Contextualized, Multicomponent Language Instruction: From Theory to Randomized Controlled Trial.

PURPOSE: Clinicians address a wide range of oral language skills when working with school-age students with language and literacy difficulties (LLDs). Therefore, there is a critical need for carefully designed, rigorously tested, multicomponent contextualized language interventions (CLIs) that have a high likelihood of successful implementation and measurable academic impacts. This clinical focus article summarizes the development and testing of a CLI entitled Supporting Knowledge in Language and Literacy (SKILL), which is a supplementary narrative intervention program for elementary school-age children. Our aims are to (a) to review the foundational theoretical models that are the foundation of SKILL; (b) describe the iterative process used to develop the phases, lessons, procedures, materials, and progress monitoring tool; (c) summarize recent findings of the randomized controlled trial that was conducted to test its efficacy; and (d) discuss factors that may contribute to successful implementation of multicomponent language interventions. METHOD: A total of 357 students in Grades 1-4 with LLDs were randomized to a treatment group or to a business-as-usual control group. The treatment group received the SKILL curriculum in small groups during 30-min lessons by trained speech-language pathologists, teachers, and special educators. RESULTS: Students who received SKILL significantly outperformed those who did not on oral and written measures of storytelling and comprehension immediately after treatment and after 5-months at follow-up. Gains were similar among students with different levels of language ability (at-risk, language impaired) and language status (monolingual, bilingual) at pretest. CONCLUSIONS: There is growing support for the use of multicomponent CLIs to bring about educationally relevant outcomes for students with LLDs. The authors present this review of how SKILL was designed, manualized, and rigorously tested by a team of researchers and practitioners with the hope that this approach will serve as a springboard for the development of future multicomponent CLIs that may meaningfully improve communicative and educational outcomes for students with LLDs.

Evaluating the Efficacy of a Narrative Language Intervention for Bilingual Students.

PURPOSE: This study examines the narrative language and reading outcomes of monolingual and bilingual students who received instruction with the Supporting Knowledge in Language and Literacy (SKILL) program, a narrative language intervention. METHOD: The main effects of the SKILL program were evaluated in a randomized controlled trial in which students (N = 355) who were at risk for English language and literacy difficulties were randomized to the SKILL intervention or a business-as-usual instruction. This article reports secondary analyses examining the efficacy of SKILL for bilingual (n = 148) and monolingual (n = 207) students who completed measures of oral and written narrative language and reading comprehension in English. RESULTS: Moderation results showed that the effects of SKILL did not differ for monolinguals and bilinguals across most narrative language measures and did not vary for monolinguals or bilinguals based on their pre-intervention language performance. CONCLUSION: These findings that suggest a language-based approach to improving narrative production and comprehension yielded similar results for monolinguals and bilinguals and that neither monolinguals nor bilinguals in this study needed to meet a certain threshold of English language proficiency to benefit from the intervention.

Experientially based narrative instruction: A neurocognitive perspective.

Background: The purpose of this study was to evaluate the feasibility of a personal narrative intervention based on neurocognitive principles and experientially based learning for improving the personal narrative language abilities of a school-age child with Down's syndrome. Method: A single-case design using contemporary statistical techniques was employed to complete this study. The participant was 8 years 8 months at the time of the study and he participated in a 14-week personal narrative intervention. Personal narrative samples were collected at the beginning of each intervention session prior to instruction. Narrative samples were scored for narrative quality, language productivity, and lexical diversity. Results: As a result of the intervention, the participant demonstrated moderate-significant increases in narrative abilities for narrative quality, language productivity, and lexical diversity. Conclusions: The use of a personal narrative based on neurocognitive principles and experientially based learning may be feasible for improving the personal narrative language abilities of school-age children with Down's syndrome.

Automated Progress-Monitoring for Literate Language Use in Narrative Assessment (LLUNA).

Language sample analysis (LSA) is an important practice for providing a culturally sensitive and accurate assessment of a child's language abilities. A child's usage of literate language devices in narrative samples has been shown to be a critical target for evaluation. While automated scoring systems have begun to appear in the field, no such system exists for conducting progress-monitoring on literate language usage within narratives. The current study aimed to develop a hard-coded scoring system called the Literate Language Use in Narrative Assessment (LLUNA), to automatically evaluate six aspects of literate language in non-coded narrative transcripts. LLUNA was designed to individually score six literate language elements (e.g., coordinating and subordinating conjunctions, meta-linguistic and meta-cognitive verbs, adverbs, and elaborated noun phrases). The interrater reliability of LLUNA with an expert scorer, as well as its' reliability compared to certified undergraduate scorers was calculated using a quadratic weighted kappa (K qw ). Results indicated that LLUNA met strong levels of interrater reliability with an expert scorer on all six elements. LLUNA also surpassed the reliability levels of certified, but non-expert scorers on four of the six elements and came close to matching reliability levels on the remaining two. LLUNA shows promise as means for automating the scoring of literate language in LSA and narrative samples for the purpose of assessment and progress-monitoring.

An Evaluation of Expedited Transcription Methods for School-Age Children's Narrative Language: Automatic Speech Recognition and Real-Time Transcription.

Purpose This study examined the accuracy and potential clinical utility of two expedited transcription methods for narrative language samples elicited from school-age children (7;5-11;10 [years;months]) with developmental language disorder. Transcription methods included real-time transcription produced by speech-language pathologists (SLPs) and trained transcribers (TTs) as well as Google Cloud Speech automatic speech recognition. Method The accuracy of each transcription method was evaluated against a gold-standard reference corpus. Clinical utility was examined by determining the reliability of scores calculated from the transcripts produced by each method on several language sample analysis (LSA) measures. Participants included seven certified SLPs and seven TTs. Each participant was asked to produce a set of six transcripts in real time, out of a total 42 language samples. The same 42 samples were transcribed using Google Cloud Speech. Transcription accuracy was evaluated through word error rate. Reliability of LSA scores was determined using correlation analysis. Results Results indicated that Google Cloud Speech was significantly more accurate than real-time transcription in transcribing narrative samples and was not impacted by speech rate of the narrator. In contrast, SLP and TT transcription accuracy decreased as a function of increasing speech rate. LSA metrics generated from Google Cloud Speech transcripts were also more reliably calculated. Conclusions Automatic speech recognition showed greater accuracy and clinical utility as an expedited transcription method than real-time transcription. Though there is room for improvement in the accuracy of speech recognition for the purpose of clinical transcription, it produced highly reliable scores on several commonly used LSA metrics. Supplemental Material https://doi.org/10.23641/asha.15167355.