Clinical Trial Details
— Status: Completed
Administrative data
NCT number |
NCT04775199 |
Other study ID # |
NSF1661166 |
Secondary ID |
|
Status |
Completed |
Phase |
N/A
|
First received |
|
Last updated |
|
Start date |
January 9, 2021 |
Est. completion date |
August 1, 2023 |
Study information
Verified date |
August 2023 |
Source |
Father Flanagan's Boys' Home |
Contact |
n/a |
Is FDA regulated |
No |
Health authority |
|
Study type |
Interventional
|
Clinical Trial Summary
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
Description:
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:
1. 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.
2. 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.
3. 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.
4. 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.