Improving STEM Outcomes for Young Children With Language Learning Disabilities Via Telehealth

Improving STEM Outcomes for Young Children With Language Learning Disabilities by Intervening at the Intersection of Language and Scientific Thought: A Telehealth RCT

In this study the investigators focus on a subset of at-risk students who find the language of science to be a barrier to the learning of science. These are the nearly 3 million children in the U.S. who have a learning disability called specific language impairment (SLI). Children with SLI present with deficits in spoken grammar and vocabulary and they are 3.9 to 8.1 times more likely to have reading deficits than children in the general population. Specific Aim #1: To determine whether science-relevant language intervention enhances the learning of science concepts in young children who have SLI. Specific Aim #2: To determine whether science-relevant language intervention facilitates generalization of science concepts and practices in young children who have SLI

63 4- to 7-year-olds who have not yet begun 1st grade, who are monolingual speakers of English, and who have SLI will participate. Note that the investigators may recruit extra participants to allow for attrition. The investigators will adopt a Randomized Controlled Trial design, randomly assigning participants into three intervention conditions: science only (the control arm), science + vocabulary supports, and science + grammar supports. Pre- and post-measures will reveal the extent of learning in each condition and comparisons between conditions will reveal whether the grammar and vocabulary supports improved learning. The hypothesis is that the language and learning of science are integrally related. Therefore, the investigators will use evidenced-based language interventions to improve the children's science-relevant language skills, with the prediction that this will cascade into changes in the acquisition of science concepts and practices: Children in the science + language intervention conditions will show greater gains in taught science concepts across the 6-week intervention period than children in the control arm. Children in the science + language intervention conditions will show greater gains from pretest to posttest on measures of generalized science concepts and practice than children in the control arm. Children who demonstrate the greatest improvement in the use of the language targets will also demonstrate the greatest improvements in taught concepts, generalized concepts, and generalized practice knowledge. Children will benefit from language supports directed at vocabulary as well as those directed at grammar, but these supports may differently benefit the science learning process. First the investigators will document that the language supported interventions resulted in improved language abilities by comparing performance on probes of grammar and vocabulary at posttest to pretest performance. The investigators expect significant improvements in vocabulary knowledge for the vocabulary intervention condition as compared to the other two conditions, and significant improvements in use of complement clauses for the grammar intervention condition as compared to the other two conditions. Next, to be tested are the predictions associated with the specific aims via a series of logistic mixed models. Mixed models are appropriate for designs with unbalanced cell sizes due to missing data (due to non-response and dropout). There will be one model for targeted science concept outcomes with condition (control arm, science + vocabulary, science + grammar) and time as independent variables (Predictions 1 and 4). There will also be one model each for generalized concepts and generalized practice outcomes with condition (control arm, science + vocabulary, science + grammar) and time (pretest and posttest) as independent variables (Predictions 2 and 4). Within-subject correlation will be accounted for with random subject intercepts. Additional random effects (including random item intercepts or random condition slopes by item) will be determined by selecting the model with the best model fit (lowest AIC value). In each of the models, it is further expected that amount of improvement in grammar and vocabulary are mediators between the outcome and the other factors (Prediction 3). To assess this prediction, performance on the language probes will be considered as covariates. It is expected that performance on the language probes after instruction will be a significant predictor of science learning, and that including performance on the language probes as a covariate will reduce or eliminate the effect of condition because language performance will be the main factor predicting science performance.

Language Therapy, Randomized Controlled Trial, Specific Language Impairment, Developmental Language Disorder

Although it is impossible to mask the assessors of the proximal outcome measures, those assessing the distal outcomes can be masked. Specifically, the people scoring the science exposition (a distal measure of generalized scientific content) will be masked to the treatment arm assignments.

Control: In all conditions, the examiners will teach science using the Full Option Science System Next Generation Edition (FOSS, 2015, https://www.fossweb.com/) curriculum that involves 1) Prediction, 2) Experiment, 3) a visual Journal/Reflection, and 4) a pre-recorded reading centered around a given theme such as sound. A research speech-language pathologist will provide two 30-minute interactive science lessons per week for six weeks to children with language learning challenges recruited nationwide. Children will participate in groups of three. Families will log on five additional times during each week to view the science book reading. In the control condition, children will receive these science lessons but no language intervention. Therefore, this intervention constitutes a nonintervention.

Grammar: In the science + grammar condition, focused stimulation plus explicit instruction will be employed. Focused stimulation an intervention commonly used to target expressive language, will be used to treat complement clauses during the FOSS activities.. The active ingredients are models (30) and recasts (5 per child) of the target structure (e.g., "You measured how long the ramp is"). Recasts occur when an examiner responds to a child's naturally occurring utterance by expanding or extending the child's utterance to include a target grammatical structure. Focused stimulation will be supplemented with explicit instruction using choral production and visual supports (3x per lesson) and a definition of the meaning of the structure (1 per lesson).

Vocabulary: This arm will provide Robust Vocabulary Instruction, an explicit approach that emphasizes multiple and rich encounters in authentic contexts to promote depth of semantic knowledge of 12 words that pertain to scientific practices applicable to the FOSS lessons. The words are: compare, diagram, evidence, explanation, hypothesis, materials, model, multiple, pattern, problem, scientist, search. Two words will be targets in each session. The examiners will ensure that for each target word per session there will be at least one definition model and 3 other models directed to the triad of participants and at least 2 elicitations per child. The recorded books also include 6 additional exposures to the words, for a cumulative exposure of 12.

To measure outcomes relevant to aim 1, the examiner will administer three 10-item, proximal concept assessments one for each science unit at the end of all instruction. These will be adapted from the FOSS I-check probes (e.g., What would you see in the night sky? Stars? Sun? Moon?). From these, the investigators will be able to determine whether the children learn more of the target science concepts in the language-supported conditions than in the control arm.

The examiner will administer two proximal, investigator-created probes for language: 1) 20 complement clause elicitations; and 2) 20 vocabulary items (12 that were taught and 8 foils) will be tested for receptive knowledge in a 3-alternative -forced choice format.

These two assessments will be administered to children in all three conditions within three weeks before intervention and again within three weeks after the intervention is withdrawn.

To measure outcomes relevant to aim 2, the examiner will administer a distal measure of generalized science knowledge. For science concepts, the distal measure is science exposition retell task adapted from Panayota Mantzicopoulos & Helen Patrick (2010) "The Seesaw Is a Machine That Goes Up and Down": Young Children's Narrative Responses to Science-Related Informational Text, EARLY EDUCATION AND DEVELOPMENT, 21:3, 412-444, DOI:10.1080/10409281003701994 The child listens to a series of one-paragraph-long descriptions of a science or engineering construct (e.g., levers) and retells each to the examiner. The retells are scored for the scientific themes and linguistic content included. This task will be administered to children in all three conditions within three weeks before intervention and again within three weeks after withdrawing the language intervention.

Inclusion Criteria: Age between 4 and 7 years Not yet begun first grade Monolingual speaker of English Has SLI confirmed by a standard score of below 85 on the Diagnostic Evaluation of Language Variation-Norm Referenced DELV-NR (Seymour, Roeper, De Villiers, & De Villiers, 2005) Differential Abilities Scale-II (Elliott, 2007) t score greater than or equal to 35 Can produce simple sentences Performs with less than 40% accuracy on expressive probes of complement clauses prior to study onset Exclusion Criteria: Other diagnosed developmental disorders (e.g., autism, Down syndrome) via parent report

The investigators will share de-identified spreadsheets of data (scores on all measures) along with a data dictionary in Open Science Framework.

The investigators will share all materials within six months after publishing the outcomes of the RCT.