Displaying results 1 - 7 of 7 (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.
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: National Project on Achievement in Twins
Description: This is the survey that was sent to NatPAT caregivers and twins in mid-summer 2022, focused on COVID-19 pandemic impacts on caregivers' and twin's lives.
Document
Part of Project: National Project on Achievement in Twins
Description: This document contains the codebook and all necessary information for the NatPAT COVID-19 Survey that was administered in mid-summer 2022.
Document Type: Codebook
Document
Part of Project: National Project on Achievement in Twins
Description: This document contains the codebook and all necessary information for the NatPAT COVID-19 Survey that was administered in mid-summer 2022.
Document Type: Codebook
