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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.
Document
Part of Project: Project KIDS
Description: This is a useful figure for new users of Project KIDS to see how the project worked. It lists the original projects, dates, and sample sizes of each component of Project KIDS.
Document Type: Figure
Document
Part of Project: Improving the Academic Performance of First-Grade Students with Reading and Math Difficulty
Description: This document is the following article:
Fuchs, L. S., Fuchs, D., Cho, E., Barnes, M. A., Koponen, T., & Espinas, D. R. (2024). Comorbid Word Reading and Mathematics Computation Difficulty at Start of First Grade. Journal of Learning Disabilities, 0(0).
https://doi.org/10.1177/00222194241248188
Document Type: Journal Contribution
