University of Delaware
Newark, Delaware, 19716, United States
NCT Number: NCT04775199
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
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Notify Me4 year–7 year
All sexes
Interventional
Not applicable
Newark, Delaware, 19716, United States
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:
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.
Healthy volunteers accepted: No
Only the study team can determine whether someone qualifies for participation.
Inclusion criteria
Exclusion criteria
The examiners will target vocabulary and grammar in the context of preschool science instructions.
Time frame: within three weeks following end of instruction
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.
Time frame: 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.
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.
Time frame: within three weeks following end of instruction
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.
Karla McGregor
Other
Improving STEM Outcomes for Young Children With Language Learning Disabilities by Intervening at the Intersection of Language and Scientific Thought: A Telehealth RCT
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