Predicting response to vocabulary intervention using dynamic assessment

Research output: Contribution to journalJournal articlepeer-review

Purpose: The purpose of this study was to examine how well students’ response to a morphological vocabulary intervention can be predicted before the start of the intervention from traditional static assessments and to determine whether a dynamic assessment with graduated prompts improves the prediction. Method: A planned secondary analysis of a randomized trial of a morphological vocabulary intervention for fifth-grade students with limited vocabulary was conducted. Response to this intervention was examined for 111 participants based on their development in definitions of morphologically transparent words from pretest to posttest. Traditional static measures of vocabulary, knowledge of morphology, and morphological analysis as well as a dynamic assessment of morphological analysis were evaluated as predictors of students’ response to intervention. Results: The static pretest measures predicted more than half of the overall variance in students’ response to intervention and provided a good classification of students with subsequent poor or good response to intervention. The single best static predictor was the static assessment of morphological analysis. Furthermore, the dynamic assessment added significantly to the prediction of the overall variance in students’ response to intervention and to the correct early classification of students as poor or good responders. Conclusions: The results suggest that an acceptable level of prediction of students’ response to morphological vocabulary intervention can be obtained by means of a couple of static morphological measures. This study also provides evidence for the added predictive value of a dynamic assessment of morphological analysis.

Original languageEnglish
JournalLanguage, Speech and Hearing Services in Schools
Volume51
Issue number4
Pages (from-to)1112-1123
Number of pages12
ISSN0161-1461
DOIs
Publication statusPublished - Oct 2020

ID: 250379647