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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: 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
