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 The benefit of the science + language interventions will remain after the language supports are withdrawn, that is, children in the science + language intervention conditions will show greater gains in taught science concepts during the withdrawal week than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).
