Journal of Learning Disabilities, 46(4), 304. doi:10.1177/0022219411421810
Reading this journal article, I was struck by the lack of empirical studies on gifted with learning disability (G/LD) population in the research literature and the wide variabilities in inclusion criteria for different studies. There was only about 5% of written G/LD articles that report empirical data.
I have presented Table 1 above to visually display the IQ and achievement levels under various conditions based on the empirical research investigated by Lovett and Sparks (2013). The pink colored tab represents the conditions with the highest number of identified G/LD and the red tab represents the smallest number of identified G/LD. I suspect that the IQ-achievement level discrepancies shown in most studies using relative discrepancies would not flag such students in school as having learning disability (LD), their achievement is still average or less than 1 standard deviation (SD) below average. Making the case that such students require special education (SPED) services may be challenging.
An empirical study published by Barnard-Brak et. al. (2015) used a sample of 13,176 special education elementary children to investigate how many of those pre-identified LD students fit gifted criteria and how many were actually participating in gifted programming. Their finding indicated that only 11.1% of the 9.1% of sample who qualified as gifted (scoring in top 10% of WJ-III) were actually participating in gifted programming. Their findings indicate an under identification of G/LD population, even within the population that had be subject to stringent SPED admissions criteria. I believe that aiming to increase and capture gifted students that already qualify for SPED services is viable, feasible and morally the right thing to do as research continues on proper identification, characteristics and programming. This approach allows for educators and researchers to continue making positive impacts to service such students as more research can be conducted using pre-identified SPED populations.
The second area that caught my eye in this article is the authors’ presentation of the weighted mean test scores used in multiple studies. I have replicated it in for easy reference below in Table 2. The authors combined different versions of the Wechsler tests ( WAIS and WISC) to create the weighted mean scores. My understanding is that between different versions of the Wechsler tests, sub test changes may affect scores for different populations. For example, Kuehnel, Castro, and Furey (2019) compared the VCI in WISC IV and WISC V in 48 childrens with ASD. The WISC IV included three subtests (Similarities, Vocabulary, and Comprehension) to create the composite VCI, while comprehension subtest was dropped in calculation of VCI in WISC V. Comprehension test is the most difficult for ASD population as “success on the Comprehension subtest requires linguistic sophistication that many individuals with ASD do not possess, given commonly occurring core expressive and receptive language deficits (Kwok, Brown, Smyth, & Cardy, 2015), as well as pragmatic language deficits (Whyte & Nelson, 2015)” (Kuehnel, Castro, & Furey, 2019). The researchers found statistically significant score increase in the composite VCI in WISC V. Findings as such signals that careful consideration is required when making the decision to include/exclude studies in aggregate studies investigations.
|Score||# studies||Total N||Weighted M|
|WJ reading Cluster||5||442||95.8|
|WJ Mathematics Cluster||5||442||111.1|
|WJ written language Cluster||5||440||93|
Creating weighted mean scores for the various WJ clusters without grouping students into various groups that demonstrate LD in reading, math, written language or any combination of the three abilities, creates potential “masking effect” on the resulting scores. “Masking refers to the principle that many gifted students with learning disabilities have patterns of strengths and weaknesses that make them appear to have average abilities and achievement” (McCoach, Kehle, Bray, & Siegle, 2001). I would use this information to advocate for future studies to investigate specific G/LD types, or types of processing/skills (e.g advanced reasoning, basic processing skills, higher order thinking) in order to minimize masking effects. Ottone-Cross Et al (2017) found that GLD had similar higher order processing demands as the GT group but presented with significant weakness in decoding and math computation (lower-level processing demands) similar to SLD samples in their study.
Last but not least, LD label within the G/LD context having a very different meaning then the label LD as academic impairment in a “normative absolute sense” (Lovett & Sparks, 2011, p.313). This way of viewing LD within the G/LD context emphasizes the deficit model view of education, whereby you receive support only AFTER you have failed. I would like to propose a modified view of looking at the G/LD inclusion criterias. If the student is working at the right academic challenge level, can the child be successful without support? The complexity of G/LD is that LD doesn’t show when the academic level is way below the abilities of the child. On other hand, when the academic challenge is raised to the right level, such children often can not perform up to their potential as they no longer can use their strengths to compensate for the weaknesses. Currently, based on my still growing knowledge in 2e area, I feel that comprehensive neuropsychological evaluation can highlight potential issues for the child as school work progressing towards the ability level of the student.
Barnard-Brak, L., Johnsen, S. K., Pond Hannig, A., & Wei, T. (2015). The incidence of potentially gifted students within a special education population. Roeper Review, 37(2), 74. doi:10.1080/02783193.2015.1008661
Kuehnel, C. A., Castro, R., & Furey, W. M. (2019). A comparison of WISC-IV and WISC-V verbal comprehension index scores for children with autism spectrum disorder. The Clinical Neuropsychologist, 33(6), 1127. doi:10.1080/13854046.2018.1503721
McCoach, D. B., Kehle, T. J., Bray, M. A., & Siegle, D. (2001). Best practices in the identification of gifted students with learning disabilities. Psychology in the Schools, 38(5), 403. doi:10.1002/pits.1029
Ottone-Cross, K., Dulong-Langley, S., Root, M. M., Gelbar, N., Bray, M. A., Luria, S. R., . . . Pan, X. (2017). Beyond the mask: Analysis of error patterns on the KTEA-3 for students with giftedness and learning disabilities. Journal of Psychoeducational Assessment, 35(1-2), 74. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&db=eric&AN=EJ1130079&site=ehost-live&scope=site