Displaying results 1 - 15 of 15 (Go to Advanced Search)
Project
Description: Language sampling is a critical component of language assessments. However, there are many ways to elicit language samples that likely impact the results. The purpose of this study was to examine how different discourse types and elicitation tasks affect various language sampling outcomes.
Project
Description: The aim of this research is to create developmentally appropriate, play-based storytelling elicitation procedures to collect language samples of young children aged 18-48 months, tools for evaluating the magnitude and quality of narrative language produced in play-based storytelling sessions, and examine the psychometric properties of these new
Project
Description: The analysis of narratives often accompanies comprehensive language assessments of students. While analyzing narratives can be time-consuming and labor-intensive, recent advances in large language models (LLMs) indicate that it may be possible to automate this process.
Project
Description: In research, augmentative and alternative communication (AAC) interventions have primarily focused on teaching children to make requests (Logan et al., 2017); however, AAC intervention should not stop there.
Project
Description: Science of reading experts have called for increased attention on oral academic language. Specifically, interventions need to integrate multiple dimensions of academic language—word-, sentence-, and discourse-level patterns—to impact listening comprehension.
Project
Description: This project relates to the published manuscript available at https://doi.org/10.1177%2F1747021820923944
Project
Description: Raw data files on test statistics of students in our SBL class of 2022.
Dataset
Part of Project: Impact of Discourse Type and Elicitation Task on Language Sampling Outcomes
Description: These are the data for 1037 K-3 students who contributed oral academic language samples. https://doi.org/10.1044/2023_AJSLP-22-00365
Dataset
Part of Project: Automated Narrative Scoring Using Large Language Models
Description: Narrative language samples elicited using the ALPS Oral Narrative Retell and Oral Narrative Generation tasks from diverse K-3 students. The test data set was drawn randomly from the larger corpus of narrative language samples.
Dataset
Part of Project: Automated Narrative Scoring Using Large Language Models
Description: Narrative language samples elicited using the ALPS Oral Narrative Retell and Oral Narrative Generation tasks from diverse K-3 students. The training data set was drawn randomly from the larger corpus of narrative language samples.
Dataset
Part of Project: Automated Narrative Scoring Using Large Language Models
Description: Narrative language samples elicited using the ALPS Oral Narrative Retell and Oral Narrative Generation tasks from diverse K-3 students. The tworaters data set was drawn randomly from the larger corpus of narrative language samples. These samples were scored by two raters for reliability purposes.
Dataset
Part of Project: AAC Narrative Intervention for Children with Autism
Description: These are the data for story grammar scores and number of different symbols used that resulted from the AAC narrative intervention. They are organized according to multiple baseline across participants design and pseudonyms are used instead of children's names.
Dataset
Part of Project: Oral Academic Narrative Language Intervention
Description: This file contains data according to two research designs: small scale RCT and a Repeated Acquisition Design (single case research). The vocabulary at the pre- and post- collections were untaught words whereas the weekly probes for the RAD were the taught words of that week.
Document
Part of Project: Listening to Speech and Non-speech Sounds Activates Phonological and Semantic Knowledge Differently
Description: This document contains all of the stimuli developed and used in the overarching project.
Document Type: Other
Document
Part of Project: Simulation Based Learning (SBL): an engineering course on Chao Phraya River’s salinity forecasting by Mathematical Modeling
Description: This document is a paper submitted to a Hindawi journal of education.
Document Type: Journal Contribution
