research articles on learning disabilities

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Understanding, Educating, and Supporting Children with Specific Learning Disabilities: 50 Years of Science and Practice

Elena l. grigorenko.

1 University of Houston, Houston, USA

2 Baylor College of Medicine, Houston, USA

Donald Compton

3 Florida State University, Tallahassee, USA

4 Vanderbilt University, Nashville, USA

Richard Wagner

Erik willcutt.

5 University of Colorado Boulder, Boulder, USA

Jack M. Fletcher

Specific learning disabilities (SLD) are highly relevant to the science and practice of psychology, both historically and currently, exemplifying the integration of interdisciplinary approaches to human conditions. They can be manifested as primary conditions—as difficulties in acquiring specific academic skills—or as secondary conditions, comorbid to other developmental disorders such as Attention Deficit Hyperactivity Disorder. In this synthesis of historical and contemporary trends in research and practice, we mark the 50th anniversary of the recognition of SLD as a disability in the US. Specifically, we address the manifestations, occurrence, identification, comorbidity, etiology, and treatment of SLD, emphasizing the integration of information from the interdisciplinary fields of psychology, education, psychiatry, genetics, and cognitive neuroscience. SLD, exemplified here by Specific Word Reading, Reading Comprehension, Mathematics, and Written Expression Disabilities, represent spectrum disorders each occurring in approximately 5–15% of the school-aged population. In addition to risk for academic deficiencies and related functional social, emotional, and behavioral difficulties, those with SLD often have poorer long-term social and vocational outcomes. Given the high rate of occurrence of SLD and their lifelong negative impact on functioning if not treated, it is important to establish and maintain effective prevention, surveillance, and treatment systems involving professionals from various disciplines trained to minimize the risk and maximize the protective factors for SLD.

Fifty years ago, the US federal government, following an advisory committee recommendation ( United States Office of Education, 1968 ), first recognized specific learning disabilities (SLD) as a potentially disabling condition that interferes with adaptation at school and in society. Over these 50 years, a significant research base has emerged on the identification and treatment of SLD, with greater understanding of the cognitive, neurobiological, and environmental causes of these disorders. The original 1968 definition of SLD remains statutory through different reauthorizations of the 1975 special education legislation that provided free and appropriate public education for all children with disabilities, now referred to as the Individuals with Disabilities Education Act (IDEA, 2004). SLD are recognized worldwide as a heterogeneous set of academic skill disorders represented in all major diagnostic nomenclatures, including the Diagnostic and Statistical Manual-5 (DSM-5, American Psychiatric Association, 2013) and the International Statistical Classification of Diseases and Related Health Problems (ICD-11, World Health Organization, 2018).

In the US, the SLD category is the largest for individuals who receive federally legislated support through special education. Children are identified as SLD through IDEA when a child does not meet state-approved age- or grade-level standards in one or more of the following areas: oral expression, listening comprehension, written expression, basic reading skills, reading fluency, reading comprehension, mathematics calculation, and mathematics problem solving. Although children with SLD historically represented about 50% of the children aged 3–21 served under IDEA, percentages have fluctuated across reauthorizations of the special education law, with some decline over the past 10 years ( Figure 1 ).

The Individuals with Disabilities Education Act (IDEA), enacted in 1975 as Public Law 94–142, mandates that children and youth ages 3–21 with disabilities be provided a free and appropriate public school education in the least restricted environment. The percentage of children served by federally mandated special education programs, out of total public school enrollment, increased from 8.3 percent to 13.8 percent between 1976–77 and 2004–05. Much of this overall increase can be attributed to a rise in the percentage of students identified as having SLD from 1976–77 (1.8 percent) to 2004–05 (5.7 percent). The overall percentage of students being served in programs for those with disabilities decreased between 2004–05 (13.8 percent) and 2013–14 (12.9 percent). However, there were different patterns of change in the percentages served with some specific conditions between 2004–05 and 2013–14. The percentage of children identified with SLD declined from 5.7 percent to 4.5 percent of the total public school enrollment during this period. This number is highly variable by state: for example, in 2011 it ranged from 2.3% in Kentucky to 13.8% in Puerto Rico, as there is much variability in the procedures used to identify SLD, and disproportional demographic representation. Figure by Janet Croog.

This review is a consensus statement developed by researchers currently leading the National Institute of Child Health and Human Development (NICHD) supported Consortia of Learning Disabilities Research Centers and Innovation Hubs. This consensus is based on the primary studies we cite, as well as the meta-analytic reviews (*), systematic reviews (**), and first-authored books (***) that provide an overview of the science underlying research and practice in SLD (see references). The hope is that this succinct overview of the current state of knowledge on SLD will help guide an agenda of future research by identifying knowledge gaps, especially as the NICHD embarks on a new strategic plan. The research programs on SLD from which this review is derived represent the integration of diverse, interdisciplinary approaches to behavioral science and human conditions. We start with a brief description of the historical roots of the current view of SLD, then provide definitions as well as prevalence and incidence rates, discuss comorbidity between SLD themselves and SLD and other developmental disorders, comment on methods for SLD identification, present current knowledge on the etiology of SLD, and conclude with evidence-based principles for SLD intervention.

Three Historical Strands of Inquiry that Shaped the Current Field of SLD

Three strands of phenomenological inquiry culminated in the 1968 definition and have continued to shape current terminology and conventions in the field of SLD ( Figure 2 ). The first, a medical strand, originated in 1676, when Johannes Schmidt described an adult who had lost his ability to read (but with preserved ability to write and spell) because of a stroke. Interest in this strand reemerged in the 1870s with the publication of a string of adult cases who had lived through a stroke or traumatic brain injury. Subsequent cases involved children who were unable to learn to read despite success in mathematics and an absence of brain injury, which was termed “word blindness” ( W. P. Morgan, 1896 ). These case studies laid the foundation for targeted investigations into the presentation of specific unexpected difficulties related to reading printed words despite typical intelligence, motivation, and opportunity to learn.

A schematic timeline of the three stands of science and practice in the field of SLD. The colors represent the strands (blue—first, yellow—second, and green—third). Blue: provided phenomenological descriptions and generated hypotheses about the gene-brain bases of SLD (specifically, dyslexia or SRD); it also provided the first evidence that the most effective treatment approaches are skill-based and reflect cognitive models of the conditions. Yellow: differentiated SLD from other comorbid conditions. Green: stressed the importance of focusing on SLD in academic settings and developing both preventive and remediational evidence-based approaches to managing these conditions. Due to space constraints, the names of many highly influential scientists (e.g., Marilyn Adams, Joseph Torgesen, Isabelle Liberman, Keith Stanovich, among others) who shaped the field of SLD have been omitted. Figure by Janet Croog.

The second strand is directly related to the formalization of the American Psychiatric Association’s Diagnostic and Statistical Manual (DSM). Rooted in the work of biologically oriented physicians, the 1952 first edition (DSM-I) referenced a category of chronic brain syndromes of unknown cause that focused largely on behavioral presentations we now recognize as hyperkinesis and Attention Deficit Hyperactivity Disorder (ADHD). The 1968 DSM-II defined “mild brain damage” in children as a chronic brain syndrome manifested by hyperactive and impulsive behavior with reference to a new category, “hyperkinetic reaction of childhood” if the origin is not considered “organic.” As these categories evolved, they expanded to encompass the academic difficulties experienced by many of these children.

After almost 30 years of research into this general category of “minimal brain dysfunction,” representing “... children of near average, average, or above average general intelligence with certain learning or behavioral disabilities ... associated with deviations of function of the central nervous system.” ( Clements, 1966 , pp. 9–10), the field acknowledged the heterogeneity of these children and the failure of general “one size fits all” interventions. As a result, the 1980 DSM-III formally separated academic skill disorders from ADHD. The 1994 DSM-IV differentiated reading, mathematics, and written expression SLD. The DSM-5 reversed that, merging these categories into one overarching category of SLD (nosologically distinct from although comorbid with ADHD), keeping the notion of specificity by stating that SLD can manifest in three major academic domains (reading, mathematics, and writing).

The third strand originated from the development of effective interventions based on cognitive and linguistic models of observed academic difficulties. This strand, endorsed in the 1960s by Samuel Kirk and associates, viewed SLD as an overarching category of spoken and written language difficulties that manifested as disabilities in reading (dyslexia), mathematics (dyscalculia), and writing (dysgraphia). Advances have been made in understanding the psychological and cognitive texture of SLD, developing interventions aimed at overcoming or managing them, and differentiating these disorders from each other, from other developmental disorders, and from other forms of disadvantage. This work became the foundation of the 1968 advisory committee definition of SLD, which linked this definition with that of minimal brain dysfunction via the same “unexpected” exclusionary criteria (i.e., not attributable primarily to intellectual difficulties, sensory disorders, emotional disturbance, or economic/cultural diversity).

Although its exclusionary criteria were well specified, the definition of SLD did not provide clear inclusionary criteria. Thus, the US Department of Education’s 1977 regulatory definition of SLD included a cognitive discrepancy between higher IQ and lower achievement as an inclusionary criterion. This discrepancy was viewed as a marker for unexpected underachievement and penetrated the policy and practice of SLD in the US and abroad. In many settings, the measurement of such a discrepancy is still considered key to identification. Yet, IDEA 2004 and the DSM-5 moved away from this requirement due to a lack of evidence that SLD varies with IQ and numerous philosophical and technical challenges to the notion of discrepancy (Fletcher, Lyon, Fuchs, & Barnes, 2019). IDEA 2004 also permitted an alternative inclusion criterion based on Response-to-Intervention (RTI), in which SLD reflects inadequate response to effective instruction, while the DSM-5 focuses on evidence of persistence of learning difficulties despite treatment efforts.

These three stands of inquiry into SLD use a variety of concepts (e.g., word blindness, strephosymbolia, dyslexia and alexia, dyscalculia and acalculia, dysgraphia and agraphia), which are sometimes differentiated and sometimes used synonymously, generating confusion in the literature. Given the heterogeneity of their manifestation and these diverse historical influences, it has been difficult to agree on the best way to identify SLD, although there is consensus that their core is unexpected underachievement. A source of active research and controversy is whether “unexpectedness” is best identified by applying solely exclusionary criteria (i.e., simple low achievement), inclusionary criteria based on uneven cognitive development (e.g., academic skills lower than IQ or another aptitude measure, such as listening comprehension), or evidence of persisting difficulties (DSM-5) despite effective instruction (IDEA 2004).

Manifestation, Definition, and Etiology

That the academic deficits in SLD relate to other cognitive skills has always been recognized, but the diagnostic and treatment relevance of this connection has remained unclear. A rich literature on cognitive models of SLD ( Elliott & Grigorenko, 2014 ; Fletcher et al., 2019) provides the basis for five central ideas. First, SLD are componential ( Melby-Lervåg, Lyster, & Hulme, 2012 ; Peng & Fuchs, 2016 ): Their academic manifestations arise on a landscape of peaks, valleys, and canyons in various cognitive processes, such that individuals with SLD have weaknesses in specific processes, rather than global intellectual disability ( Morris et al., 1998 ). Second, the cognitive components associated with SLD, just like academic skills and instructional response, are dimensional and normally distributed in the general population ( Ellis, 1984 ), such that understanding typical acquisition should provide insight into SLD and vice versa ( Rayner, Foorman, Perfetti, Pesetsky, & Seidenberg, 2001 ). Third, each academic and cognitive component may have a distinct signature in the brain ( Figure 3 ) and genome ( Figure 4 ). These signatures and etiologies likely overlap because they are correlated, but are not interchangeable, as their unique features substantiate the distinctness of various SLD ( Vandermosten, Hoeft, & Norton, 2016 ). Fourth, the overlap at least partially explains their rates of comorbidity ( Berninger & Abbott, 2010 ; Szucs, 2016 ; Willcutt et al., 2013 ). Fifth, deficiencies in these cognitive and academic processes appear to last throughout the lifespan, especially in the absence of intervention ( Klassen, Tze, & Hannok, 2013 ).

Results of meta-analyses of functional neuroimaging studies that exemplify the distribution of activation patterns in different reading- ( A ) and mathematics- ( B ) related networks, corresponding to componential models of the skills. A (Left panel, light blue): A lexical network in the basal occipito-temporal regions and in the left inferior parietal cortex. A (Middle panel, dark blue): A sublexical network, primarily involving regions of the left temporo-parietal lobe extending from the left anterior fusiform region. A (Right panel): Activation likelihood estimation map of foci from the word>pseudowords (light blue) and pseudowords>words (dark blue) contrasts. The semantic processing cluster is shown in green. B (Left panel): A number-processing network, primarily involving a region of the parietal lobe. B (Middle panel): An arithmetic-processing network, primarily involving regions of the frontal and parietal lobes. B (Right panel): Children (red) and adult (pink) meta-analyses of brain areas associated with numbers and calculations. Figure by Janet Croog.

A schematic representation of the genetic regions and gene-candidates linked to or associated with SRD and reading-related processes (shown in blue), and SMD and mathematics-related processes (shown in red). Dark blue signifies more studied loci and genes. Blue highlighted in red indicate the genes implicated in both SRD and SMD. Figure by Janet Croog.

The DSM-5 and IDEA 2004 reflect agreement that SLD can occur in word reading and spelling (Specific Word Reading Disability; SWRD) and in specific reading comprehension disability (SRCD). SWRD represents difficulties with beginning reading skills due at least in part to phonological processing deficits, while other language indicators (e.g., vocabulary) may be preserved ( Pennington, 2009 ). In contrast, SRCD ( Cutting et al., 2013 ), which is more apparent later in development, is associated with non-phonological language weaknesses ( Scarborough, 2005 ). The magnitude of SRCD is greater than that of vocabulary or language comprehension difficulties, suggesting that other problems, such as weaknesses in executive function or background knowledge, also contribute to SRCD ( Spencer, Wagner, & Petscher, 2018 ).

Math SLDs are differentiated as calculations (SMD) versus problem solving (word problems) SLD, which are associated with distinct cognitive deficits ( L. S. Fuchs et al., 2010 ) and require different forms of intervention ( L. S. Fuchs et al., 2014 ). Calculation is more linked to attention and phonological processing, while problem solving is more linked to language comprehension and reasoning; working memory has been associated with both. Specific written expression disability, SWED ( Berninger, 2004 ; Graham, Collins, & Rigby-Wills, 2017 ) occurs in the mechanical act of writing (i.e., handwriting, keyboarding, spelling), associated with fine motor-perceptual skills, or in composing text (i.e., planning and revising, understanding genre), associated with oral language skills, executive functions, and the automaticity of transcription skills. Although each domain varies in its cognitive correlates, treatment, and neurobiology, there is overlap. By carefully specifying the domain of academic impairment, considerable progress has been made in the treatment and understanding of the factors that lead to SLD.

Identification methods have searched for other markers of unexpected underachievement beyond low achievement, but always include exclusionary factors. Diagnosis solely by exclusion has been criticized due to the heterogeneity of the resultant groups ( Rutter, 1982 ); thus, the introduction of a discrepancy paradigm. One approach relies on the aptitude-achievement discrepancy, commonly operationalized as a discrepancy between measures of IQ and achievement in a specific academic domain. IQ-discrepancy was the central feature of federal regulations for identification from 1977 until 2004, although the approaches used to qualify and quantify the discrepancy varied in the 50 states. Lack of validity evidence ( Stuebing et al., 2015 ; Stuebing et al., 2002 ) resulted in its de-emphasis in IDEA 2004 and elimination from DSM-5.

A second approach focuses on identifying uneven patterns of strengths and weaknesses (PSW) profiles of cognitive functioning to explain observed unevenness in achievement across academic domains ( Flanagan, Alfonso, & Mascolo, 2011 ; Hale et al., 2008 ; Naglieri & Das, 1997 ). According to these methods, a student with SLD demonstrates a weakness in achievement (e.g., word reading), which correlates with an uneven profile of cognitive weaknesses and strengths (e.g., phonological processing deficits with advanced visual-spatial skills). Proponents suggest that understanding these patterns is informative for individualizing interventions that capitalize on student strengths (i.e., maintain and enhance academic motivation) and compensate for weaknesses (i.e., enhance the phonological processing needed for the acquisition and automatization of reading), but little supporting empirical evidence is available ( Miciak, Fletcher, Stuebing, Vaughn, & Tolar, 2014 ; Taylor, Miciak, Fletcher, & Francis, 2017 ). Meta-analytic research suggests an absence of cognitive aptitude by treatment interactions ( Burns et al., 2016 ), and limited improvement in academic skills based on training cognitive deficits such as working memory ( Melby-Lervåg, Redick, & Hulme, 2016 ).

Newer methods of SLD identification are linked to the development of the third historical strand, based on RTI. With RTI, schools screen for early indicators of academic and behavior problems and then progress monitor potentially at-risk children using brief, frequent probes of academic performance. When data indicate inadequate progress in response to adequate classroom instruction (Tier 1), the school delivers supplemental intervention (Tier 2), usually in the form of small-group instruction.

A child who continues to struggle requires more intensive, individualized intervention (Tier 3), which may include special education. An advantage of RTI is that intervention is provided prior to the determination of eligibility for special education placement. RTI juxtaposes the core concept of underachievement with the concept of inadequate response to instruction, that is, intractability to intervention. It prioritizes the presence of functional difficulty and only then considers SLD as a possible source of this difficulty ( Grigorenko, 2009 ). Still, concerns about the RTI approach to identification remain. One concern is that RTI approaches may not identify “high-potential” children who struggle to develop appropriate academic skills ( Reynolds & Shaywitz, 2009 ). Other concerns involve low agreement across different methods for defining inadequate RTI ( D. Fuchs, Compton, Fuchs, Bryant, & Davis, 2008 ; L. S. Fuchs, 2003 ) and challenges schools face in adequately implementing RTI frameworks ( Balu et al., 2015 ; D. Fuchs & Fuchs, 2017 ; Schatschneider, Wagner, Hart, & Tighe, 2016 ).

Prevalence and Incidence

Because the attributes of SLD are dimensional and depend on the thresholds used to subdivide normal distributions ( Hulme & Snowling, 2013 ), estimates of prevalence and incidence vary. SWRD’s prevalence estimates range from 5 to 17% ( Katusic, Colligan, Barbaresi, Schaid, & Jacobsen, 2001 ; Moll, Kunze, Neuhoff, Bruder, & Schulte-Körne, 2014 ). SRCD is less frequent ( Etmanskie, Partanen, & Siegel, 2016 ), but still represents about 42% of all children ever identified with SLD in reading at any grade ( Catts, Compton, Tomblin, & Bridges, 2012 ). Estimates of incidence and prevalence of SMD vary as well: from 4 to 8% ( Moll et al., 2014 ). Cumulative incidence rates by the age of 19 years range from 5.9% to 13.8%. Similar to SWRD, SMD can be differentiated in terms of lower- and higher-order skills and by time of onset. Computation-based SMD manifests earlier; problem-solving SMD later, sometimes in the absence of computation-based SMD ( L. S. Fuchs, D. Fuchs, C. L. Hamlett, et al., 2008 ). SWED is the least studied SLD. Its prevalence estimates range from 6% to 22% ( P. L. Morgan, Farkas, Hillemeier, & Maczuga, 2016 ) and cumulative incidence ranges from 6.9% to 14.7% ( Katusic, Colligan, Weaver, & Barbaresi, 2009 ).

Comorbidity and Co-Occurrence

One reason SLD can be difficult to define and identify is that different SLDs often co-occur in the same child. Comorbidity involving SWRD ranges from 30% ( National Center for Learning Disabilities, 2014 ) to 60% ( Willcutt et al., 2007 ). The most frequently observed co-occurrences are between (1) SWRD and SMD ( Moll et al., 2014 ; Willcutt et al., 2013 ), with 30–50% of children who experience a deficit in one academic domain demonstrating a deficit in the other ( Moll et al., 2014 ); (2) SWRD and early language impairments ( Dickinson, Golinkoff, & Hirsh-Pasek, 2010 ; Hulme & Snowling, 2013 ; Pennington, 2009 ) with 55% of individuals with SWRD exhibiting significant speech and language impairment ( McArthur, Hogben, Edwards, Heath, & Mengler, 2000 ); and (3) SWRD and internalizing and externalizing behavior problems, with 25–50% of children with SWRD meeting criteria for ADHD ( Pennington, 2009 ) and for generalized anxiety disorder and specific test anxiety, depression, and conduct problems ( Cederlof, Maughan, Larsson, D’Onofrio, & Plomin, 2017 ), although comorbid conduct problems are largely restricted to the subset of individuals with both SWRD and ADHD ( Willcutt et al., 2007 ).

The co-occurrence of SMD is less studied, but there are some consistently replicated observations: (1) individuals with SMD exhibit higher rates of ADHD, and math difficulties are observed in individuals with ADHD more frequently than in the general population ( Willcutt et al., 2013 ); (2) math difficulties are associated with elevated anxiety and depression even after reading difficulties are controlled ( Willcutt et al., 2013 ); and (3) SMD are associated with other developmental conditions such as epilepsy ( Fastenau, Shen, Dunn, & Austin, 2008 ) and schizophrenia ( Crow, Done, & Sacker, 1995 ).

SLD is clearly associated with difficulties in adaptation, in school and in larger spheres of life associated with work and overall adjustment. Longitudinal research reports poorer vocational outcomes, lower graduation rates, higher rates of psychiatric difficulties, and more involvement with the justice system for individuals with SWRD ( Willcutt et al., 2007 ). Importantly, there is evidence of increased comorbidity across forms of SLD with age, with accumulated cognitive burden ( Costa, Edwards, & Hooper, 2016 ). Individuals with comorbid SLDs have poorer emotional adjustment and school functioning than those identified with a single impairment ( Martinez & Semrud-Clikeman, 2004 ).

Identification (Diagnosis)

Comorbidity indicates that approaches to assessment should be broad and comprehensive. For SLD, the choice of a classification model directly influences the selection of assessments for diagnostic purposes. Although all three models are used, the literature (Fletcher et al., 2019) demonstrates that a single indicator model, based either on cut-off scores, other formulae, or assessment of instructional response, does not lead to reliable identification regardless of the method employed. SLD can be identified reliably only in the context of multiple indicators. A step in this direction is a hybrid method that includes three sets of criteria, two inclusionary and one exclusionary, recommended by a consensus group of researchers (Bradley, Danielson, & Hallahan, 2002). The two inclusionary criteria are evidence of low achievement (captured by standardized tests of academic achievement) and evidence of inadequate RTI (captured by curriculum-based progress-monitoring measures or other education records). The exclusionary criterion should demonstrate that the documented low achievement is not primarily attributable to “other” (than SLD) putative causes such as (a) other disorders (e.g., intellectual disability, sensory or motor disorders) or (b) contextual factors (e.g., disadvantaged social, religious, economic, linguistic, or family environment). In the future, it is likely that multi-indicator methods will be extended, with improved identification accuracy, by the addition of other indicators, neurobiological, genetic, or behavioral. It is also possible that assessment of specific cognitive processes beyond academic achievement will improve identification, but presently there is little evidence that such testing adds value to identification ( Elliott & Grigorenko, 2014 ; Fletcher et al., 2019). All identification methods for SLD assume that children referred for assessment are in good health or are being treated and that their physical health, including hearing and vision, is monitored. Currently, there are no laboratory tests (i.e., DNA or brain structure/activity) for SLD. There are also no tests that can be administered by an optometrist, audiologist, or physical therapist to diagnose or treat SLD.

Etiological Factors

Neural structure and function.

Since the earliest reports of reading difficulties, it has been assumed that the loss of function (i.e., acquired reading disability) or challenges in the acquisition of function (i.e., congenital reading disability) are associated with the brain. Functional patterns of activation in response to cognitive stimuli show reliable differences in degrees of activation between typically developing children and those identified with SWRD, and reveal different spatial distributions in relation to children identified with SMD and ADHD ( Dehaene, 2009 ; Seidenberg, 2017 ). In SWRD, there are reduced gray matter volumes, reduced integrity of white matter pathways, and atypical sulcal patterns/curvatures in the left-hemispheric frontal, occipito-temporal, and temporo-parietal regions that overlap with areas of reduced brain activation during reading.

These findings together indicate the presence of atypicalities in the structures (i.e., grey matter) that form the neural system for reading and their connecting pathways (i.e., white matter). These structural atypicalities challenge the emergence of the cognitive—phonological, orthographic, and semantic—representations required for the assembly and automatization of the reading system. Although some have interpreted the atypicalities as a product of reading instruction ( Krafnick, Flowers, Luetje, Napoliello, & Eden, 2014 ), there is also evidence that atypicalities can be observed in pre-reading children at risk for SWRD due to family history or speech and language difficulties ( Raschle et al., 2015 ), sometimes as early as a few days after birth with electrophysiological measures ( Molfese, 2000 ). What emerges in a beginning reader, if not properly instructed at developmentally important periods, is a suboptimal brain system that is inefficient in acquiring and practicing reading. This system is complex, representing multiple networks aligned with different reading-related processes ( Figure 3 ). The system engages cooperative and competitive brain mechanisms at the sublexical (phonological) and lexical levels, in which the phonological, orthographic, and semantic representations are utilized to rapidly form representations of a written stimulus. Proficient readers process words on sight with immediate access to meaning ( Dehaene, 2009 ). In addition to malleability in development, there is strong evidence of malleability through instruction in SWRD, such that the neural processes largely normalize if the intervention is successful ( Barquero, Davis, & Cutting, 2014 ).

The functional neural networks for SMD also vary depending on the mathematical operation being performed, just as the neural correlates of SWRD and SRCD do ( Cutting et al., 2013 ). Neuroimaging studies on the a(typical) acquisition of numeracy posit SMD ( Arsalidou, Pawliw-Levac, Sadeghi, & Pascual-Leone, 2017 ) as a brain disorder engaging multiple functional systems that together substantiate numeracy and its componential processes ( Figure 3 ). First, the intraparietal sulcus, the posterior parietal cortex, and regions in the prefrontal cortex are important for representing and processing quantitative information. Second, mnemonic regions anchored in the medial temporal lobe and hippocampus are involved in the retrieval of math facts. Third, additional relevant regions include visual areas implicated in visual form judgement and symbolic processing. Fourth, prefrontal areas are involved in higher-level processes such as error monitoring, and maintaining and manipulating information. As mathematical processes become more automatic, reliance on the parietal network decreases and reliance on the frontal network increases. All these networks, assembled in a complex functional brain system, appear necessary for the acquisition and maintenance of numeracy, and various aberrations in the functional interactions between networks have been described. Thus, SMD can arise as a result of disturbances in one or multiple relevant networks, or interactions among them ( Arsalidou et al., 2017 ; Ashkenazi, Black, Abrams, Hoeft, & Menon, 2013 ). There is also evidence of malleability and the normalization of neural networks with successful intervention in SMD ( Iuculano et al., 2015 ).

Genetic and environmental factors

Early case studies of reading difficulties identified their familial nature, which has been confirmed in numerous studies utilizing genetically-sensitive designs with various combinations of relatives—identical and fraternal twins, non-twin siblings, parent-offspring pairs and trios, and nuclear and extended families. The relative risk of having SWRD if at least one family member has SWRD is higher for relatives of individuals with the condition, compared to the risk to unrelated individuals; higher for children in families where at least one relative has SWRD; even higher for families where a first-degree relative (i.e., a parent or a sibling) has SWRD; and higher still for children in families where both parents have SWRD ( Snowling & Melby-Lervåg, 2016 ). Quantitative-genetic studies estimate that 30–80% of the variance in reading, math or spelling outcomes is explained by heritable factors ( Willcutt et al., 2010 ).

Since the 1980s, there have been systematic efforts to identify the sources of structural variation in the genome, i.e., genetic susceptibility loci that can account for the strong heritability and familiality of SWRD ( Figure 4 ). These efforts have yielded the identification of nine regions of the genome thought to harbor genes, or other genetic material, whose variation is associated with the presence of SWRD and individual differences in reading-related processes. Within these regions, a number of candidate genes have been tapped, but no single candidate has been unequivocally replicated as a causal gene for SWRD, and observed effects are small. In addition, multiple other genes located outside of the nine linked regions have been observed to be relevant to the manifestation of SWRD and related difficulties. Currently there are ongoing efforts to interrogate candidate genes for SWRD and connect their structural variation to individual differences in the brain system underlying the acquisition and practice of reading.

There are only a few molecular-genetic studies of SMD and its related processes ( Figure 4 ). Unlike SWRD, no “regions of interest” have been identified. Only one study investigated the associations between known single-nuclear polymorphisms (SNP) and a composite measure of mathematics performance derived from various assessments of SMD-related componential processes and teacher ratings. The study generated a set of SNPs that, when combined, accounted for 2.9% of the phenotypic variance ( Figure 4 shows the genes in which the three most statistically significant SNPs from this set are located). Importantly, when this SNP set was used to study whether the association between the 10-SNP set and mathematical ability differs as a function of characteristics of the home and school, the association was stronger for indicators of mathematical performance in chaotic homes and in the context of negative parenting.

Finally, studies have investigated the pleiotropic (i.e., impacting multiple phenotypes) effects of SWRD candidate genes on SMD, ADHD, and related processes. These effects are seemingly in line with the “generalist genes” hypothesis, asserting the pleiotropic influences of some genes to multiple SLD ( Plomin & Kovas, 2005 ).

Environmental factors are strong predictors of SLD. These factors penetrate all levels of a child’s ecosystem: culture, demonstrated in different literacy and numeracy rates around the world; social strata, captured by social-economic indicators across different cultures; characteristics of schooling, reflected by pedagogies and instructional practices; family literacy environments through the availability of printed materials and the importance ascribed to reading at home; and neighborhood and peer influences. Interactive effects suggest that reading difficulties are magnified when certain genetic and environmental factors co-occur, but there is evidence of neural malleability even in SWDE ( Overvelde & Hulstijn, 2011 ). Neural and genetic factors are best understood as risk factors that variably manifest depending on the home and school environment and child attributes like motivation.

Intervention

Although the content of instruction varies depending on whether reading, math, and/or writing are impaired, general principles of effective intervention apply across SLD i . First, intervention for SLD is explicit ( Seidenberg, 2017 ): Teachers formally present new knowledge and concepts with clear explanations, model skills and strategies, and teach to mastery with cumulative practice with ongoing guidance and feedback. Second, intervention is individualized: Instruction is formatively adjusted in response to systematic progress-monitoring data ( Stecker, Fuchs, & Fuchs, 2005 ). Third, intervention is comprehensive and differentiated, addressing the multiple components underlying proficient skill as well as comorbidity. Comprehensive approaches address the multifaceted nature of SLD and provide more complex interventions that are generally more effective than isolated skills training in reading ( Mathes et al., 2005 ) and math ( L. S. Fuchs et al., 2014 ). For example, children with SLD and ADHD may need educational and pharmacological interventions ( Tamm et al., 2017 ). Anxiety can develop early in children who struggle in school, and internalizing problems must be treated ( Grills, Fletcher, Vaughn, Denton, & Taylor, 2013 ). Differentiation through individualization in the context of a comprehensive intervention also permits adjustments of the focus of an intervention on specific weaknesses.

Fourth, intervention adjusts intensity as needed to ensure success, by increasing instructional time, decreasing group size, and increasing individualization ( L. S. Fuchs, Fuchs, & Malone, 2017 ). Such specialized intervention is typically necessary for students with SLD ( L. S. Fuchs et al., 2015 ). Yet, effective instruction for SLD begins with differentiated general education classroom instruction ( Connor & Morrison, 2016 ), in which intervention is coordinated with rather than supplanting core instruction ( L. S. Fuchs, D. Fuchs, C. Craddock, et al., 2008 ).

In addition, intervention is more effective when provided early in development. For example, intervention for SWRD was twice as effective if delivered in grades 1 or 2 than if started in grade 3 ( Lovett et al., 2017 ). This is underscored by neuroimaging research ( Barquero et al., 2014 ) showing that experience with words and numbers is needed to develop the neural systems that mediate reading and math proficiency. A child with or at risk for SWRD who cannot access print because of a phonological processing problem will not get the reading experience needed to develop the lexical system for whole word processing and immediate access to word meanings. This may be why remedial programs are less effective after second grade; with early intervention, the child at risk for SLD develops automaticity because they have gained the experience with print or numbers essential for fluency. Even with high quality intensive intervention, some children with SLD do not respond adequately, and students with persistent SLD may profit from assistive technology (e.g., computer programs that convert text-to-speech; Wood, Moxley, Tighe, & Wagner, 2018 ).

Finally, interventions for SLD must occur in the context of the academic skill itself. Cognitive interventions that do not involve print or numbers, such as isolated phonological awareness training or working memory training without application to mathematical operations do not improve reading or math skill ( Melby-Lervåg et al., 2016 ). Physical exercises (e.g., cerebellar training), optometric training, special lenses or overlays, and other proposed interventions that do not involve teaching reading or math are ineffective ( Pennington, 2009 ). Pharmacological interventions are effective largely due to their impact on comorbid symptoms, with little evidence of a direct effect on the academic skill ( Tamm et al., 2017 ).

No evaluations of recovery rate from SLD have been performed. Intervention success has been evaluated as closing the age-grade discrepancy, placing children with SLD at an age-appropriate grade level, and maintaining their progress at a rate commensurate with typical development. Meta-analytic studies estimate effect sizes of academic interventions at 0.49 for reading ( Scammacca, Roberts, Vaughn, & Stuebing, 2015 ), 0.53 for math ( Dennis et al., 2016 ), and 0.74 for writing ( Gillespie & Graham, 2014 ).

Implications for Practice and Research

Practitioners should recognize that the psychological and educational scientific evidence base supports specific approaches to the identification and treatment of SLD. In designing SLD evaluations, assessments must be timely to avoid delays in intervention; they must consider comorbidities as well as contextual factors, and data collected in the context of previous efforts to instruct the child. Practitioners should use the resulting assessment data to ensure that intervention programs are evidence-based and reflect explicitness, comprehensiveness, individualization, and intensity. There is little evidence that children with SLD benefit from discovery, exposure, or constructivist instructional approaches.

With respect to research, the most pressing issue is understanding individual differences in development and intervention from neurological, genetic, cognitive, and environmental perspectives. This research will ultimately lead to earlier and more precise identification of children with SLD, and to better interventions and long-term accommodations for the 2–6% of the general population who receive but do not respond to early prevention efforts. More generally, other human conditions may benefit from the examples of progress exemplified by the integrated, interdisciplinary approaches that underlie the progress of the past 50 years in the scientific understanding of SLD.

Acknowledgments

The authors are the Principal Investigators of the currently funded Learning Disabilities Research Centers ( https://www.nichd.nih.gov/research/supported/ldrc ) and Innovation Hubs ( https://www.nichd.nih.gov/research/supported/ldhubs ), the two key NICHD programs supporting research on Specific Learning Disabilities. The preparation of this articles was supported by P20 HD090103 (PI: Compton), P50 HD052117 (PI: Fletcher), P20 HD075443 (PI: Fuchs), P20 HD091005 (PI: Grigorenko), P50 HD052120 (PI: Wagner), and P50 HD27802 (PI: Willcutt). Grantees undertaking such projects are encouraged to express their professional judgment. Therefore, this article does not necessarily reflect the position or policies of the abovementioned agencies, and no official endorsement should be inferred.

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MINI REVIEW article

Psychological aspects of students with learning disabilities in e-environments: a mini review and future research directions.

• Department of Pedagogy, Psychology, Philosophy, University of Cagliari, Cagliari, Italy

What are the main learning difficulties or advantages encountered by students with learning disabilities (LDs) within e-environments? As a result of the Covid-19 emergency, e-learning is being increasingly used to support students’ learning processes. A number of countries closed their schools altogether, so face-to-face lessons were and have been replaced by distance lessons. A search of current literature via Scopus, Eric and Google Scholar electronic databases was conducted according to Prisma Guidelines. Other sources of literature were also considered, starting from the references in the full text of the articles consulted. We used the following search keywords: “LDs” combined with the “AND/OR” Boolean operator and “e-learning platforms,” “well-being,” “psychological factors,” “emotional distress,” and “self-regulation.” One body of literature highlights the lack of inclusive accessibility standards and a lack of attention to specific tools for addressing LDs, which causes students to develop high levels of stress/anxiety and emotional distress, in addition to low levels of well-being, self-esteem and self-efficacy. Another area of literature looks at how students can develop high levels of self-regulation and emotional awareness, as well as high levels of inclusion. Results are discussed in terms of the promotion of e-learning that focuses on the psychological well-being of students and teachers use of technological tools.

Introduction

The forced interruption of face-to-face teaching due to the worldwide outbreak of Covid-19, has significantly reactivated the debate on the concrete effectiveness and functionality of e-learning courses. Specifically, our goal was to better understand the psychological effects and efficacy of the current massive use of the e-environments on students with learning disabilities (LDs) ( Viner et al., 2020 ). Literature shows a variety of ways to define e-learning. For example, Cidral et al. (2018) define e-learning as a web-based learning system for the dissemination of information, communication, and knowledge for education and training. Until 2002, Eletti had affirmed that e-learning is a new type of training, a new teaching system that allows you to follow and above all personalize learning. The services and tools used allow for continuous contact with the “student”. In addition, a platform and an interface built ad hoc , adapting the contents, allows to model the teaching on the user’s needs ( Eletti, 2002 ). Thus, in light of the massive use of e-environments, there is a definite need to question how effective these tools are for students with LDs. According to international diagnostic criteria, LDs are an overarching group of neurodevelopmental disorders comprising different learning disorders that affect primary and/or secondary academic abilities and a child’s overall capabilities ( American Psychiatric Association, 2013 ; Schulte-Korne, 2014 ). Children with specific LDs are a rather heterogeneous group, both with regard to specific academic abilities such as listening, thinking, reading, speaking, writing, calculating, and spelling ( Sorrenti et al., 2019 ), as well as to their neuropsychological and functional profiles. For example, they may have impairments affecting different cognitive and neuropsychological abilities (working memory), long-term memory (implicit and explicit memory), attention (selective and sustained), and linguistic, praxis, visuospatial, problem solving, and/or executive abilities ( Petretto and Masala, 2017 ; Visser et al., 2020 ), etc. Moreover, there is general agreement on the association between LDs and other neurodevelopmental disorders (ADHD and specific language disorders); LDs typically occur in individuals of normal intelligence ( Sorrenti et al., 2019 ). A body of studies indicates a relationship between children’s LD and poor social relations in school ( Walker and Nabuzoka, 2007 ), this aspect is confirmed also in the University context ( Filippello et al., 2019 ). Literature shows a relationship between LDs and internalizing (depressive and anxiety disorders) and externalizing disorders (conduct disorders) ( Frith, 2013 ; Bonifacci et al., 2016 ; Panicker and Chelliah, 2016 ; Visser et al., 2020 ). If LDs are not adequately treated, they can evolve over time, potentially resulting in forms of psycho-social maladjustment ( Sorrenti et al., 2019 ). Regarding the use of e-learning, only a small number of studies have addressed these psychological factors and consequences, and there are few studies which have directly examined the quality of life of students with LDs, or the quality of interpersonal relationships (parents, teachers, and peers). In this mini-review and according to previous research in the field, we analyze these aspects and focus our attention to the following questions:

(1) What are the effects of the use of e-learning on psychological well-being?

(2) What are the effects of accessibility standards in promoting inclusion and in reducing stress, anxiety and emotional distress among students with LDs?

Methodology

A search of current literature using Scopus, Eric and Google Scholar electronic databases was conducted according to Prisma Guidelines ( Moher et al., 2015 ). Other sources of literature were also considered, starting from the references in the full texts of the articles examined. We used the following search keywords: “LDs” combined with the “AND/OR” Boolean operator and “e-learning platforms,” “well-being,” “psychological factors,” “emotional distress,” and “self-regulation”. Applying a systematic procedure, literature was then selected and results were charted and analyzed. The following inclusion criteria were established: papers on the use of e-learning with LD; on the relationship between e-learning platforms and related psychological aspects (self-esteem, emotional distress, and self-regulation); written in English and published from 2015 to 2020. The following exclusion criteria were applied: systematic reviews; papers on the use of e-learning without LD. On the basis of the research questions and the literature considered, we chose a minireview. For this reason the data will be presented as a narrative review.

Results and Discussion

In the first part of the search, two independent assessors found 53 articles. Applying our inclusion and exclusion criteria, after reading the abstract, 27 articles were considered. After reading the full texts, 4 further articles were excluded, thus a final group of 23 articles were considered ( Table 1 ). As expected, in literature, regarding the definition of “e-learning”, we found different systems and tools (platforms, devices, web materials/sites, Learning Content Management Systems, ICT, etc.). According to Bjekic et al. (2014) we categorized the different definitions in two groups. The first group refers to the use of Assistive Technology (AT) (hardware or software, used to increase, improve or maintain capabilities of persons with LDs aimed to support and/or increase learning). The second group of e-learning refers to a system of procedures, processes and instructional materials that supports learning. Moreover, we considered a difference between e-platforms and ICT tools ( Salehi et al., 2015 ; Table 2 ).

Table 1. Characteristics of papers which met the inclusion criteria.

Table 2. Papers which met the inclusion criteria in the school setting analyzed according to Bjekic et al. (2014) .

The papers showed a certain amount of heterogeneity in their definition of LDs. Some authors proposed a specific definition ( Chen et al., 2015 ; Richardson, 2015 ; Shonfeld and Ronen, 2015 ; Straub and Vasquez, 2015 ; Benmarrakchi et al., 2017 ; Sharabi et al., 2016 ; Adam and Tatnall, 2017 ; Vasalou et al., 2017 ; Lambert and Dryer, 2018 ; Lipka et al., 2019 ; Ziadat, 2019 ), while others proposed a general reference to Special Educational Needs or used the World Health Organization definition of Disability ( World Health Organization, 2001 ; Berizzi et al., 2017 ; Naumova et al., 2017 ; García-González et al., 2020 ). Some papers reported the definition of LD based on international diagnostic criteria, others described specific national law/s or references ( Sharabi et al., 2016 ). Moreover, with regard to sample recruitment, some authors chose samples consisting of different groups of students with other kinds of disabilities and then specified the number of students with LDs ( Richardson, 2015 , 2016 ; Shonfeld and Ronen, 2015 ; Terras et al., 2015 ; Benmarrakchi et al., 2017 ; Sharabi et al., 2016 ; Alamri and Tyler-Wood, 2017 ; Berizzi et al., 2017 ; Kent et al., 2018 ; Lipka et al., 2019 ; Ouherrou et al., 2019 ; García-González et al., 2020 ); while in other papers, the sample is made up only of students with LDs ( Chen et al., 2015 ; Straub and Vasquez, 2015 ; Vasalou et al., 2017 ; Lambert and Dryer, 2018 ). Regarding the level of schooling, about 1/2 of the studies focused on University environments ( Richardson, 2015 , 2016 ; Terras et al., 2015 ; Alamri and Tyler-Wood, 2017 ; Naumova et al., 2017 ; Kent et al., 2018 ; García-González et al., 2020 ) and the other 1/2 examined primary and secondary schools ( Chen et al., 2015 ; Straub and Vasquez, 2015 ; Benmarrakchi et al., 2017 ; Rice and Carter, 2016 ; Smith et al., 2016 ; Adam and Tatnall, 2017 ; Berizzi et al., 2017 ; Vasalou et al., 2017 ; Baharuddin and Dalle, 2019 ; Lipka et al., 2019 ; Ouherrou et al., 2019 ; Ziadat, 2019 ; Nieto-Márquez et al., 2020 ). One paper focused on the transition from school to university ( Sharabi et al., 2016 ). As expected, we also found a considerable heterogeneity in school settings, ranging from mainstream school/classrooms to special needs schools/classrooms, according to specific national and theoretical approaches and policies regarding the field of inclusion (see Table 2 ). Given that the countries in our sample ranged across Europe, United States, as well as Arab and Slavic countries, there was some diversity in the idea of inclusive policies for students with LDs. This is due to national differences regarding the issues of policies for students with LDs and, in general, for students with SEN. In some countries, there is an inclusion-based approach where students with LDs are placed in mainstream schools; in other countries there are special schools and special classrooms for them. In some countries, transition to complete inclusion is still ongoing ( Lindsay, 2016 ; Norwich, 2016 ; Petretto et al., 2019 ; Pilia, 2019 ). While one of the papers described a specific experience in two special needs classes ( Adam and Tatnall, 2017 ), other research papers concentrated on the use of specific e-learning approaches to designated groups of children with LDs or to all the children in the classroom in mainstream schools ( Straub and Vasquez, 2015 ; Vasalou et al., 2017 ).

The approaches employed range from the use of specific devices and/or platforms, to the use of specific “reasonable accommodations” (such as font quality and sizes in the learning materials on the web or the use of specific support technologies) ( Chen et al., 2015 ; Benmarrakchi et al., 2017 ; Rice and Carter, 2016 ; Alamri and Tyler-Wood, 2017 ; Berizzi et al., 2017 ; Ouherrou et al., 2019 ; García-González et al., 2020 ); or the use of software/games aimed to increase specific abilities in students with LDs ( Straub and Vasquez, 2015 ; Vasalou et al., 2017 ). For university settings, some articles describe the experiences of so-called “Open universities” that have been based on distance learning methods since they started. With the development of ICTs, in the past few decades these universities have started to use e-learning platforms to contact students and to promote learning and social connections ( Richardson, 2015 , 2016 ; Kent et al., 2018 ). Their ongoing experiences focus mainly on the attainment of students with LDs as well as on the need to increase access to information and learning. Other studies focus on the need for dedicated online courses to specific categories of students, aiming at reducing barriers and distances and providing specific accommodations ( Terras et al., 2015 ).

The age range in these university samples is very wide. From a positive perspective it can represent a sign of the wider opportunity for older people to access university courses. However, according to some studies, it could be also the sign of a lower and slower attainment of students with LDs in University ( Richardson, 2015 , 2016 ; Shonfeld and Ronen, 2015 ). The topics of attainment and achievement are interesting because even though some papers have discussed the risk of low achievement for students with LDs, other studies have demonstrated the positive effect of accommodations and have showed examples of unexpected achievement by LD students ( Shonfeld and Ronen, 2015 ). Another aspect is the fear of disclosure of their diagnosis by some students with LDs and the effects on their tendency to hide diagnoses rather than to communicate it, even when they should do so in order to define specific “reasonable accommodations” ( Richardson, 2015 , 2016 ; Terras et al., 2015 ). Although there may be increased student awareness of the need to disclose their diagnosis and the functional profiles that help to define a personalized approach that facilitates their access to learning and materials, some authors have highlighted the importance of further discussing the role of communication between teachers/instructors and students with LDs in the development of more comfortable learning environments and in the pursuit of shared learning and achievement aims ( Terras et al., 2015 ).

Focus on Psychological Well-Being

Few studies have directly examined the psychological aspects of students with LDs in e-environments. Some papers have focused on psychological consequences of the intensified use of Information and Communications Technologies (ICTs); other papers instead focused especially on adults, addressing some psychological effects of e-learning procedures adapted to students with LDs. In their study, Ouherrou et al. (2019) highlighted the fact that the integration of ICTs in special needs education may have a positive impact on the emotional states of children with LDs, because they may experience fewer negative emotions than findings of current literature would suggest with regard to the presence of higher levels of negative emotions in the classroom. Vasalou et al. (2017) argued that a socially constructed view of digital games-based learning provides new opportunities for the support of children with dyslexia. Children spontaneously engage in “game talk” regarding game performance, content, actions and they strategically use their individual game experiences to express their personality and interact with their peers. Also, such experiences can help improve the intra-individual function by enhancing a child’s self-esteem. The findings of Sharabi et al. (2016) supported earlier studies that assessed children and adolescents with LDs ( Sharabi and Margalit, 2014 ), showing that college students with LDs possess lower levels of personal resources (sense of coherence, hope and academic self-efficacy) and suffer higher levels of social distress and loneliness than their peers. The loneliness factor was predicted by measuring online avoidance coping, their amount of smartphone use and by examining their personal resources, the use of ICTs may provide additional environmental conditions to enable youngsters to meet their emotional needs. At the same time, these opportunities may also be misused as avoidance coping and thus may contribute to increased loneliness and lower academic self-efficacy. Coherently with previous studies, Lambert and Dryer (2018) highlighted that in high education the e-environment had a negative influence on the quality of life of students with increased stress and anxiety, the perception of feelings of inadequacy, a decrease in time available for other activities and personal relationships. The same authors also highlighted that for many students, the academic and emotional support provided by family and friends was a key factor in study success. Studies on the perception of the impact of e-learning on the development of academic skills and social interaction from the perspective of students and/or teachers showed that the quality of teacher-student relationships contribute to producing improvements in learning achievement ( Alamri and Tyler-Wood, 2017 ; Lipka et al., 2019 ; Ziadat, 2019 ). Only a small number of studies have considered the role of parents. Smith et al. (2016) investigated parents’ perceptions and experiences regarding exclusive online learning for their children with disabilities. The results showed that this experience altered parents’ previous roles and that many parents were not equipped to take a teaching role due to lack of training, time, and other constraints. A parent-as-teacher role can negatively affect parent–child dynamics, leading to frustration for parent and child but full online learning requires increased parent–teacher communication. This increased level of interaction and the positive outcomes associated with the shared information enhanced a collaborative parent–teacher relationship. The use of ICT and e-learning can improve the learning of students with LDs only where a supportive context is present. The support provided by family, teachers and peers can create a protective factor which improves the well-being of students with LDs.

Focus on the Accessibility Standards and Emotional Distress

Many of the difficulties in designing e-learning courses are due to accessibility issues that can affect successful engagement ( Draffan, 2012 ; Seale, 2013 ). The heterogeneity of the LD population entails great challenges to all parties involved in creating, managing and using e-learning content, tools and platforms with accessibility features ( Guenaga et al., 2004 ; Baharuddin and Dalle, 2019 ). Some papers described the risks of a design approach based on a general and average idea of students without LDs ( Kent et al., 2018 ). For Beacham and Alty (2006) the e-learning materials commonly employed were developed with the needs and capabilities of non-dyslexic learners in mind; clearly, resources do not generally take into consideration the individual learning approaches that these students manifest ( Alsobhi and Abeysinghe, 2013 ; Chen et al., 2015 ; Luongo, 2018 ). Chen et al. (2015) also underline this point, observing that empirically derived guidelines for designing accessible online learning environments for learners with dyslexia are still scarce. The problem of accessibility is fundamental in e-learning design, as it is strictly linked to certain psychological factors that will affect students, like willingness to focus on learning, management of emotions and behavior, learning motivation, interest and self-regulation ( Chen et al., 2015 ; Berizzi et al., 2017 ; Luongo, 2018 ). Existing literature provides clear evidence that text-based synchronous activities commonly used in education, like chat programs and videoconference, can create psychological and learning difficulties. However, only a small number of papers take into account the problems of students with LDs in collaborative environments ( Luongo, 2018 ). Some papers focus on the positive aspects of the use of e-learning platforms in increasing accessibility to information and learning materials ( Richardson, 2016 ), above all because participation in remote activities, like on-line forum discussions, improves the autonomy and self-regulation of students ( Berizzi et al., 2017 ). These aspects are reinforced by continuous support of tutors and peers, and reflection on what has been done, the goals to be achieved, and ultimately the strategies to be adopted. Other articles described the possible role of a “universal design for learning approach” in the design of websites, web materials and e-learning platforms ( Chen et al., 2015 ; Shonfeld and Ronen, 2015 ; Alamri and Tyler-Wood, 2017 ; Kent et al., 2018 ; Nieto-Márquez et al., 2020 ) in order to create environments that can be useful also for students with LDs.

This mini-review has attempted to analyze both the quality of life of students with LDs and their interpersonal relationships and the features of e-learning that can have positive and negative effects on them. The considerable heterogeneity of the articles we selected led us to the following reflections: we are aware that the heterogeneity could represent a limit but also an expected consequence of the chosen way of to explore a complex topic. Bearing in mind this issue, in a following article we will discuss the picture of the state of art that we derived from this minireview. In the near future, we will explore specific and more focused aspects, also with an attention on intervention aims. Two issues are emerged.

The first is how important online-support is to consolidate teacher-learner relationships, as it can affect a student’s well-being and learning achievement. We know that e-learning is a psychological process supported by e-technology, and learning is a social activity. Understanding that it is socially constructed should ensure that e-learning is organized to promote participation, allowing all students to take part in all activities, thus enhancing cooperative-learning.

The second consideration regards the fundamental role of accessibility and “reasonable accommodations”, which should lead to a reduction of emotional distress and promote positive psychological factors through full engagement with e-learning. In order to be effective, e-learning must go beyond simply digitizing books and ought to be designed carefully and appropriately for learners ( Penna and Stara, 2007 , 2010 ). What about the current and ongoing experience of the massive use of e-learning due to the COVID-19 outbreak? We agree with Al Lily et al. (2020) , who coined the term “Crisis Distance learning,” that the current ongoing experience is different from previous ones, and that caution is needed before making any kind of generalizations from previous experiences. Nevertheless, some general considerations can be drawn for future research. It is necessary to encourage and maintain cooperative approaches in all spheres, including in the use of e-learning in school and universities, with particular attention on the quality of the relationships between all the people involved (students-teachers-parents-peers) and with an even more specific focus on the psychological needs of students with LDs. The improvement of e-learning systems designed with attention to the care and quality of relationships can promote well-being among all parties involved in the learning process.

Author Contributions

All authors equally contributed to the design of the study. All authors have read and agreed to the published version of the manuscript.

This work was supported by ATS Sardinia: title project “ Profilo Neuro-Psicologico e Problematiche Emotive nei DSA: Una Proposta di Ricerca-Intervento” - – “Neuro-Psychological Profile and Emotional Problems in LDs: A Research-Intervention Proposal ” (November, 2019; June 2021).

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Visser, L., Kalmar, J., Linkersdorfer, J., Gorgen, R., Rother, J., Marcus, H., et al. (2020). Comorbidities between specific learning disorders and psychopathology in elementary school children in Germany. Front. Psychiatry 11:292. doi: 10.3389/fpsyt.2020.00292

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Ziadat, A. H. (2019). The impact of e – learning in developing academic skills and social interaction among students with learning disabilities in jordan from the perspective of their teachers. TEM J. 8, 1440–1448. doi: 10.18421/TEM84-48

Keywords : e-learning, psychological well-being, emotional distress, self-regulation, learning disabilities

Citation: Cataudella S, Carta S, Mascia ML, Masala C, Petretto DR and Penna MP (2021) Psychological Aspects of Students With Learning Disabilities in E-Environments: A Mini Review and Future Research Directions. Front. Psychol. 11:611818. doi: 10.3389/fpsyg.2020.611818

Received: 29 September 2020; Accepted: 01 December 2020; Published: 07 January 2021.

Reviewed by:

Copyright © 2021 Cataudella, Carta, Mascia, Masala, Petretto and Penna. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Stefania Cataudella, [email protected]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

Greater Good Science Center • Magazine • In Action • In Education

How to Help Students with Learning Disabilities Focus on Their Strengths

I sat across the table from Dawn, a wide-eyed eight-year-old girl in pigtails, bracing myself to tell her the news. 

I have told students they have a learning disability hundreds of times over my 20 years as a school psychologist. But there was something about her earnest and expectant face that made me pause.

Was giving her an official label going to make her feel stigmatized and defeated? 

Would the benefits of having access to more specialized services outweigh the cost to her academic self-esteem?

I carefully explained how her brain worked with a visual aid of her brain in pictures. I told her where she was strong and where she needed to do “brain pushups” to get stronger. And I told her that she had something called “dyslexia.”

And she jumped out of her chair, smiled ear to ear, screamed “YES!,” and did a move I’ve seen in sports celebrations many times—the signature victory arm pump. 

Wait, what? 

As it turns out, I had also tested her brother a few years before, and he had dyslexia, too. He had told her that knowing he had dyslexia made him not feel stupid, and that it really helped his teachers understand how his brain worked differently. Dawn told me, “So this means I’m not dumb!”

According to labeling theory , when you label a student with a learning disability, this creates a problem—they hold lower expectations for themselves and others hold lower expectations of them. In turn, the student may live up to these low expectations. However, the research supporting this claim is controversial. Although students with learning disabilities do tend to struggle with lower achievement and hold negative beliefs about their academic abilities , some researchers point out that it is difficult to disentangle what is causing these challenges. It gets murky—would Dawn be behind her classmates in reading because she has dyslexia, because she doesn’t have access to high-quality support programs, or because her teachers and parents now hold lower expectations for her?

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We don’t really know. But the reality is that, in our public schools, access to special services sometimes depends upon having a diagnosis or label—and it’s possible that the way we treat students with these labels is holding them back. Here are several research-backed tips for educators and parents to reduce the negative effects of labels and the stigma around them. 

1. Don’t focus on the labels

Language is powerful. Even a subtle shift in language can influence how students see themselves and how stigmatized they feel. A 2018 article by researcher Mark Weist and his colleagues offers a number of suggestions for reducing the stigma of labels:

• When a label is required, describe to the student why labels are used. For example, “Labels help us understand why reading is hard for you and what the research says about how to help. Labels can also give you more specific help that you might not be able to get without the label. But we are all going to focus on what we are going to do to help, not what we call it.”
• Use person-centered language. Instead of referring to a student as a “dyslexic student,” refer to them as “a student with dyslexia.” 
• When possible, especially with younger students, use less stigmatizing language in describing their challenges (e.g., “learning differences” versus “learning disorder” or “severe dyslexia”).

By using this language, we’re trying to prevent students from overidentifying with their challenges and weaknesses.

2. Focus on the “sea of strengths” around the “islands of weakness” 

What if you were defined only by your greatest weakness? Focusing on strengths isn’t just a nice thing to do; it’s essential for students with learning disabilities (and all students) to feel good about themselves as learners.

Get Support

Did you know that every public school has access to the services of a school psychologist, who can provide support for students with learning challenges? School psychologists are uniquely qualified members of school teams who support students’ ability to learn and teachers’ ability to teach. They can be reached by inquiring directly at the school or the district’s central office, or locating contact information on the school or district website.

Renowned dyslexia expert Sally Shaywitz of the The Yale Center for Dyslexia & Creativity coined this phrase: “Dyslexia is an island of weakness surrounded by a sea of strengths.” When students see their weaknesses as “islands,” their challenges become more specific and manageable (e.g., “I need help figuring out long words”) rather than global and difficult to tackle (e.g., “I am not good at reading”).

Using specific language can also foster a “growth mindset” more conducive to learning. Rather than thinking, “I don’t have a math brain because I have dyscalculia,” students might say, “I need to do brain pushups in math.” The same goes for adults when we talk about students; instead of saying, “He has a major reading and learning deficit,” we can make it more specific and manageable by saying, “He has areas of strength and weakness in learning to read that we can address through phonics instruction.” 

Research is also emerging on the sea of “hidden strengths” of students with dyslexia and other learning disabilities:

• Strong visual-spatial thinking and skill in figuring out patterns
• The ability to make unique associations between concepts
• Strengths in seeing the “big picture” and creative problem solving

As educators and parents, we can highlight these strengths with students like Dawn (who, incidentally, scored off the charts on tests of visual logic puzzles). Indeed, all students would profit from having a spotlight on their unique talents and skills and not just on their core weaknesses.

“Often times we focus on the difficulties, but they have these incredible superpowers,” says psychologist Tracy Alloway, whose new children’s book series turns children’s learning disabilities into superpowers. Bringing those “superpowers” to the front of the discussion is a tool for empowering those with learning disabilities and helping other children appreciate their classmates’ unique talents, as well.

3. Foster self-awareness and self-advocacy skills

Not only can we focus on the extraordinary strengths these students already have, but we can also help them cultivate other strengths they’ll need to be successful.

Being diagnosed with a learning disability does not have to resign students to a life of struggle, frustration, and poor achievement. In a 30-year study by the Frostig Center , students with learning disabilities did better in their academic and personal lives if they had these six skills and resources:


• Self-awareness: Recognizing their unique talents and accepting their challenges.
• Proactivity: Believing in the power to make changes, taking responsibility for their actions, making decisions and acting upon them. 
• Perseverance: Learning from hardships and not giving up when tasks get hard.
• Goal setting: Making realistic and attainable goals, taking into account strengths and areas of need.
• Support systems: Identifying people who can provide support and actively seeking out support.
• Emotional coping strategies: Recognizing stress triggers for learning difficulties and developing effective means of coping with them.

As parents and educators, we would be well-served to focus on cultivating these social-emotional and behavioral skills so that students with learning disabilities can emerge from their educational experience with success in school and life.

Several researchers offer practical ways to support these resilience skills, using brain-based research. In their book The Yes Brain: How to Cultivate Courage, Curiosity, and Resilience in Your Child , psychotherapists Dan Siegel and Tina Payne Bryson share how parents and educators can help students strengthen their sense of balance, resilience, insight, and empathy. This “balanced brain” helps students with different learning profiles think more flexibly, be more willing to take chances and make mistakes, and manage adversity and big feelings. All of these traits are essential for learners, especially those who have additional frustration due to the challenges of their learning differences. 

Tapping into our students’ potential

At home and in the classroom, educators and parents can easily get tripped up on the language to use with students with learning disabilities. Having a strength-based lens and a few scripts ready can make a big difference in how the students we interact with see themselves as learners.  

We could all take a lesson from my student, Dawn, on positive mindset. When I later shared Dawn’s reaction to being diagnosed with dyslexia with her mother, we both teared up a little. Dawn gave me hope that as educators and parents, we can cultivate that resilience and strength in all the students who come to us with diverse learning needs.

About the Author

Rebecca Branstetter

Rebecca Branstetter, Ph.D. , is a school psychologist, speaker, and author on a mission to help children be the best they can be in school and in life by supporting school psychologists, educators, and parents. She is the co-creator of the “Make It Stick Parenting” course, which provides parents tools to build their child’s social-emotional learning, and creator of the “Peace of Mind Parenting” course. She is also the founder of The Thriving School Psychologist Collective, an online community dedicated to improving mental health and learning supports in public schools. Learn more at  www.thrivingschoolpsych.com .

How to Help Kids Overcome Fear of Failure

Does Neurodiversity Have a Future?

How to Be a Strength-Based Parent

How to Bring SEL to Students with Disabilities

How to Help Students Develop Hope

How Teachers Can Help Students Who Fail in Class to Succeed at Life

‘A Unique Challenge’: What English Learners With Disabilities Need

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Students with disabilities face a gamut of challenges when it comes to accessing high-quality K-12 education, including a shortage of specialized teachers. The nation’s growing English-learner population faces outsized needs as their English-language proficiency scores remain lower than pre-COVID-19-pandemic averages , and immigrant English learners in particular require more trauma-informed instruction.

English learners who also have disabilities face their own intersectional issues, researchers and advocates say. They range from schools locking students out of dual-language programs in favor of English-only special education programs, language barriers between schools and families, and teachers ill-equipped to serve their students’ needs.

“It’s a complex issue. If it was easy, we would have probably figured out a better way forward by now,” said Sarah Salinas, an assistant professor at Minnesota State University, Mankato’s department of special education. “[This group] includes students that are at the intersection potentially of cultural differences, linguistic differences, and disability differences.”

According to federal data from the school year 2020-21 , nearly 14 percent of all students ages 5 through 21 enrolled in public schools were served under IDEA Part B. Of those students, 11.7 percent were English learners.

As this dual-identified population continues to grow, researchers and advocates offer some potential systemic solutions to many of the prevailing challenges these students and their families face.

A lack of access to bilingual education

One of the top concerns researchers and parents alike shared in interviews with Education Week when it comes to English learners with disabilities is a lack of access to bilingual education or dual-language programs.

Parents are encouraged to speak only English with dual-identified students, in part because of a flawed assumption that bilingualism will confuse them or hinder their academic progress or language progress, said Nikkia Borowski, a Ph.D. candidate in inclusive education at Syracuse University who studies access to bilingualism among such students.

She added that there is also the idea that dual-language programs are enrichment programs designed for academically gifted students, locking dual-identified students out in the process.

This preference for English-only instruction for English learners with disabilities plays out in smaller contexts as well, such as speech-generating devices students use that are programmed only in English.

“As a result, the students are missing access to a bilingual identity and missing access to really important cultural aspects as well,” Borowski said.

There is also the matter of how federal policy works for these dual-identified students.

Both the Equal Education Act of 1968 and the Individuals with Disabilities Education Act apply to this student population.

The IDEA, in its 2004 reauthorization, defines a least restrictive environment as the premise of providing services to a student with the greatest access to the general education curriculum, without any explicit mention of what these services look like for multilingual students, Salinas said. The Bilingual Education Act of 1968 focuses on language access for students whose first language is not English without explicit mention of education access for students with disabilities.

So while dual-identified students stand at the intersection of distinct federal policies and laws, the policies and laws are not intersectional themselves.

And even though an English-learner tool kit from the U.S. Department of Education’s office of English-language acquisition reminds educators that a student’s English learner and disability-related educational needs must be met, what ultimately ends up happening is special education and IDEA are consistently prioritized over bilingual education services, Salinas said.

Policymakers have talked about reauthorizing IDEA with more explicit mentions of the needs of dual-identified students, though such a move remains hypothetical, Salinas added.

But even before policies and practices can better align to the linguistic, cultural, and disability-related needs of students, another challenge is at play that presents a quicker potential solution.

The need to reassess communication between schools and families

Navigating IDEA and individual education programs, or IEPs, can already be a daunting task for families. Doing so while English is not the family’s home language is all the more complicated.

Under IDEA, districts must ensure that a student’s parents understand the proceedings of the IEP team meeting, including taking steps such as providing a translator.

In an April survey by the EdWeek Research Center, 65 percent of participating district and school leaders said they offered translation services for special education programming for students whose first language is not English. 37 percent said they did so for all relevant languages spoken by students and families.

Meanwhile, 6 percent of leaders said they do not offer such a service although they have special education students with that need.

Even when considering that 37 percent said their school or districts covered all relevant languages in translation needs, there’s a question of whether the translators involved were trained professionals who understand things like IEPs, or if Spanish-language teachers and bilingual receptionists were called in instead, said Christy Moreno, the chief community advocacy and impact officer of the Missouri-based family-advocacy group Revolucion Educativa.

Moreno, a trained interpreter and translator herself, said offering translation services is the minimum schools and districts must offer families. High-quality translation is key to ensuring families are fully informed of their rights, she added.

“I’ve seen IEPs that are done by Google Translate,” Moreno said.

In addition to investing in proper translation and interpretation, Moreno said educators need to proactively ensure that parents understand how to ask questions about their children’s education. That includes taking into account cultural barriers at play such as stigma within the Latino community over the experiences of students in special education.

Lizdelia Piñón, an emergent bilingual education associate for the Texas-based advocacy nonprofit Intercultural Development Research Association, or IDRA, knows all too well how important it is for families to advocate for their children. Her Spanish-speaking 11-year-old triplets require several accommodations for their autism, cerebral palsy, ADHD, and more.

On several occasions, Piñón said she had to file formal complaints against her local school district to ensure her children’s linguistic and special education needs were met—including pushing back against an attempt to reduce the time her triplets spent with their special education teacher.

However, one systemic issue she sees is a lack of proper training among educators on how to best work with dual-identified students.

The need for better teacher preparation

Piñón worked as a bilingual teacher for about 10 years. She knows that existing bilingual teachers can get their certification in special education as well. But there is a gap of information in both programs, she said, leaving teachers without full context on how to best work with dual-identified students.

“I think that educating English learners with disabilities is a unique challenge for our teachers,” Piñón said.

Overall, there aren’t many teacher-preparation programs that train teachers on what to do in bilingual special education classrooms, said Salinas of Minnesota State University.

Recognizing that knowledge gap, Piñón worked on legislation signed into law in 2021 in Texas to create a bilingual special education certification. However, approval of the new certificate program remains stalled within the state board of education.

Yet, a temporary solution to such knowledge gaps in teacher preparation lies in strategic collaboration among educators, Salinas said.

Such work isn’t always possible between special education and bilingual education teachers on account of tight school schedules and other barriers, she added.

Still, it’s a strategy researchers focusing on English learners say can mitigate not only a lack of bilingual and special education teachers but also address how little training general education teachers have when it comes to working with English learners and special education students overall.

Coverage of students with learning differences and issues of race, opportunity, and equity is supported in part by a grant from the Oak Foundation, at www.oakfnd.org . Education Week retains sole editorial control over the content of this coverage. A version of this article appeared in the May 22, 2024 edition of Education Week as ‘A Unique Challenge’: What English Learners With Disabilities Need

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Frontiers for Young Minds

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The Mind-Bending World of New Brain Technologies

Our amazing brains allow us to do incredible things, yet they remain mysterious in many ways. Researchers have discovered some situations in which the brain can be “fooled”, and these insights into the brain’s inner workings have led to some exciting new technologies, including virtual reality (VR). In addition to its well-known role in gaming and entertainment, VR has some amazing uses in the field of medicine. VR can help patients manage pain, and it can also help surgeons practice delicate procedures and guide them during operations. Other advances called brain-machine interfaces can listen to the brain’s chatter and translate thoughts into commands for computers or even robotic limbs, which could greatly improve the lives of people with certain disabilities. In this article, we will explain how researchers are using findings from cutting-edge brain research to produce exciting new technologies that can heal or even enhance the brain’s functions.

Learning About the Brain by “Tricking” It

Do you like to fool your brain? Many people love things like optical illusions, magic tricks, and other effects that change the way we see reality. Some popular museum exhibits even include distorted rooms that make people appear to grow or shrink as they move around, mirrors that create the illusion of endless corridors, or holographic images that seem to float in space (for examples, see here and here ). Beyond a fun type of entertainment, however, studying the ways the brain can be fooled can help researchers understand how this critical organ works and how they can help certain brain problems.

One famous brain-tricking science experiment is called the rubber hand illusion. In this experiment, which you can watch here , a volunteer sits at a table with one of his arms hidden behind a partition, replaced by a fake-looking rubber arm lying on the table in front of him. The scientist gives the same touch cues (gentle stroking with a paint brush) to both the fake and the real hand, which tricks the volunteer’s brain into perceiving the fake hand is part of his own body. Suddenly, another experimenter hits or stabs the fake hand, causing the volunteer to jump and react as if his hand were threatened. Do you think your brain would be fooled by this experiment? Chances are, it would. But that is not a bad thing, because figuring out how the brain can be “tricked” in this way gives scientists important information, as you will see as you keep reading.

The Amazing Brain

The brain is an amazing, complex organ. It controls everything we do, from moving and breathing to thinking, feeling, and remembering. It is also the source of our creativity, imagination, and intelligence—important characteristics that help make us who we are.

How does one organ do all these things? Neuroscientists are still trying to understand exactly how the brain works, and many mysteries remain unsolved. What is known is that the brain is made up of billions of cells called neurons, which communicate with each other using electrical and chemical signals. Neurons are connected in intricate networks that form the basis for the brain’s functions. We also know that specific areas of the brain are primarily responsible for certain functions, like vision, touch, hearing, movement, and emotions, to name just a few.

All this complexity makes the brain extremely difficult to treat when it gets sick or injured. Problems that originate in the brain can cause pain, memory loss, mood disorders, or movement difficulties. In addition, the brain is extremely delicate and sensitive, so it is not easy for doctors to work on—especially since it is so well-protected by the skull.

Neuroscientists and doctors are working hard to improve their understanding of the brain so that they can develop new ways to help this vital organ heal and improve. One clever way to study something complex is to find its limits. Observing situations in which the brain “fails” or does not work as expected can help scientists to understand how it normally functions. For example, in the rubber hand illusion, the brain “fails” at telling a rubber hand apart from its own hand. Investigating how this happens can teach scientists about the way the brain perceives the body. What is more, learnings from such experiments are leading to cool brain-related technologies that can make life better for both doctors and patients.

Virtual Reality Can Aid Brain Surgery

Have you experienced virtual reality (VR)? Maybe you have played computer games using a VR headset or visited a museum featuring an immersive or interactive VR-based exhibit designed to show you the world through the eyes of a certain artist or allow you to examine objects in a virtual collection . VR is a computer-generated 3D environment that you can experience as if you were there. VR can make you feel like you are in another place or world, where you can explore and interact with objects or even other people. There are many exciting applications of VR technology.

For example, neurosurgery is a type of surgery that involves operating on the brain or the nerves. Due to the complexity and fragility of the brain, neurosurgery requires an extremely high degree of skill and lots of training. Neurosurgeons can use VR to view a 3D map of a patient’s brain, to help them plan for surgery or to guide them during surgery, so that they know where to cut and how to get to the problem area without damaging healthy brain tissue. VR images can be combined with robotics , in what is called robotic-assisted minimally invasive surgery . “Minimally invasive” means that the incisions are smaller than those made in “regular” surgery, and it is usually easier for a patient to recover after the operation. In robotic-assisted minimally invasive surgery, the surgeon does not actually touch the patient—the robot has tiny tools and a camera, and the surgeon controls the robot kind of like a tiny drone, by watching a screen and moving specialized controllers that tell the robot exactly what to do. Although the surgeon might be sitting a meter or more away from the patient, VR allows them to feel like they are in another body, or avatar , actually operating on the patient. The better the VR technology, the more the avatar will feel like the surgeon’s own body as they use the robotic tools, and the more accurate and safer the surgery will be. (To see what robotic-assisted minimally invasive surgery looks like, watch this video or this one .)

Virtual reality can also be used to train doctors as they learn to become surgeons. Practicing delicate surgeries in a VR environment before attempting them on real patients helps surgeons to have more confidence in themselves and improves their surgical skills [ 1 ]. As VR technology continues to improve, more training programs are likely to start using VR to prepare their surgeons for the operating room.

Virtual Reality Can Help Manage Pain

If you have experienced VR, you know that it can be very effective at “tricking” the brain—sometimes you can forget where you really are as you fully imagine yourself in the virtual setting. As the rubber hand illusion illustrated, scientists know that the brain can be tricked into thinking a fake hand is their own hand. Could we similarly trick the brain into not feeling pain? Research tells us that the answer is yes! Neuroscientists are finding that VR can help to reduce or control several kinds of pain, including the pain and anxiety patients ( even kids !) feel during certain medical procedures. VR may also help patients cope with chronic pain , which is pain that often begins with an injury but keeps happening even years after the injury has healed. Managing chronic pain can be extremely challenging, and it is a serious problem because the pain can make it hard for people to do normal activities, like go to work or school, have fun with friends, or even sleep.

How exactly does VR help with pain? For one thing, VR is really good at distracting people from their pain by focusing all of their attention on the VR environment. However, the pain-relief effects of VR go beyond distraction. When a person in a VR setting feels strongly that their avatar is their actual body, they switch to perceiving the avatar’s body as their body - and feel less pain at their real body. Instead, the person’s brain pays attention to the information coming from the avatar. Since the avatar is not feeling pain, the brain “learns” that the body is pain free… and this “lesson” that the brain learned can carry over into reality, when the person stops using VR ( Figure 1 ).

• Figure 1 - (A) If a person has chronic pain in their leg, for example, the brain pays attention to that pain and “learns” that the leg hurts.
• (B) If the person uses VR that makes them feel strongly that their avatar is their own body, the brain stops paying attention to signals from the actual body and pays attention to signals from the avatar instead. Since the avatar does not have leg pain, the brain “learns” that the leg is pain free. (C) The pain-reducing effect of the VR treatment can sometimes last for hours (figure created by carlottacat.com ).

While some people have no pain for hours after a VR “treatment”, most neuroscientists and doctors agree that VR only temporarily reduces pain—it does not cure it. VR can be used in other ways to help people to cope with their pain, for example by immersing them in peaceful scenes that make them feel relaxed, or by guiding them through meditations and breathing exercises [ 2 , 3 ]. One big advantage of some VR-based pain-management technologies is their convenience—if patients have a VR headset, many of these treatments can be done from home.

Controlling Machines With Our Thoughts?

Beyond VR, there are other cutting-edge technologies being developed to help the brain. Have you ever wondered what it would be like to control a robot with your mind? This might sound like science fiction, but it is possible with brain-machine interfaces (BMIs) . BMIs involve technologies that can read brain activity and programs that can translate the brain’s messages into commands for computers or robotic devices. Such devices include neuroprosthetics designed to replace or restore the function of a missing or damaged body part, like a paralyzed arm. To read the brain’s electrical signals, small devices called electrodes are often used. The electrodes are inserted into the brain or attached to the brain’s surface. If a person has a neuroprosthetic arm and wants to move it, the electrodes pick up the brain’s electrical message from the thought “move arm”, and those messages are translated into signals telling the neuroprosthetic arm to move. In addition to helping people with neuroprosthetic limbs move around, eat, and get dressed, for example, BMIs can also be used to help people who have issues hearing, speaking, or even seeing [ 4 ].

A new advance in the field of BMI research involves flexible BMIs. Instead of being made of hard, uncomfortable materials, like the chips inside a laptop or phone, flexible BMIs have soft, bendable electrodes that can adapt to the brain’s shape and movements, making BMIs more effective, accurate, and comfortable [ 5 ]. BMIs are still in the early stages of development, but with further research, they could help make the lives of people with disabilities much easier and help many people to regain their independence. Using BMIs to make normal brain abilities better is another exciting area of research. For example, BMIs could be used to send signals into the brain, to boost certain brain functions like movement control, memory, mood, or attention.

The Future is Mind Blowing!

When it comes to new technologies that could help our brains, the future looks bright ( Figure 2 )! Early research tells us that these technologies hold a lot of promise for everything from training surgeons to improving the lives of people with pain or disabilities. Although more research is needed to make brain-assisting technologies widely usable, scientists are dreaming big. In the future, with the help of these technologies, our brains might have some cool new “superpowers”. We might be able to control machines with our thoughts, virtual reality might help us learn new skills faster and remember more than ever before, and we might find new ways to heal and help the brain in conditions like dementia, depression, or anxiety. Some of these technologies could even help us to understand the mysteries of consciousness. The possibilities for improving health and quality of life are virtually endless—the future of brain tech is going to be mind blowing!

• Figure 2 - Researchers are working on many promising new technologies that could heal or even improve the brain.
• These include techniques to help people manage pain and anxiety, neuroprosthetics that can help people with disabilities regain important functions, and systems to train surgeons to perform delicate surgeries, just to name a few. Together, these advances could help many people live happier, healthier lives (figure created by carlottacat.com ).

Neuroscientist : ↑ A scientist who studies the brain and how it helps us think, feel, and do everything we do.

Virtual Reality : ↑ Advanced technology that puts you inside a computer-generated, 3D world, making it feel like you are in a totally different place.

Robotics : ↑ The science of designing and using robots—machines that can be programmed to perform tasks, often mimicking actions of humans, to help make our lives easier.

Minimally Invasive Surgery : ↑ Surgery that involves using tiny cuts and specialized tools to fix health problems, causing less pain and quicker recovery compared to traditional surgery.

Avatar : ↑ A virtual representation of oneself in a computer-generated environment.

Chronic Pain : ↑ A type of pain that does not go away, lasting for months or even years, and can affect both the body and emotions, making everyday life challenging.

Brain-Machine Interface : ↑ A direct communication pathway between the brain and an external device, allowing a person to control or communicate with computers or machines using only their brain activity.

Neuroprosthetics : ↑ Advanced devices that replace or enhance nerves or brain areas that do not work properly, helping people regain lost abilities like movement or hearing.

Conflict of Interest

SD was employed by SJD Consulting LLC. OB is co-founder and shareholder of Metaphysiks Engineering SA and a member of the board and shareholder of Mindmaze SA.

The remaining author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

Articled inspired by the Sparks! Serendipity Forum at CERN . For more info on this particular topic, see talk by Olad Blanke .

[1] ↑ Paro, M. R., Hersh, D. S., and Bulsara, K. R. 2022. History of virtual reality and augmented reality in neurosurgical training. World Neurosurg. 167:37–43. doi: 10.1016/j.wneu.2022.08.042

[2] ↑ Wynn, P. 2022. Could Virtual Reality Replace Your Next Pain Pill? U.S. News and World Report . Available online at: https://health.usnews.com/health-care/patient-advice/article/virtual-reality-and-pain-management (accessed May 13, 2024).

[3] ↑ McNeil, T. 2023. How Virtual Reality Can Help Relieve Chronic Pain . Tufts Now. Available online at: https://now.tufts.edu/2023/04/10/how-virtual-reality-can-help-relieve-chronic-pain (accessed May 13, 2024).

[4] ↑ Ptito, M., Bleau, M., Djerourou, I., Paré, S., Schneider, F. C., and Chebat, D. R. 2021. Brain-machine interfaces to assist the blind. Front. Hum. Neurosci. 15:638887. doi: 10.3389/fnhum.2021.638887

[5] ↑ Tang, X., Shen, H., Zhao, S., Li, N., and Liu, J. 2023. Flexible brain–computer interfaces. Nat. Electron. 6:109–18. doi: 10.1038/s41928-022-00913-9

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• About Mild TBI and Concussion
• After a Mild TBI or Concussion
• Health Disparities in TBI
• Comparing Head Impacts
• Clinical Guidance
• Mild Traumatic Brain Injury Management Guideline
• Resources for Health Care Providers

Traumatic Brain Injury & Concussion

About Moderate and Severe TBI

Preventing TBI

Symptoms of Mild TBI and Concussion

Where to Get Help

Facts About TBI

For Medical Professionals

Clinical Guidance for Pediatric mTBI

Health Care Provider Resources

CDC Programs

HEADS UP Online Training Courses

National Concussion Surveillance System

Core State Injury Prevention Program (Core SIPP)

A traumatic brain injury, or TBI, is an injury that affects how the brain works. TBI is a major cause of death and disability in the United States.

For Everyone

Health care providers.