Journal of Learning Disabilities最新文献

We examined how generalized and mathematics-specific language skills predicted the word-problem performance of students with mathematics difficulty. Participants included 325 third-grade students in the southwestern United States who performed at or below the 25th percentile on a word-problem measure. We assessed generalized language skills in word reading, passage comprehension, and vocabulary knowledge. In addition, we measured mathematics-specific vocabulary knowledge. To explore variation within the mathematics-difficulty population, we utilized unconditional quantile regression to determine how each of these skill sets predicted word-problem performance when controlling for computation and emergent bilingual status. Results revealed that mathematics-vocabulary knowledge significantly predicted word-problem performance at all but two quantiles (p < .001), with strongest predictive relations at the highest quantiles. Passage comprehension had an overall significant relation to word-problem performance (p < .05) that was also reflected in multiple quantiles. Neither word-reading accuracy nor generalized-vocabulary knowledge demonstrated a significant predictive relation to word-problem performance. Given the consistent relation between mathematics-vocabulary knowledge and word-problem performance across quantiles, researchers and practitioners should prioritize evidence-based mathematics-vocabulary instruction to support students' word-problem-solving skills.

Youth with attention-deficit/hyperactivity disorder (ADHD) often exhibit impairments in mathematics, but long-term math development into adulthood, particularly in females, is underexplored. We characterized trajectories of math achievement in girls with ADHD and an age- and ethnicity-matched comparison sample from childhood through early adulthood across four waves and examined childhood cognitive predictors (global executive functioning, working memory, processing speed) of trajectories. The ethnically and socioeconomically diverse sample comprised 140 girls with carefully diagnosed ADHD and 88 neurotypicals, ages 6 to 12 years at baseline. Using latent growth curve models, we examined predictors of 16-year math achievement trajectories. In both the ADHD and neurotypical groups, scores declined over time; rates of change did not differ significantly. Yet in the ADHD sample, math difficulties (defined as scores at least 1 SD below the national average) increased notably over time, with many such difficulties emerging after childhood. By adulthood, nearly half of women with ADHD exhibited clear math difficulties. Worse baseline global executive functioning predicted slower math growth over time. Girls with ADHD may benefit from math supports before concerns emerge or worsen after childhood. Additional research on preventive interventions for math difficulties, including investigation of executive functioning, is warranted.

School context can shape relative intervention response in myriad ways due to factors, such as instructional quality, resource allocation, peer effects, and correlations between the school context and characteristics of enrolled students (e.g., higher-poverty students attending higher-poverty schools). In the current study, we used data from 16,000 U.S. Grade 3 students in a community-based supplemental reading intervention program to investigate the degree to which school context factors (percentage eligible for free/reduced-price lunch [FRPL], school-level achievement) relate to the differences in triannual reading fluency growth rates between students actively receiving supplemental intervention (active recipients) and those that formerly received intervention (and therefore only received general class instruction at this time; former recipients). Using Bayesian multilevel modeling, our findings indicate that school-level FRPL eligibility played a more prominent factor in growth rate differences between these two groups than school-level reading achievement. However, school-level reading achievement was much more strongly related to reading fluency differences between active and former intervention recipients at the beginning of the school year (when controlling for FRPL). Implications for investigating school-level heterogeneity in intervention response and sustainability are discussed.

Children with developmental dyslexia (DD) display partially preserved morphology skills which they rely upon for reading and spelling. Therefore, we conducted explicit and intensive training of derivational morphology in French and Swiss individuals with DD, ages 9 to 14 years, in order to assess its effect on: morphological awareness, reading (speed and accuracy), and spelling. Our pre-posttest design included a group trained in derivational morphology and a group of children who continued their business-as-usual rehabilitation program with their speech-language therapist. Results showed effects on morphological awareness and on the spelling of complex words, with a large between-group effect size for trained items and a large to moderate effect size for untrained items. All these gains tended to be maintained over time on the delayed posttest, 2 months later. For reading, the results were more contrasted, with large between-group effect sizes for accuracy and speed for trained items, reducing to a small effect for accuracy on the delayed posttest. For untrained items, small effects were observed on accuracy (at both posttests) but not on speed. These results are very promising and argue in favor of using derivational morphology as a medium to improve literacy skills in French-speaking children and adolescents with DD.

Although data-based individualization (DBI) has positive effects on learning outcomes for students with learning difficulties, this framework can be difficult for teachers to implement due to its complexity and contextual barriers. The first aim of this synthesis was to investigate the effects of ongoing professional development (PD) support for DBI on teachers' DBI knowledge, skills, beliefs, and fidelity and the achievement of preschool to Grade 12 students with academic difficulties. The second aim was to report on characteristics of this support and explore whether features were associated with effects. We identified 26 studies, 16 and 22 of which examined teacher and student outcomes, respectively. Meta-analyses indicated that the weighted mean effect size for DBI with ongoing support for teachers was g = 0.86 (95% confidence interval [CI] = [0.43, 1.28], p < .001, I² = 83.74%, k = 46) and g = 0.31 for students (95% CI = [0.19, 0.42], p < .001, I² = 61.38%, k = 103). We did not identify moderators of treatment effects. However, subset effects were descriptively larger for ongoing support that targeted data-based instructional changes or included collaborative problem-solving. Researchers may improve future DBI PD by focusing on support for teachers' instructional changes, describing support practices in greater detail, and advancing technological supports.

Data-based instruction (DBI) is a process in which teachers use progress data to make ongoing instructional decisions for students with learning disabilities. Curriculum-based measurement (CBM) is a common form of progress monitoring, and CBM data are placed on a graph to guide decision-making. Despite the central role that graph interpretation plays in the successful implementation of DBI, relatively little attention has been devoted to investigating this skill among special education teachers. In the present study, we examined the data decisions of 32 U.S. pre-service special education teachers (29 females and 3 males). Participants viewed data presented sequentially on CBM progress graphs and used a think-aloud procedure to explain their reasoning each time they indicated they would make instructional changes. We also asked participants to make the same type of decisions in response to static CBM progress graphs depicting 10 weeks of data. Overall, there was inconsistency in pre-service teachers' responses related to when or why they would make an instructional change. Decisions were often influenced by graph-related features, such as variability in the data. Furthermore, responses suggested misunderstandings that led to premature instructional change decisions and reliance on individual data points.

In a multiyear, multisite, randomized control trial, we examined the effects of comprehensive professional development designed to support teachers' data-based instruction (DBI) for students with intensive early writing needs. Teachers (N = 154; primarily special educators or intervention specialists) were assigned randomly to a treatment group (n = 76), in which they received tools, learning, and coaching to support their DBI implementation over 20 weeks, or to a control group (n = 78). Students either received DBI in early writing (n = 155) from treatment teachers or their usual writing instruction (n = 154) from control teachers. Treatment teachers outperformed controls on measures of DBI knowledge and skills (d = 1.57) and self-efficacy for writing instruction (d = .94), and treatment students outperformed controls on proximal and distal writing outcomes (ds = .14-.29). Student characteristics (grade, special education status, English learner status, and race/ethnicity) did not moderate intervention effects. We discuss findings in terms of the importance of supporting students with intensive learning needs, the efficacy and feasibility of implementing DBI-TLC, and implications for pre- and in-service teacher training and support.