Displaying results 1 - 11 of 11 (Go to Advanced Search)
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: This study examined longitudinal outcomes of late-talking toddlers. Specifically, we included toddlers who participated in the Vocabulary Acquisition and Usage for Late Talkers (VAULT) treatment protocol.
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: A Longitudinal Assessment of Late Talking Toddlers
Description: This dataset contains the data from the overarching project that was collected via the Vocabulary Acquisition and Usage for Late Talkers (VAULT) protocol. This data is longitudinal in nature, containing data for baseline, pretest, at treatment, post treatment, and a delayed follow-up for 68 students.
Dataset
Part of Project: Support networks for instructional supervisors and special education teachers
Description: These data include demographic information on the participating coaches (n = 15) and their coaching context. These data are crosssectional and include specific ego-IDs that can be used to merge with Alter and Tie datasets.
Dataset
Part of Project: Support networks for instructional supervisors and special education teachers
Description: These data contain perceived demographic data on named alters (by Ego) and perceived support provision. These data can be combined with Ego level data to make a nested set using key ID variables.
Dataset
Part of Project: Support networks for instructional supervisors and special education teachers
Description: These data are ties between alters in the network. These data are needed to visualize the complete network using ego and alter data as well.
Document
Part of Project: A Longitudinal Assessment of Late Talking Toddlers
Description: This codebook contains the information needed to understand the variable naming conventions and structure for the VAULT dataset from the larger Longitudinal Assessment of Late Talking Toddlers project. Information is available for all timepoints.
Document Type: Codebook
Document
Part of Project: A Longitudinal Assessment of Late Talking Toddlers
Description: This document serves as an adjunct for understanding the nature of the VAULT data and its accompanying codebook. Specific information and citations are provided for the individual measures used. Additionally, the timeline/protocol used within the project is detailed in this document.
Document Type: Other
