on cognitive-linguistic indicators? 2.2 Research Hypothesis H₁ The Primary Reading Inventory model, based on Word Recognition and Meaning, Rapid Naming and Vocabulary, and Phonemic Awareness, can effectively differentiate between dyslexic and non-dyslexic pupils. 3.0 Methods 3.1 Methodological Approach This chapter explains the research framework, providing a comprehensive roadmap for executing the research objectives (Bhatta et al., 2025 ). It conceptualizes and outlines a step-by-step procedure meticulously designed to attain the desired goal of identifying pupils with dyslexia in some selected primary schools in Bauchi (Ahuja, 2011 ; Gakuu et al., 2016 ). Utilizing a statistical approach, specifically using discriminant analysis, the study accurately predict and identified children with dyslexia in some selected primary schools in Bauchi. This was achieved through a robust screening process utilizing established models for accurate and efficient assessment (Clark et al., 2019; Rauschenberger et al., 2020). Similarly, the study utilized the Primary Reading Inventory (PRI) model, which deploys 3- to 5 screening stages to identify children at risk of reading problems, including dyslexia, within the primary school groups (Fletcher et al., 2021 ). The PRI model provides a structured approach to examining pupils’ reading abilities across three (3) key domains that influence literacy development (Adekola & Olarewaju, 2017 ; Geertsema et al., 2022 ). Understanding how these domains interact to shape reading performance is critical for the early detection and remediation of dyslexia (Snowling et al., 2020 ). The three core components of the PRI model—Word Recognition and Meaning, Rapid Naming and Vocabulary, and Phonemic Awareness—are examined to determine their discriminant power in accurately classifying pupils as either dyslexic or non-dyslexic (Novita et al., 2019 ). Additionally, PRI provides a more in-depth 30-minute inventory to determine specific reading concepts that need to be addressed, ensuring a comprehensive understanding of individual needs. Building on this foundation, the study integrates insights from the National Institutes of Child Health and Human Development (NICHD)-funded early assessment of reading skills (EARS) project (Badian, 2001 ). This longitudinal assessment of reading precursor skills in children enhances the screening stages, providing a more nuanced evaluation (Fletcher et al., 2021 ). The screening process, guided by these validated models, aims to identify dyslexia with precision and efficiency, facilitating a targeted and informed approach in subsequent research stages. 3.2 Population and Sampling The population size of 85 participants was derived from two primary schools, selected based on recommendations from teachers familiar with the students' learning behaviors and potential dyslexia indicators. These are Mai Kalam Inclusive Academy and Sunshine International School, both located in metropolitan Bauchi. The sample size was deemed adequate for the study's objectives, ensuring a diverse representation of pupils from different backgrounds and learning environments (Denckla & Rudel, 1976 ; Novita et al., 2019 ). By incorporating input from teachers who interact closely with the students daily, the selection process aimed to capture a comprehensive range of potential dyslexic traits and learning challenges (Chitsa & Mpofu, 2016 ; Fletcher et al., 2021 ). Furthermore, the inclusion of participants from two schools enhances the generalizability of the findings, offering insights into dyslexia prevalence and identification practices across different educational settings. The participant selection for this study targeted children aged 4 to 10 years, covering primary levels 1 to 6, with an inclusive approach toward both genders (Huang et al., 2020 ; Makgato et al., 2022 ). Purposive sampling was chosen to ensure that the participants selected met specific criteria relevant to the study's objectives, such as age range, school enrollment, and absence of sensory, motor, or cognitive impairments (Ahuja, 2011 ). This approach allowed for the recruitment of a targeted sample population that could provide valuable insights into the research questions and intervention outcomes. 3.3 Inclusion and Exclusion Criteria: The inclusion criteria were stringent, requiring participants to have parents or guardians who provided signed Informed Consent forms. Moreover, participants were required not to exhibit sensory, motor, or cognitive impairments (Kandel & Perret, 2015 ). Similarly, the exclusion criteria were also applied to children with sensory, motor, or cognitive impairments, as well as those whose parents or guardians did not sign the Informed Consent form. This careful selection aimed to create a homogeneous group, ensuring that the study focused on children within the specified age and class range who did not have pre-existing conditions that could impact the results (Fletcher et al., 2021 ; Huang et al., 2020 ). 3.4 Dyslexia Screening Sessions The experiment took place in the computer room of the Universal Basic Education Commission Zonal office in Bauchi, Bauchi State, Nigeria. To maintain a controlled environment, the experiment was conducted during the summer break when participants were on holidays, preventing interactions among them during treatment sessions. Additionally, the experiment was carried out individually for each participant, ensuring that the impact of the developed e-learning multi-sensory intervention on learning outcomes could be accurately assessed for each child. To identify children with dyslexia, the study employs five distinct screening stages utilizing various techniques to detect children at risk of dyslexia among two primary schools in Bauchi. 3.4.1 Rapid Naming and Vocabulary Assessment: During this session, the study adopted the rapid automized naming test by Denckla & Rudel, ( 1976 ). Participants were presented with a test consisting of commonly used lowercase letters, such as "a," "d," "o," "s," and "p," repeated in random sequences on a large paper. Their task was to name each letter as quickly as possible within a 60-second time frame. The number of correct responses named within this time limit was then converted to a rate of correct responses per second. This test evaluates the speed and accuracy of letter naming, which is often impaired in individuals with dyslexia and serves as a key indicator of reading difficulties. Additionally, participants were asked to take the Peabody Picture Vocabulary Test-Revised (Dunn & Dunn, 1981 ), a norm-referenced measure of receptive vocabulary. During the test, a stimulus word was presented orally to the child, who then viewed four corresponding pictures. The child's task was to select the picture that best represented the meaning of the stimulus word, providing an indication of their receptive vocabulary skills. 3.4.2 Phonemic Awareness Assessment: During this session, the Wagner's et al., (1999) Comprehensive test of Phonological process (CTPP) was adopted using five measures of phonological awareness. Participants were provided with a set of twenty- six letters of the alphabet. The test required learners to match each letter with its corresponding phonemic sound. The total score for the test, outlined in Appendix A, is 50 marks, with learners achieving a complete score by correctly identifying and matching all alphabetic letters with their respective phonemic sounds. This evaluation aims to assess the participants' ability to recognize and manipulate phonemes, which is crucial for proficient reading and language development. 3.4.3 Word Recognition and Meaning Assessment: During this session, participants were presented with 15 words individually on cards. Each participant was instructed to identify and read the words aloud one at a time. This evaluation aimed to measure the participants' proficiency in reading words, providing insight into their reading fluency and accuracy (Adekola & Olarewaju, 2017 ). 4.0 Results Discriminant analysis is a multivariate statistical technique used to identify variables that best distinguish between two or more groups (Huberty, 1974 ). In the context of this study, it is particularly suitable for differentiating between dyslexic and non-dyslexic pupils by examining how cognitive and linguistic indicators such as phonemic awareness, rapid naming, and word recognition contribute to group separation (Novita et al., 2019 ). This allows for the identification of the most significant predictors that classify pupils accurately based on their reading-related abilities. 4.1 Test for Interaction Effects Undertaking discriminant analysis requires certain validity and reliability tests to ensure that the discriminant function and its predictors accurately distinguish between the groups being studied (Novita et al., 2019 ; Sohail & Chen, 2022 ). Validity testing confirms that the chosen variables truly represent the constructs under investigation, while reliability testing ensures that the measurements are consistent and dependable across samples. For the purpose of assessing the validity and reliability of the instrument, certain statistical procedures were employed (Sohail & Chen, 2022 ). This procedure is implemented to ensure that the model’s construct appropriately captures the underlying measurement and predictor variables for discriminant analysis (Akbari et al., 2021 ). Specifically, Wilks’ Lambda and the eigenvalue of the discriminant function were used to evaluate the model’s capacity to accurately distinguish between groups (Ab Hamid et al., 2017 ; Thia, 2023 ) (See Table 1 ). The Wilks’ Lambda is a multivariate test statistic used in discriminant analysis to evaluate the extent to which the predictor variables collectively distinguish between predefined groups, in this case between dyslexic and non-dyslexic pupils. Table 1 Validity and Reliability Test Eigenvalues Wilk’s Metrics Eigenvalue Canonical correlation Wilk’s Lambda Sig. 3.466 .881 .224 0.000 Table 1 . Validity and Reliability Test It represents the proportion of total variance in the discriminant function that is not explained by group differences. Values range between 0 and 1, with values closer to zero indicating strong discriminatory power and substantial separation between groups, while values approaching one suggest weak discrimination (Akbari et al., 2021 ; Thia, 2023 ). The Wilk’s Lambda of 0.224 indicates strong discriminatory power between dyslexic and non-dyslexic groups (Novita et al., 2019 ). Similarly, with a P < .000, the findings reveal a statistically significant Wilks’ Lambda which indicates that the set of predictors meaningfully differentiates the groups (See Table 1 ). The assessment results for each test performance revealing initial dyslexic risk across the three measures, namely PA, RNV, and WRM, in identifying suspected dyslexic pupils (see Table 2 ). The Phonemic Awareness (PA) test demonstrated the strongest predictive indication, identifying 39 pupils as suspected dyslexic and 46 pupils as non-suspecting. The Rapid Naming and Vocabulary (RNV) test followed, identifying 32 pupils as suspected dyslexic and 53 pupils as non-suspecting. In contrast, the Word Recognition and Meaning (WRM) test identified only 3 pupils as suspected dyslexic, while 82 pupils were classified as non-suspecting. Table 2 . Dyslexia Risk Classification Based on Individual Cognitive Test Performance The assessment results for individual test reveal varying levels of prediction across the three tests namely WRM, RNV and PA in identifying suspected dyslexic pupils (See Table 2 ). The WRM test identified only 3 pupils as suspected dyslexic and 82 pupils as non-suspecting based on the pupils’ individual scores. For the RNV test, 32 pupils were identified as suspected dyslexic, whereas 53 pupils were categorized as non- suspecting. In contrast, the PA test identified 39 suspected dyslexic pupils, with 46 pupils classified as non- suspecting. These results indicate that the PA test produced the highest number of suspected dyslexic classifications, followed by the WRM test and then the RNV test, prior to considering the cumulative discriminant outcomes. The test scores of the three different cognitive linguistic abilities demonstrate clear variation in pupils’ performance, with evident reading-related learning challenges (See Table 3). The PA test recorded the lowest success rate among pupils suspected of dyslexia (average score = 3/50), indicating significant difficulty in sound manipulation and phonological awareness tasks (Treiman et al., 2019 ). This reinforces established evidence that phonological processing deficits represent a core feature of dyslexia and often manifest as early challenges in distinguishing, segmenting, and blending speech sounds (Badian, 2001 ; Wagner et al., 1999 ). Similarly, performance on the RNV test supports the PA findings in identifying high- risk dyslexic pupils (average score = 5/50), revealing that children with dyslexia struggle with tasks requiring rapid lexical retrieval and symbol-sound association (Germano et al., 2017 ). Reduced efficiency in rapid naming has been consistently linked to difficulties in reading fluency, and the lower performance observed in this domain further supports its value as a sensitive indicator of dyslexic tendencies (Adekola & Olarewaju, 2017 ; Ebere, 2016 ). In contrast, pupils performed comparatively better on the WRM test. The higher pass rate among suspected dyslexic pupils in this domain may suggest that word-recognition challenges emerge more gradually or may be compensated for through memorization or repeated exposure to familiar vocabulary in classroom settings. This trend highlights that, although word recognition difficulties are part of the dyslexia profile, they may not be as prominent in early screening as deficits in phonemic awareness and rapid naming. Table 4 . presents significant findings for the study. The high canonical correlation coefficient (0.88) indicates a strong linear relationship between the discriminant function and the predictor variables, confirming that the selected indicators collectively explain a substantial portion of the variance between the two groups (Novita et al., 2019 ). Table 4 Discriminatory functions and Canonical Correlations Ave. Scores Number PA Test 3 Dyslexic RNV Test 5 13 WRM test 21 PA test 16 Non-dyslexic RNV Test 17 72 WRM test 40 Total 85 Table 4 . Discriminatory functions and Canonical Correlations Similarly, the standardized canonical coefficients highlight the relative importance of each test variable in explaining the discriminant function (Huberty & Olejnik, 2007 ). The findings show that PA assessment emerges as the most influential predictor, with a coefficient of 0.560, indicating its strong explanatory power in identifying dyslexic pupils (Novita et al., 2019 ). Following closely is the RNV assessment, which examines word reading skills of both suspecting and non-suspecting dyslexic pupils. With a coefficient value of 0.512, the result show a significant predictive ability to differentiate dyslexic and non-dyslexic pupils. Conversely, the WRN assessment exhibits the least explanatory power among the cognitive linguistic assessments, with a coefficient of 0.320, see Table 4 . This hierarchy suggests that phonemic awareness plays a crucial role in distinguishing dyslexic pupils, followed by word reading skills, while word recognition contributes relatively less to the discriminant function. The confusion matrix result of the prediction demonstrates a high degree of accuracy in identifying dyslexic and non-dyslexic individuals. Out of the 85 participants who underwent the pretest exams, 70 were correctly classified as non-dyslexic, while 13 were accurately identified as dyslexic. Notably, only 2 participants were erroneously grouped as non-dyslectic, but this discrepancy was rectified through cross-validation, where the model adjusted based on the Y threshold of -4.60–0.88. Consequently, the overall accuracy of the classification reached 97.2%, indicating a robust performance of the discriminant analysis model, which closely approaches 100% (Novita et al., 2019 ), (See Table 5 ). Table 5 Confusion Matrix for Discriminant Analysis Prediction Test Standardized Canonical Discriminant coefficient Test Predictor variables within groups Canonical correlation Wilk’s df WRM Test .320 PA Test .815 0.88 3 RNV Test .512 RNV Test .730 PA Test .560 WRM Test .533 Table 5 . Confusion Matrix for Discriminant Analysis Prediction Based on the results of the discriminant analysis model—specifically the low Wilks’ Lambda (0.224), statistically significant p-value (p < 0.05), high Eigenvalue (3.466), strong canonical correlation coefficient (0.88), and high prediction accuracy (97.2%)—the study’s hypothesis is statistically supported and therefore accepted. These metrics collectively confirm that the discriminant function effectively distinguishes between dyslexic and non-dyslexic pupils, validating the predictive strength and reliability of the model. 5.0 Discussion Through the utilization of discriminant analysis, the study examines the effectiveness of the Primary Reading Inventory (PRI) model in identifying and distinguishing dyslexic pupils from non-dyslexic ones using three cognitive-linguistic indicators, namely Phonemic Awareness, Rapid Naming and Vocabulary, and Word Recognition and Meaning. The results revealed strong statistical evidence supporting the validity of the model, offering meaningful insights into how specific reading-related cognitive skills can be used for early dyslexia screening and educational intervention. These findings corroborate Novita’s (2023) results, which also demonstrated the efficiency of discriminant analysis in classifying dyslexic and non- dyslexic learners across multiple countries. The consistency between this study and Novita’s reinforces the utility of discriminant analysis as a reliable statistical approach for identifying dyslexia, particularly when cognitive-linguistic indicators are systematically integrated. The model’s high accuracy rate of 97.2% in this study strengthens the argument that discriminant analysis can effectively serve as an educational diagnostic tool in resource-limited contexts. Furthermore, the Primary Reading Inventory model proved to be an effective framework for children with dyslexia screening (Colenbrander et al., 2018 ; Ferrer et al., 2010 ). Its predictive accuracy demonstrates its practical value for early screening in schools. Integrating this approach with e-learning tools and gamified interventions could enhance its impact, allowing teachers to tailor instruction to individual learning needs and helping dyslexic pupils improve reading fluency and confidence through interactive digital platforms. This research provides a significant contribution by addressing the long-standing dilemma of dyslexia identification in Nigeria without reliance on expensive neuropsychological or medical assessments (Adubasim, 2018 ). By offering a statistically validated, classroom-based approach, it empowers educators to identify and support at-risk pupils using data-driven methods that are affordable and scalable within the Nigerian educational system. Given the promising outcomes of this study, it is recommended that similar research be replicated across other Nigerian states and African countries to validate the robustness and generalizability of the PRI-based discriminant model. Future research should explore integrating socio- cultural and linguistic variables, as well as digital diagnostic tools, to refine and expand early dyslexia screening and intervention strategies across diverse educational contexts. Conclusion This study explored the effectiveness of integrating a statistical approach with cognitive linguistic indicators, specifically utilizing the PRI model in distinguishing between dyslexic and non-dyslexic pupils using discriminant analysis. The findings from this study demonstrate the strong predictive power of the discriminant analysis model in accurately distinguishing dyslexic from non-dyslexic pupils using the Primary Reading Inventory (PRI) model. The high canonical correlation coefficient (0.88) indicates a strong linear association between the discriminant function and the independent variables, confirming the robustness of the selected predictors. Moreover, the low Wilks’ Lambda value (0.224) and a statistically significant p-value (p < 0.05) further validate the model’s effectiveness in differentiating between the two groups. Among the predictor variables, phonemic awareness (Test 3) emerged as the most influential, emphasizing its central role in dyslexia diagnosis. This aligns with existing literature that identifies phonological processing deficits as a core feature of dyslexia, often manifesting as difficulties in decoding and manipulating speech sounds (Badian, 2001 ; Treiman et al., 2019 ). Through the integration of this framework, the study provides valuable insights into how dyslexia in children can be effectively screened without relying on complex medical or neuroimaging procedures such as EEG, fMRI, or neuropsychological testing (Parmar & Paunwala, 2023 ). Instead, it offers a practical, school-based diagnostic approach that leverages cognitive-linguistic indicators to identify reading difficulties early. This approach not only simplifies the screening process but also paves the way for developing targeted, evidence-based interventions aimed at enhancing the learning outcomes of the affected pupil population. Declarations Ethical approval This study was approved by the Universal Basic Education Commission Research Committee Bauchi. All methods were performed in accordance with the relevant guidelines and regulations of the Universal Basic Education Commission Research Committee Bauchi. Consent to participate Written informed consent was obtained from the parents or legal guardians of all participating children prior to data collection. Consent to publish Not applicable. Author Contribution This study represents the initial phase of an ongoing doctoral research programme focused on the identification, design and development of a multisensory mixed-reality learning console for learners with dyslexia. The present findings provide the empirical foundation for subsequent system design and intervention studies. KA executed the study design, coordinated data collection in the participating schools, conducted all statistical analyses, and produced the initial manuscript draft. IU provided strategic academic supervision, oversaw the research progression, strengthened the conceptual framework, confirmed the validity of the analytical approach, and contributed substantively to manuscript refinement. Both authors approved the final manuscript and accept full responsibility for the integrity and accuracy of the work. Data Availability Data available upon reasonable request. References Ab Hamid, M. R., Sami, W., & Mohmad Sidek, M. H. (2017). Discriminant validity assessment: Use of Fornell and Larcker criterion versus HTMT criterion. Journal of Physics: Conference Series, 890 , 012163. https://doi.org/10.1088/1742-6596/890/1/012163 Acheampong, E., Yeboah, M., Anokye, R., Adusei, A. K., Naadutey, A., & Afful, B. F. (2019). Knowledge of basic school teachers on identification and support services for children with dyslexia. Journal of Indian Association for Child and Adolescent Mental Health, 15 (2), 86–101. https://doi.org/10.1177/0973134220190206 Adanna, A. B. (2020). Teachers’ perception of the prevalence of dyslexia among primary school pupils in Abakaliki metropolis, Ebonyi State, Nigeria: Psychological implications on academic performance. 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I., Nwauzoije, E. J., Umennuihe, C. L., Nwobi, C. A., Ogba, K. T., Chukweze, M. E., Aliche, J. C., Ogbonnaya, E. K., Okoli, D. N., Onyekachi, C. C., Abang, S., Epistle, E., Nnorodi, C., & Obi, C. V. (2023). Development and validation of a teacher awareness questionnaire about dyslexia. South African Journal of Childhood Education, 13 (1). https://doi.org/10.4102/sajce.v13i1.1228 Parmar, S., & Paunwala, C. (2023). Early detection of dyslexia based on EEG with novel predictor extraction and selection. Discover Artificial Intelligence, 3 (1), 33. https://doi.org/10.1007/s44163-023-00082-4 Snowling, M. J., Hulme, C., & Nation, K. (2020). Defining and understanding dyslexia: Past, present and future. Oxford Review of Education, 46 (4), 501–513. https://doi.org/10.1080/03054985.2020.1765756 Sohail, M. T., & Chen, S. (2022). A systematic PLS-SEM approach on assessment of indigenous knowledge in adapting to floods: A way forward to sustainable agriculture. Frontiers in Plant Science, 13 , 990785. Sturm, V. E., Roy, A. R. K., Datta, S., Wang, C., Sible, I. J., Holley, S. R., Watson, C., Palser, E. R., Morris, N. A., Battistella, G., Rah, E., Meyer, M., Pakvasa, M., Mandelli, M. L., Deleon, J., Hoeft, F., Caverzasi, E., Miller, Z. A., Shapiro, K. A., & Gorno-Tempini, M. L. (2021). Enhanced visceromotor emotional reactivity in dyslexia and its relation to salience network connectivity. Cortex, 134 , 278–295. https://doi.org/10.1016/j.cortex.2020.10.022 Thia, J. A. (2023). Guidelines for standardizing the application of discriminant analysis of principal components to genotype data. Molecular Ecology Resources, 23 (3), 523–538. https://doi.org/10.1111/1755-0998.13706 Treiman, R., Cardoso-Martins, C., Pollo, T. C., & Kessler, B. (2019). Statistical learning and spelling: Evidence from Brazilian prephonological spellers. Cognition, 182 , 1–7. https://doi.org/10.1016/j.cognition.2018.08.016 Treiman, R., Decker, K., Kessler, B., & Pollo, T. C. (2015). Variation and repetition in the spelling of young children. Journal of Experimental Child Psychology, 132 , 99–110. https://doi.org/10.1016/j.jecp.2014.12.008 Uzoekwe, H. E., & Ezeani, N. P. (2018). Helping dyslexia children succeed in primary schools. Journal of Guidance, 2 (2), 140–147. Wagner, R. K., Torgesen, J. K., Rashotte, C. A., & Pearson, N. A. (1999). Comprehensive test of phonological processing . Pro-Ed. Willows, D. M., Kruk, R., & Corcos, E. (2012). Visual processes in reading and reading disabilities . Routledge. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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09:28:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":787217,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8499892/v1/47f352bf-cd49-45aa-ab29-4e136132913a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Discriminant Analysis of Cognitive Linguistic Indicators for Early Identification of Dyslexia in Nigerian Primary School Pupils","fulltext":[{"header":"1.0 Introduction","content":"\u003cp\u003eDyslexia is a learning disability that presents a significant challenge for individuals termed as having specific reading disabilities (Agu \u0026amp; Omenyi, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Eyo \u0026amp; Nkanga, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This group encounters obstacles in excelling within a competitive society due to mental, social, or physical impairments (Ihekwoaba et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Dyslexia, defined as a reading disability, profoundly restricts an individual's ability to read accurately, swiftly, and comprehend written content (Chitsa \u0026amp; Mpofu, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Leseyane et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). To be considered substantial, this limitation extends beyond mere difficulty and must impact both academic performance and daily activities.\u003c/p\u003e \u003cp\u003eDyslexia in children is a neurodevelopmental disorder primarily affecting reading skills (Altin et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Other challenges associated with dyslexia encompass difficulties in accurate word recognition, spelling, decoding, and mathematical calculation (Treiman et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Uzoekwe Helen \u0026amp; Ezeani Nneka, 2018). These challenges are distinct from factors like intellectual disabilities, visual, or auditory impairments (Adekola \u0026amp; Olarewaju, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Altin et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Sturm et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Despite average to above-average cognitive abilities observed in many individuals with dyslexia (Kandel \u0026amp; Perret, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), they encounter significant difficulties in segmenting words into distinct sounds and recognizing words, leading to reading struggles (Agu \u0026amp; Omenyi, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGlobally, dyslexia is recognized as a prevalent learning disability, impacting 15\u0026ndash;20% of the population (Ebere, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2016\u003c/span\u003e); International Dyslexia Association (IDA), 2019; Skiada, Soroniati, Gardeli, \u0026amp; Zissis, 2014). This developmental issue affects schoolchildren irrespective of gender or age, underscoring its significance in the educational landscape (Skiada et al., 2014). The high prevalence of dyslexia, affecting as many as one in five children, highlights the urgent need for comprehensive research and understanding in this field. With 80 to 90 percent of children with learning disorders experiencing dyslexia, the impact of this neurodevelopmental condition on the educational landscape is substantial.\u003c/p\u003e \u003cp\u003eHowever, despite dyslexia being acknowledged as a primary learning disorder among children, it has received very few scholarly attentions in developing nations, including Nigeria (Makgato et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Consequently, this led to notable scarcity of readily available statistics and widespread awareness regarding dyslexia in Nigeria. This knowledge gap hinders the implementation of effective interventions and support systems for children grappling with dyslexia. The lack of awareness about dyslexia within Nigeria, as highlighted by the Dyslexia Foundation of Nigeria (Okechukwu et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), has profound implications. This knowledge gap contributes to a harsh reality for children contending with dyslexia, subjecting them to physical punishment, derogatory name-calling, bullying, and shaming in the class (Ngwu \u0026amp; Nuhu, 2022). A report from The Guardian in 2018 sheds light on the distressing reality that these young individuals encounter, pushing them to develop a sense of inferiority due to the lack of understanding and empathy surrounding dyslexia within Nigerian society.\u003c/p\u003e \u003cp\u003eIn Nigeria, despite the global advancements in e-learning, substantial challenges hinder its efficacy, particularly for individuals grappling with dyslexia (Haroun et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The traditional e-learning programs often necessitate a certain level of computer proficiency and familiarity with graphical user interfaces (GUI) like WINDOWS or IOS, thereby creating a digital divide for those lacking such skills. Consequently, the existing challenges in e-learning technology in Nigeria underscore the imperative for a more inclusive approach that explicitly addresses the unique learning needs of individuals with dyslexia.\u003c/p\u003e \u003cp\u003eAdditionally, children with dyslexia often face persistent challenges in learning, particularly in reading, spelling, and writing, which significantly affect their academic progress and self-esteem (Cheng-Lai et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Chitsa \u0026amp; Mpofu, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). In many Nigerian schools, these difficulties are frequently misunderstood as signs of laziness or lack of intelligence rather than as indicators of a specific learning difference (Fletcher et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Gus \u0026amp; Samuelsson, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). Teachers often struggle to identify dyslexic pupils due to limited training, inadequate diagnostic tools, and the absence of structured screening frameworks within the educational system (Chitsa \u0026amp; Mpofu, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Okechukwu et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Consequently, many children with dyslexia remain undiagnosed and fail to receive the targeted instructional support they need to succeed (Cassidy et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Therefore, the first and most critical step in supporting pupils with dyslexia is proper identification (Germano et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Early and accurate identification enables the implementation of targeted interventions that address specific learning deficits rather than relying on generalized teaching methods that may not meet individual needs (Javed et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Recognizing dyslexic pupils allows teachers to adopt differentiated instructional strategies, such as multisensory learning techniques and phonics-based reading interventions, which have been shown to improve literacy outcomes (Adubasim, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Daniel et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Malekian \u0026amp; Askari, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). It also empowers teachers to be more patient and responsive to the unique challenges faced by such pupils, fostering an inclusive classroom environment. Moreover, early identification provides parents with valuable insight into their child\u0026rsquo;s learning difficulties, helping them to offer emotional support, collaborate effectively with teachers, and seek specialized assistance when necessary (Ngwu \u0026amp; Nuhu, 2022).\u003c/p\u003e \u003cp\u003eIdentifying dyslexic children has been enhanced through the use of various techniques, each providing distinct insights into the cognitive and linguistic characteristics linked to dyslexia (Colenbrander et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Daniel et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). However, there remains considerable variability in the approaches employed for detection across studies and contexts. For instance, (Parmar \u0026amp; Paunwala (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) utilized EEG recordings to identify neural signatures of dyslexia at an early age, demonstrating the potential of neurophysiological methods in detecting subtle brain activity differences linked to reading and language processing. In contrast, Acheampong et al. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) adopted descriptive statistical methods to analyze behavioral and performance- based indicators of dyslexia, highlighting the value of quantitative assessment tools in classroom-based evaluations. Similarly, (Novita et al. (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) applied Multivariate Analysis of Variance (MANOVA) and Discriminant Analysis to identify and compare dyslexic children across three countries, finding these statistical techniques effective in distinguishing dyslexic from non-dyslexic learners based on cognitive- linguistic variables. Building on this approach, the present study employs discriminant analysis to predict and identify dyslexic pupils among primary school children in Bauchi city. This method provides a systematic, data-driven means of classifying pupils based on measurable test performance, thereby offering a reliable framework for early detection and targeted educational intervention.\u003c/p\u003e"},{"header":"2.0 Theoretical Framework","content":"\u003cp\u003eDyslexia is widely understood to originate from two principal domains: language processing deficits (cognitive\u0026ndash;linguistic disorder) and visual processing deficits (cognitive\u0026ndash;visual disorder) (Adanna, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Loftin, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). These deficiencies are conceptualized as cognitive disorders that affect the acquisition and processing of written language rather than a reflection of intellectual impairment (Adlof \u0026amp; Hogan, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Hamidi \u0026amp; Beygmohammadloo, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eEarly researchers investigating dyslexia explored multiple approaches in an effort to understand its origin and establish reliable identification models (Berninger et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Denckla \u0026amp; Rudel, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1976\u003c/span\u003e). Initial studies were largely exploratory, relying on surveys, observational data, and hypotheses aimed at determining whether visual deficits were the primary source of dyslexic symptoms (Willows et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). This line of inquiry reflected the early belief that difficulties in visual perception or eye movement were responsible for reading impairments (Daniel et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAs research evolved, attention shifted toward cognitive explanations, particularly the relationship between intelligence and reading ability. The IQ\u0026ndash;reading discrepancy model subsequently emerged as a dominant framework throughout much of the twentieth century (Daniel et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Ferrer et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). This model posited that dyslexia could be identified when a learner\u0026rsquo;s reading achievement was significantly lower than what would be expected based on their measured intelligence quotient (IQ) (Shipley and Jones, 1969; Mason, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e1967\u003c/span\u003e). However, later empirical studies questioned the validity of this approach. Scholars such as Iovino et al. (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e1998\u003c/span\u003e) and Fletcher et al. (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e1998\u003c/span\u003e) argued that the discrepancy model lacked reliability and consistency in accurately identifying dyslexic individuals. They emphasized that reading difficulties occur across the full spectrum of intellectual ability and that reliance on IQ discrepancy often excluded struggling readers who required intervention but did not meet the \u0026ldquo;discrepancy\u0026rdquo; threshold.\u003c/p\u003e \u003cp\u003eSimilarly, Benson et al. (2020) reported that school psychologists in the United States continue to employ a range of frameworks for identifying dyslexia, some of which remain outdated, such as the intelligence\u0026ndash; achievement discrepancy model. This approach assumes that dyslexia can only be diagnosed when there is a marked gap between a pupil\u0026rsquo;s intellectual ability and academic performance, and when all other possible causes of reading difficulties have been ruled out (Ebere, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Likewise, Cheng-Lai et al. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) examined the relationship between writing-to-dictation, handwriting, orthographic, and perceptual\u0026ndash;motor skills among Chinese children with dyslexia. Dyslexic pupils were identified through dictation and writing tasks, and the results of stepwise multiple regression analyses revealed that Chinese character naming was the only significant predictor of word dictation performance.\u003c/p\u003e \u003cp\u003eIn light of these limitations, contemporary research has shifted toward multifactorial and processing-based models that emphasize phonological awareness, rapid naming, working memory, and other cognitive\u0026ndash; linguistic variables as more valid indicators of dyslexia than IQ-based comparisons (Badian, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Treiman et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Wagner et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). In response to the inadequacies of traditional models, frameworks such as the Response to Intervention (RTI) and the Primary Reading Inventory (PRI) have emerged as more comprehensive and effective approaches (Novita et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The RTI model identifies reading difficulties through continuous progress monitoring, emphasizing early intervention and instructional responsiveness rather than static ability measures. Similarly, the PRI model offers a structured and diagnostic framework that directly assesses the core components of reading \u0026mdash; Word Recognition and Meaning, Rapid Naming and Vocabulary, and Phonemic Awareness \u0026mdash; allowing for more accurate differentiation between dyslexic and non-dyslexic pupils. Together, these models address the weaknesses of earlier methods by focusing on measurable cognitive and linguistic processes that underpin reading development and disability.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Research Question\u003c/h2\u003e \u003cp\u003eHow effectively does the Primary Reading Inventory (PRI) model distinguish between dyslexic and non- dyslexic pupils based on cognitive-linguistic indicators?\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Research Hypothesis\u003c/h2\u003e \u003cp\u003e \u003cstrong\u003eH₁\u003c/strong\u003e \u003cp\u003eThe Primary Reading Inventory model, based on Word Recognition and Meaning, Rapid Naming and Vocabulary, and Phonemic Awareness, can effectively differentiate between dyslexic and non-dyslexic pupils.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3.0 Methods","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Methodological Approach\u003c/h2\u003e \u003cp\u003eThis chapter explains the research framework, providing a comprehensive roadmap for executing the research objectives (Bhatta et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). It conceptualizes and outlines a step-by-step procedure meticulously designed to attain the desired goal of identifying pupils with dyslexia in some selected primary schools in Bauchi (Ahuja, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Gakuu et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Utilizing a statistical approach, specifically using discriminant analysis, the study accurately predict and identified children with dyslexia in some selected primary schools in Bauchi. This was achieved through a robust screening process utilizing established models for accurate and efficient assessment (Clark et al., 2019; Rauschenberger et al., 2020). Similarly, the study utilized the Primary Reading Inventory (PRI) model, which deploys 3- to 5 screening stages to identify children at risk of reading problems, including dyslexia, within the primary school groups (Fletcher et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe PRI model provides a structured approach to examining pupils\u0026rsquo; reading abilities across three (3) key domains that influence literacy development (Adekola \u0026amp; Olarewaju, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Geertsema et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eUnderstanding how these domains interact to shape reading performance is critical for the early detection and remediation of dyslexia (Snowling et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The three core components of the PRI model\u0026mdash;Word Recognition and Meaning, Rapid Naming and Vocabulary, and Phonemic Awareness\u0026mdash;are examined to determine their discriminant power in accurately classifying pupils as either dyslexic or non-dyslexic (Novita et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAdditionally, PRI provides a more in-depth 30-minute inventory to determine specific reading concepts that need to be addressed, ensuring a comprehensive understanding of individual needs. Building on this foundation, the study integrates insights from the National Institutes of Child Health and Human Development (NICHD)-funded early assessment of reading skills (EARS) project (Badian, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). This longitudinal assessment of reading precursor skills in children enhances the screening stages, providing a more nuanced evaluation (Fletcher et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The screening process, guided by these validated models, aims to identify dyslexia with precision and efficiency, facilitating a targeted and informed approach in subsequent research stages.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Population and Sampling\u003c/h2\u003e \u003cp\u003eThe population size of 85 participants was derived from two primary schools, selected based on recommendations from teachers familiar with the students' learning behaviors and potential dyslexia indicators. These are Mai Kalam Inclusive Academy and Sunshine International School, both located in metropolitan Bauchi. The sample size was deemed adequate for the study's objectives, ensuring a diverse representation of pupils from different backgrounds and learning environments (Denckla \u0026amp; Rudel, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1976\u003c/span\u003e; Novita et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). By incorporating input from teachers who interact closely with the students daily, the selection process aimed to capture a comprehensive range of potential dyslexic traits and learning challenges (Chitsa \u0026amp; Mpofu, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Fletcher et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Furthermore, the inclusion of participants from two schools enhances the generalizability of the findings, offering insights into dyslexia prevalence and identification practices across different educational settings.\u003c/p\u003e \u003cp\u003eThe participant selection for this study targeted children aged 4 to 10 years, covering primary levels 1 to 6, with an inclusive approach toward both genders (Huang et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Makgato et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Purposive sampling was chosen to ensure that the participants selected met specific criteria relevant to the study's objectives, such as age range, school enrollment, and absence of sensory, motor, or cognitive impairments (Ahuja, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). This approach allowed for the recruitment of a targeted sample population that could provide valuable insights into the research questions and intervention outcomes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Inclusion and Exclusion Criteria:\u003c/h2\u003e \u003cp\u003e The inclusion criteria were stringent, requiring participants to have parents or guardians who provided signed Informed Consent forms. Moreover, participants were required not to exhibit sensory, motor, or cognitive impairments (Kandel \u0026amp; Perret, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Similarly, the exclusion criteria were also applied to children with sensory, motor, or cognitive impairments, as well as those whose parents or guardians did not sign the Informed Consent form. This careful selection aimed to create a homogeneous group, ensuring that the study focused on children within the specified age and class range who did not have pre-existing conditions that could impact the results (Fletcher et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Huang et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Dyslexia Screening Sessions\u003c/h2\u003e \u003cp\u003eThe experiment took place in the computer room of the Universal Basic Education Commission Zonal office in Bauchi, Bauchi State, Nigeria. To maintain a controlled environment, the experiment was conducted during the summer break when participants were on holidays, preventing interactions among them during treatment sessions. Additionally, the experiment was carried out individually for each participant, ensuring that the impact of the developed e-learning multi-sensory intervention on learning outcomes could be accurately assessed for each child. To identify children with dyslexia, the study employs five distinct screening stages utilizing various techniques to detect children at risk of dyslexia among two primary schools in Bauchi.\u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e3.4.1 Rapid Naming and Vocabulary Assessment:\u003c/h2\u003e \u003cp\u003eDuring this session, the study adopted the rapid automized naming test by Denckla \u0026amp; Rudel, (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1976\u003c/span\u003e). Participants were presented with a test consisting of commonly used lowercase letters, such as \"a,\" \"d,\" \"o,\" \"s,\" and \"p,\" repeated in random sequences on a large paper. Their task was to name each letter as quickly as possible within a 60-second time frame. The number of correct responses named within this time limit was then converted to a rate of correct responses per second. This test evaluates the speed and accuracy of letter naming, which is often impaired in individuals with dyslexia and serves as a key indicator of reading difficulties. Additionally, participants were asked to take the Peabody Picture Vocabulary Test-Revised (Dunn \u0026amp; Dunn, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e1981\u003c/span\u003e), a norm-referenced measure of receptive vocabulary. During the test, a stimulus word was presented orally to the child, who then viewed four corresponding pictures. The child's task was to select the picture that best represented the meaning of the stimulus word, providing an indication of their receptive vocabulary skills.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e3.4.2 Phonemic Awareness Assessment:\u003c/h2\u003e \u003cp\u003eDuring this session, the Wagner's et al., (1999) Comprehensive test of Phonological process (CTPP) was adopted using five measures of phonological awareness. Participants were provided with a set of twenty- six letters of the alphabet. The test required learners to match each letter with its corresponding phonemic sound. The total score for the test, outlined in Appendix A, is 50 marks, with learners achieving a complete score by correctly identifying and matching all alphabetic letters with their respective phonemic sounds. This evaluation aims to assess the participants' ability to recognize and manipulate phonemes, which is crucial for proficient reading and language development.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e3.4.3 Word Recognition and Meaning Assessment:\u003c/h2\u003e \u003cp\u003eDuring this session, participants were presented with 15 words individually on cards. Each participant was instructed to identify and read the words aloud one at a time. This evaluation aimed to measure the\u003c/p\u003e \u003cp\u003eparticipants' proficiency in reading words, providing insight into their reading fluency and accuracy (Adekola \u0026amp; Olarewaju, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"4.0 Results","content":"\u003cp\u003eDiscriminant analysis is a multivariate statistical technique used to identify variables that best distinguish between two or more groups (Huberty, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e1974\u003c/span\u003e). In the context of this study, it is particularly suitable for differentiating between dyslexic and non-dyslexic pupils by examining how cognitive and linguistic indicators such as phonemic awareness, rapid naming, and word recognition contribute to group separation (Novita et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). This allows for the identification of the most significant predictors that classify pupils accurately based on their reading-related abilities.\u003c/p\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Test for Interaction Effects\u003c/h2\u003e \u003cp\u003eUndertaking discriminant analysis requires certain validity and reliability tests to ensure that the discriminant function and its predictors accurately distinguish between the groups being studied (Novita et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Sohail \u0026amp; Chen, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Validity testing confirms that the chosen variables truly represent the constructs under investigation, while reliability testing ensures that the measurements are consistent and dependable across samples. For the purpose of assessing the validity and reliability of the instrument, certain statistical procedures were employed (Sohail \u0026amp; Chen, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This procedure is implemented to ensure that the model\u0026rsquo;s construct appropriately captures the underlying measurement and predictor variables for discriminant analysis (Akbari et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Specifically, Wilks\u0026rsquo; Lambda and the eigenvalue of the discriminant function were used to evaluate the model\u0026rsquo;s capacity to accurately distinguish between groups (Ab Hamid et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Thia, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) (See Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The Wilks\u0026rsquo; Lambda is a multivariate test statistic used in discriminant analysis to evaluate the extent to which the predictor variables collectively distinguish between predefined groups, in this case between dyslexic and non-dyslexic pupils.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eValidity and Reliability Test\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eEigenvalues\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eWilk\u0026rsquo;s Metrics\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEigenvalue\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eCanonical correlation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eWilk\u0026rsquo;s Lambda\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eSig.\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3.466\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.881\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.224\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. \u003cem\u003eValidity and Reliability Test\u003c/em\u003e\u003c/p\u003e \u003cp\u003eIt represents the proportion of total variance in the discriminant function that is not explained by group differences. Values range between 0 and 1, with values closer to zero indicating strong discriminatory power and substantial separation between groups, while values approaching one suggest weak discrimination (Akbari et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Thia, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The Wilk\u0026rsquo;s Lambda of 0.224 indicates strong discriminatory power between dyslexic and non-dyslexic groups (Novita et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Similarly, with a \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.000, the findings reveal a statistically significant Wilks\u0026rsquo; Lambda which indicates that the set of predictors meaningfully differentiates the groups (See Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe assessment results for each test performance revealing initial dyslexic risk across the three measures, namely PA, RNV, and WRM, in identifying suspected dyslexic pupils (see Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The Phonemic Awareness (PA) test demonstrated the strongest predictive indication, identifying 39 pupils as suspected dyslexic and 46 pupils as non-suspecting. The Rapid Naming and Vocabulary (RNV) test followed, identifying 32 pupils as suspected dyslexic and 53 pupils as non-suspecting. In contrast, the Word Recognition and Meaning (WRM) test identified only 3 pupils as suspected dyslexic, while 82 pupils were classified as non-suspecting.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. \u003cem\u003eDyslexia Risk Classification Based on Individual Cognitive Test Performance\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cimg 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\" width=\"724\" height=\"217\"\u003e\u003c/p\u003e\u003cp\u003eThe assessment results for individual test reveal varying levels of prediction across the three tests namely WRM, RNV and PA in identifying suspected dyslexic pupils (See Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The WRM test identified only 3 pupils as suspected dyslexic and 82 pupils as non-suspecting based on the pupils\u0026rsquo; individual scores. For\u003c/p\u003e \u003cp\u003ethe RNV test, 32 pupils were identified as suspected dyslexic, whereas 53 pupils were categorized as non- suspecting. In contrast, the PA test identified 39 suspected dyslexic pupils, with 46 pupils classified as non- suspecting. These results indicate that the PA test produced the highest number of suspected dyslexic classifications, followed by the WRM test and then the RNV test, prior to considering the cumulative discriminant outcomes.\u003c/p\u003e \u003cp\u003e\u003cimg 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\" width=\"689\" height=\"257\"\u003e\u003c/p\u003e \u003cp\u003eThe test scores of the three different cognitive linguistic abilities demonstrate clear variation in pupils\u0026rsquo; performance, with evident reading-related learning challenges (See Table\u0026nbsp;3). The PA test recorded the lowest success rate among pupils suspected of dyslexia (average score\u0026thinsp;=\u0026thinsp;3/50), indicating significant difficulty in sound manipulation and phonological awareness tasks (Treiman et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). This reinforces established evidence that phonological processing deficits represent a core feature of dyslexia and often manifest as early challenges in distinguishing, segmenting, and blending speech sounds (Badian, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Wagner et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). Similarly, performance on the RNV test supports the PA findings in identifying high- risk dyslexic pupils (average score\u0026thinsp;=\u0026thinsp;5/50), revealing that children with dyslexia struggle with tasks requiring rapid lexical retrieval and symbol-sound association (Germano et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Reduced efficiency in rapid naming has been consistently linked to difficulties in reading fluency, and the lower performance observed in this domain further supports its value as a sensitive indicator of dyslexic tendencies (Adekola \u0026amp; Olarewaju, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Ebere, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). In contrast, pupils performed comparatively better on the WRM test. The higher pass rate among suspected dyslexic pupils in this domain may suggest that word-recognition challenges emerge more gradually or may be compensated for through memorization or repeated exposure to familiar vocabulary in classroom settings. This trend highlights that, although word recognition difficulties are part of the dyslexia profile, they may not be as prominent in early screening as deficits in\u003c/p\u003e \u003cp\u003ephonemic awareness and rapid naming.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e4\u003c/span\u003e. presents significant findings for the study. The high canonical correlation coefficient (0.88) indicates a strong linear relationship between the discriminant function and the predictor variables, confirming that the selected indicators collectively explain a substantial portion of the variance between the two groups (Novita et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDiscriminatory functions and Canonical Correlations\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAve. Scores\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNumber\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePA Test\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDyslexic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRNV Test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWRM test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePA test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-dyslexic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRNV Test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e72\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWRM test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e85\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e4\u003c/span\u003e. \u003cem\u003eDiscriminatory functions and Canonical Correlations\u003c/em\u003e\u003c/p\u003e \u003cp\u003eSimilarly, the standardized canonical coefficients highlight the relative importance of each test variable in explaining the discriminant function (Huberty \u0026amp; Olejnik, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). The findings show that PA assessment emerges as the most influential predictor, with a coefficient of 0.560, indicating its strong explanatory power in identifying dyslexic pupils (Novita et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Following closely is the RNV assessment, which examines word reading skills of both suspecting and non-suspecting dyslexic pupils. With a coefficient value of 0.512, the result show a significant predictive ability to differentiate dyslexic and non-dyslexic pupils. Conversely, the WRN assessment exhibits the least explanatory power among the cognitive linguistic assessments, with a coefficient of 0.320, see Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e4\u003c/span\u003e. This hierarchy suggests that phonemic awareness plays a crucial role in distinguishing dyslexic pupils, followed by word reading skills, while word recognition contributes relatively less to the discriminant function.\u003c/p\u003e \u003cp\u003eThe confusion matrix result of the prediction demonstrates a high degree of accuracy in identifying dyslexic and non-dyslexic individuals. Out of the 85 participants who underwent the pretest exams, 70 were correctly classified as non-dyslexic, while 13 were accurately identified as dyslexic. Notably, only 2 participants were erroneously grouped as non-dyslectic, but this discrepancy was rectified through cross-validation, where the model adjusted based on the Y threshold of -4.60\u0026ndash;0.88. Consequently, the overall accuracy of the classification reached 97.2%, indicating a robust performance of the discriminant analysis model, which\u003c/p\u003e \u003cp\u003eclosely approaches 100% (Novita et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), (See Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eConfusion Matrix for Discriminant Analysis Prediction\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTest\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStandardized Canonical Discriminant coefficient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTest\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePredictor variables within groups\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCanonical correlation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eWilk\u0026rsquo;s df\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWRM Test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.320\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePA Test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.815\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRNV Test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.512\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRNV Test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.730\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePA Test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.560\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWRM Test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.533\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e5\u003c/span\u003e. \u003cem\u003eConfusion Matrix for Discriminant Analysis Prediction\u003c/em\u003e\u003c/p\u003e \u003cp\u003eBased on the results of the discriminant analysis model\u0026mdash;specifically the low Wilks\u0026rsquo; Lambda (0.224), statistically significant p-value (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), high Eigenvalue (3.466), strong canonical correlation coefficient (0.88), and high prediction accuracy (97.2%)\u0026mdash;the study\u0026rsquo;s hypothesis is statistically supported and therefore accepted. These metrics collectively confirm that the discriminant function effectively distinguishes between dyslexic and non-dyslexic pupils, validating the predictive strength and reliability of the model.\u003c/p\u003e \u003c/div\u003e"},{"header":"5.0 Discussion","content":"\u003cp\u003eThrough the utilization of discriminant analysis, the study examines the effectiveness of the Primary Reading Inventory (PRI) model in identifying and distinguishing dyslexic pupils from non-dyslexic ones using three cognitive-linguistic indicators, namely Phonemic Awareness, Rapid Naming and Vocabulary, and Word Recognition and Meaning. The results revealed strong statistical evidence supporting the validity of the model, offering meaningful insights into how specific reading-related cognitive skills can be used for early dyslexia screening and educational intervention. These findings corroborate Novita\u0026rsquo;s (2023) results, which also demonstrated the efficiency of discriminant analysis in classifying dyslexic and non- dyslexic learners across multiple countries. The consistency between this study and Novita\u0026rsquo;s reinforces the utility of discriminant analysis as a reliable statistical approach for identifying dyslexia, particularly when cognitive-linguistic indicators are systematically integrated. The model\u0026rsquo;s high accuracy rate of 97.2% in this study strengthens the argument that discriminant analysis can effectively serve as an educational diagnostic tool in resource-limited contexts. Furthermore, the Primary Reading Inventory model proved to be an effective framework for children with dyslexia screening (Colenbrander et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Ferrer et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Its predictive accuracy demonstrates its practical value for early screening in schools. Integrating this approach with e-learning tools and gamified interventions could enhance its impact, allowing teachers to tailor instruction to individual learning needs and helping dyslexic pupils improve reading fluency and confidence through interactive digital platforms.\u003c/p\u003e \u003cp\u003eThis research provides a significant contribution by addressing the long-standing dilemma of dyslexia identification in Nigeria without reliance on expensive neuropsychological or medical assessments (Adubasim, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). By offering a statistically validated, classroom-based approach, it empowers educators to identify and support at-risk pupils using data-driven methods that are affordable and scalable within the Nigerian educational system. Given the promising outcomes of this study, it is recommended that similar research be replicated across other Nigerian states and African countries to validate the robustness and generalizability of the PRI-based discriminant model. Future research should explore integrating socio-\u003c/p\u003e \u003cp\u003ecultural and linguistic variables, as well as digital diagnostic tools, to refine and expand early dyslexia screening and intervention strategies across diverse educational contexts.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study explored the effectiveness of integrating a statistical approach with cognitive linguistic indicators, specifically utilizing the PRI model in distinguishing between dyslexic and non-dyslexic pupils using discriminant analysis. The findings from this study demonstrate the strong predictive power of the discriminant analysis model in accurately distinguishing dyslexic from non-dyslexic pupils using the Primary Reading Inventory (PRI) model. The high canonical correlation coefficient (0.88) indicates a strong linear association between the discriminant function and the independent variables, confirming the robustness of the selected predictors. Moreover, the low Wilks\u0026rsquo; Lambda value (0.224) and a statistically significant p-value (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) further validate the model\u0026rsquo;s effectiveness in differentiating between the two groups. Among the predictor variables, phonemic awareness (Test 3) emerged as the most influential, emphasizing its central role in dyslexia diagnosis. This aligns with existing literature that identifies phonological processing deficits as a core feature of dyslexia, often manifesting as difficulties in decoding and manipulating speech sounds (Badian, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Treiman et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Through the integration of this framework, the study provides valuable insights into how dyslexia in children can be effectively screened without relying on complex medical or neuroimaging procedures such as EEG, fMRI, or neuropsychological testing (Parmar \u0026amp; Paunwala, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Instead, it offers a practical, school-based diagnostic approach that leverages cognitive-linguistic indicators to identify reading difficulties early. This approach not only simplifies the screening process but also paves the way for developing targeted, evidence-based interventions aimed at enhancing the learning outcomes of the affected pupil population.\u003c/p\u003e"},{"header":"Declarations","content":" \u003cp\u003e \u003cstrong\u003eEthical approval\u003c/strong\u003e \u003cp\u003eThis study was approved by the Universal Basic Education Commission Research Committee Bauchi. All methods were performed in accordance with the relevant guidelines and regulations of the Universal Basic Education Commission Research Committee Bauchi.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent to participate\u003c/strong\u003e \u003cp\u003e Written informed consent was obtained from the parents or legal guardians of all participating children prior to data collection.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent to publish\u003c/strong\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eThis study represents the initial phase of an ongoing doctoral research programme focused on the identification, design and development of a multisensory mixed-reality learning console for learners with dyslexia. The present findings provide the empirical foundation for subsequent system design and intervention studies. KA executed the study design, coordinated data collection in the participating schools, conducted all statistical analyses, and produced the initial manuscript draft. IU provided strategic academic supervision, oversaw the research progression, strengthened the conceptual framework, confirmed the validity of the analytical approach, and contributed substantively to manuscript refinement. Both authors approved the final manuscript and accept full responsibility for the integrity and accuracy of the work.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData available upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003cp\u003eAb Hamid, M. R., Sami, W., \u0026amp; Mohmad Sidek, M. H. (2017). 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Routledge.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-8499892/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8499892/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThere is a reasonable number of pupils suffering from dyslexia in Nigeria; however, very little has been done to identify and provide targeted support for them. The absence of reliable, context-specific diagnostic tools has made early detection challenging, leaving many children to struggle with persistent reading and learning difficulties. This study applied the Primary Reading Inventory (PRI) model to predict and identify dyslexic pupils using three cognitive-linguistic indicators: Phonemic Awareness (PA), Rapid Naming and Vocabulary (RNV), and Word Recognition and Meaning (WRM). Discriminant analysis was employed to determine the capacity of these indicators to differentiate between dyslexic and non-dyslexic pupils. The results revealed a strong canonical correlation coefficient (0.88) and a low Wilks\u0026rsquo; Lambda value (0.224), indicating a high level of discrimination between the groups. The analysis further showed that Phonemic Awareness was the most influential predictor, followed by RNV and WRM, with the overall model achieving a classification accuracy of 97.2%. These findings demonstrate the reliability of the PRI model as an effective and practical approach for identifying dyslexia within school contexts. The study underscores the importance of integrating statistical approaches with established cognitive-linguistic models to identify dyslexic children across schools in Nigeria and other African cities, thereby promoting inclusive education, facilitating targeted interventions, and fostering equitable learning outcomes for pupils with reading difficulties.\u003c/p\u003e","manuscriptTitle":"Discriminant Analysis of Cognitive Linguistic Indicators for Early Identification of Dyslexia in Nigerian Primary School Pupils","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-20 09:00:30","doi":"10.21203/rs.3.rs-8499892/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4d3b0637-a71c-486c-90bf-ffbf830d27c2","owner":[],"postedDate":"January 20th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-08T08:44:14+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-20 09:00:30","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8499892","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8499892","identity":"rs-8499892","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}