September 2021: The Stability of Learning Disabilities Among Emergent Bilingual Children

Overview and Purpose

Children with learning disabilities (LD) are at risk for poor academic and employment outcomes. The process to identify LD is particularly difficult for children learning a second language (in this case, English learners, or EL). For example, some studies find EL to be over-represented in special education (Artiles, Rueda, Salazar, & Higareda, 2005), while others find them to be under-represented (Morgan & Farkas, 2016). The present study sought to use both cognitive (vocabulary, “fluid” intelligence, teacher ratings of behavioral problems) and achievement (reading and math) measures over time, in two languages (English and Spanish), to identify groups or classes of individuals who have similar patterns of performance in hopes of clarifying issues relevant to LD identification.

The purpose of this study by Swanson, Arizmendi, and Li (2020) was to determine whether children at risk for academic difficulties within an EL sample reflect a discrete group/class, whether membership in that group/class is maintained over time, and how additional cognitive abilities (naming speed, executive skills, inhibition, and working memory [WM], a cognitive skill that allows the brain to briefly hold new information for a short period of time) might help predict how individuals get into and stay in the LD group/class. Swanson and colleagues (2020) addressed three primary research questions:

1. Can a group/class of children at risk for LD be found among EL?

The authors hypothesized that this would be possible. They expected to find a group/class of EL children at risk for LD, and two groups/classes who are not at risk—one with proficient language skills in English and Spanish (balanced bilinguals) and another with higher English than Spanish skills (unbalanced bilinguals) given that their educational experience is in English.

2. Does membership in those groups/classes change over time?

The authors anticipated that some students would be identified as having “late-emerging” difficulties, meaning that they would not have been identified as at-risk at the initial assessment point but would be classified in the at-risk group at a later timepoint.

3. Do specific cognitive measures predict group/class membership?

The authors proposed that executive cognitive skills (e.g., WM, inhibition) and cognitive skills related to phonological storage would uniquely predict membership in the at-risk group.

Study Methods

The study evaluated 267 students in Grades 1, 2, and 3, and followed each of them for a year. Students were identified as EL by their school. There were three kinds of statistics used. First, to establish groups/classes, Swanson et al. used latent class analysis, where subgroups of people within a population are clustered together on the basis of responses or scores across a number of variables. Group membership is not obvious from any given score, which is why they are called latent, or unobservable, classes. Second, they used a related technique, latent transitional analysis, to see how group membership changes over time. Finally, they used a technique called logistic regression to evaluate whether cognitive processes, like executive skills, predict who belongs or will belong to a particular latent class.

Key Findings

1. Can a group/class of children at risk for LD be identified among EL?

The results indicate that such a group was found. This group was characterized by average intelligence and attention but difficulties in both English and Spanish measures of reading and math. At the first timepoint, 20% of the sample fell into the at-risk group. The authors also found support for a not-at-risk group that was “balanced” (average in both languages, cognitively and academically) and another not-at-risk group that was “unbalanced” (with higher English than Spanish skills), as they expected. They also found a fourth (unexpected) group, with very low Spanish skills, that they called “English-dominant.”

2. Does membership in a latent class of at-risk EL children change over time?

After a year, 25% of the sample fit the latent at-risk class, which was an increase in the number who qualified. A surprise was that only 46% of the unbalanced students stayed in their group after a year, with the remaining 54% transitioning to the English-dominant group. This instability in group membership over time was the most pronounced among students transitioning from Grades 3 to 4, where 94% transitioned to the English-dominant group; this may be related to a transition in the language of instruction (Spanish to English) in the upper grades. In contrast, most students in the balanced (87%), English-dominant (95%), and at-risk (100%) groups stayed in their groups. Overall, 13% of the students in the balanced group transitioned to the at-risk group, and this transition was more likely for the older students (Grades 2 to 3, 24%; Grades 3 to 4, 15%) compared to the youngest (Grades 1 to 2, 9%).

3. Do specific cognitive measures predict latent class membership?

Naming speed and the executive component of WM significantly predicted latent class membership at both Year 1 and Year 2.

Conclusions, Implications, and Future Directions

Findings from this study demonstrated that latent classifications of children, including those at risk for reading and math difficulties, may be identified among a sample of EL children by using Spanish and English cognitive and achievement measures. Students in the at-risk class shared a number of weaknesses across reading and math measures, in both English and Spanish. Thus, it is important that educators recognize that there may be struggling children among their EL students whose academic difficulties are not merely due to their EL status and that these students will require more intensive interventions. From a practical perspective, looking for similar patterns of performance across a range of measures such as these can serve as indicators of who is likely to struggle. From a research perspective, the latent statistical techniques were found to be effective in identifying EL with potential LD.

The stability of membership in the at-risk class over time highlights the challenges associated with improving outcomes for EL with academic problems, which emphasizes the need to both identify and intervene with such children early.

Furthermore, finding that students with late-emerging learning difficulties (as identified by transitioning into the at-risk group in Year 2) were more likely to be either leaving or entering the third grade points to a possible critical period at the third-grade level. This suggests that EL students, even those who are not yet struggling in reading or math, should be carefully monitored for emerging academic problems in early elementary school. Specifically, Grade 1 teachers could encourage Grade 2 and 3 teachers to monitor and re-evaluate EL children even when they were not identified as having reading or math difficulties in Grade 1.

Findings for the role of WM in predicting latent class membership are consistent with prior work linking WM and achievement in both reading and math within and across language systems in EL children. This is consistent with multiple deficit models of LD, which posit that shared risk factors such as WM contribute to problems in reading and math (McGrath et al., 2011).

Educational researchers are increasingly recognizing the importance of better understanding why EL are at higher risk for poor outcomes and how these problems might be remedied by better identification and intervention techniques. This study from Swanson and colleagues (2020) is related to ongoing research conducted by the Texas Center for Learning Disabilities, as our current project is examining academic outcomes among EL in middle school. Specifically, a current project led by Kelly Macdonald aims to evaluate the role of language-related variables (English proficiency, Spanish proficiency, and English phonological skills) in math achievement as well as the overlap between reading and math skills. As with the Swanson et al. (2020) study, findings from this work have the potential to inform identification and intervention approaches for EL with LD.

For Further Reading

Artiles, A. J., Rueda, R., Salazar, J. J., & Higareda, I. (2005). Within-group diversity in minority disproportionate representation: English language learners in urban school districts. Exceptional Children, 71(3), 283–300. https://doi.org/10.1177/001440290507100305

Baddeley, A. D., & Logie, R. H. (1999). The multiple-component model. In A. Miyake & P. Shah (Eds.), Models of working memory: Mechanisms of active maintenance and executive control (pp. 28–61). Cambridge University Press. https://doi.org/10.1017/CBO9781139174909.005

McGrath, L. M., Pennington, B. F., Shanahan, M. A., Santerre‐Lemmon, L. E., Barnard, H. D., Willcutt, E. G., DeFries, J. C., & Olson, R. K. (2011). A multiple deficit model of reading disability and attention‐deficit/hyperactivity disorder: Searching for shared cognitive deficits. Journal of Child Psychology and Psychiatry, 52(5), 547–557. https://doi.org/10.1111/j.1469-7610.2010.02346.x

Morgan, P. L., & Farkas, G. (2016). Are we helping all the children that we are supposed to be helping? Educational Researcher, 45(3), 226–228. https://doi.org/10.3102/0013189X16644607

Swanson, H. L., Kong, J., & Petcu, S. (2018). Math difficulties and working memory growth in English language learner children: Does bilingual proficiency play a significant role? Language, Speech, and Hearing Services in Schools, 49(3), 379–394. https://doi.org/10.1044/2018_LSHSS-17-0098

Swanson, H. L., Orosco, M. J., & Lussier, C. M. (2015). Growth in literacy, cognition, and working memory in English language learners. Journal of Experimental Child Psychology, 132, 155–188. https://doi.org/10.1016/j.jecp.2015.01.001