Effects of Writing Interventions on the Level and Trend of Total Words Written: A Meta-Analysis

We estimated effects of writing interventions on the level and trend of writing fluency—rate of total words written over time—by students with and without disabilities. Using mixed-effects regression and an information-theoretic ranking of competing models, we synthesized results of 42 single-case experimental design studies with a total of 205 students. A variety of acquisition and fluency interventions were used across studies, such as self-regulated strategy development and timed practice with performance feedback. We found acquisition and fluency interventions produced an increase in level and a gradual increase in trend of total words written per minute. Students receiving fluency intervention tended to have higher levels of performance across experimental phases (i.e., baseline, intervention, and postintervention), but students receiving acquisition had steeper upward trends. In addition, we found higher levels of total words written per minute on writing tasks with brief time limits (i.e., ≤10 min) and in the writing of older students.

Accuracy of Automated Written Expression Curriculum-Based Measurement Scoring

We examined the extent to which automated written expression curriculum-based measurement (aWE-CBM) can be accurately used to computer score student writing samples for screening and progress monitoring. Students ( n = 174) with learning difficulties in Grades 1 to 12 who received 1:1 academic tutoring through a community-based organization completed narrative writing samples in the fall and spring across two academic years. The samples were evaluated using four automated and hand-calculated WE-CBM scoring metrics. Results indicated automated and hand-calculated scores were highly correlated at all four timepoints for counts of total words written ( rs = 1.00), words spelled correctly ( rs = .99–1.00), correct word sequences (CWS; rs = .96–.97), and correct minus incorrect word sequences (CIWS; rs = .86–.92). For CWS and CIWS, however, automated scores systematically overestimated hand-calculated scores, with an unacceptable amount of error for CIWS for some types of decisions. These findings provide preliminary evidence that aWE-CBM can be used to efficiently score narrative writing samples, potentially improving the feasibility of implementing multi-tiered systems of support in which the written expression skills of large numbers of students are screened and monitored.

Accuracy of Automated Written Expression Curriculum-based Measurement Scoring

We examined the extent to which automated written expression curriculum-based measurement (aWE-CBM) can be accurately used to computer score student writing samples for screening and progress monitoring. Students (n = 174) with learning difficulties in Grades 1–12 who received 1:1 academic tutoring through a community-based organization completed narrative writing samples in the fall and spring across two academic years. The samples were evaluated using four automated and hand-calculated WE-CBM scoring metrics. Results indicated automated and hand-calculated scores were highly correlated at all four timepoints for counts of total words written (rs = 1.00), words spelled correctly (rs = .99 – 1.00), correct word sequences (CWS; rs = .96 – .97), and correct minus incorrect word sequences (CIWS; rs = .86 – .92). For CWS and CIWS, however, automated scores systematically overestimated hand-calculated scores, with an unacceptable amount of error for CIWS for some types of decisions. These findings provide preliminary evidence that aWE-CBM can be used to efficiently score narrative writing samples, potentially improving the feasibility of implementing multi-tiered systems of support in which the written expression skills of large numbers of students are screened and monitored.

The Effects of Different Types of Environmental Noise on Academic Performance and Perceived Task Difficulty in Adolescents With ADHD

Objective: To examine the effects of environmental noises (speech and white noise) relative to a no noise control condition on the performance and difficulty ratings of youth with ADHD ( N = 52) on academic tasks. Method: Reading performance was measured by an oral retell (reading accuracy) and the time spent reading. Writing performance was measured through the proportion of correct writing sequences (writing accuracy) and the total words written on an essay. Results: Participants in the white noise condition took less time to read the passage and wrote more words on the essay compared with participants in the other conditions, though white noise did not improve academic accuracy. The participants in the babble condition rated the tasks as most difficult. Conclusion: Although white noise appears to improve reading time and writing fluency, the findings suggest that white noise does not improve performance accuracy. Educational implications are discussed.