Specific Language Impairment Clinical Trial
Official title:
Improving STEM Outcomes for Young Children With Language Learning Disabilities by Intervening at the Intersection of Language and Scientific Thought.
The sophisticated language of science can be a barrier to Science, Technology, Engineering, and Math (STEM) learning, especially for children who have specific language impairment (SLI). The purpose of this randomized controlled trial is to test vocabulary and grammar interventions embedded in a small-group inquiry-based science instruction for their potential to ameliorate language deficits that impede science learning. Participants will be 54 preschoolers or kindergartners with SLI. Proximal and distal probes will reveal their mastery of taught and generalized language and science concepts.
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.
Fifty-four 4-to-7-year-olds who have not yet begun 1st grade, who are monolingual speakers of
English, and who have SLI will participate. The investigators will adopt a Randomized
Controlled Trial design, randomly assigning participants into three intervention conditions:
science + phonological awareness practice (the control arm), science + vocabulary supports,
and science + grammar supports, followed by a brief withdrawal phase in which all three
groups receive science only instruction. 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 after the 4-week intervention period than children in the
control arm.
2. The benefit of the science + language interventions will remain after the language
supports are withdrawn, that is, children in the science + language intervention
conditions will show greater gains in taught science concepts during the withdrawal week
than children in the control arm.
3. 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.
4. 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.
5. 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.
The first step is to 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 expectations are significant changes in vocabulary knowledge for
the vocabulary intervention condition as compared to the other two conditions, and
significant changes in use of complement clauses for the grammar intervention condition as
compared to the other two conditions. The next step is to test the predictions associated
with the specific aims via a series of binomial 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), language support (present, withdrawn), and
condition x language support as the independent variables (Predictions 1 and 2). If data
plotting suggests that effects are specific to the type of concepts being taught (e.g.,
physical science vs biological science), then we will build a second model to explore
differences related to concept type. 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 (Prediction 3).
Within-subject correlation will be accounted for with random subject effects. Additional
random effects will be determined by selecting the model with the best model fit (lowest AIC
value). In each of the three models, it is further expected that amount of improvement in
grammar and vocabulary are moderators between the outcome and the other factors (Prediction
4). To assess this prediction, performance on the language tests will be included as
covariates. The expectation is 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 eliminate the effect of condition because language
performance will be the main factor predicting science performance. These models also allow
comparison of the effectiveness of the grammar- and vocabulary-supported conditions
(Prediction 5).
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