Displaying results 1 - 7 of 7 (Go to Advanced Search)
Project
Description: By leveraging technology, new knowledge will be gained on the linkage between teacher stress, teacher talk, teacher-student relationships, and pre-academic and social-emotional outcomes in Black and Latine students, paving the way for more efficacious interventions to reduce teacher stress.
Project
Description: Purpose: The special needs of young children (birth to 36 months) are not unidimensional. Early interventionists are challenged in developing interventions that fully address the needs of young children with developmental delays in multiple domains.
Project
Description: There are consistent correlations between mathematics achievement, attitudes, and anxiety, but the longitudinal relations among these constructs are not well understood nor are sex differences in these relations.
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.
