Displaying results 1 - 6 of 6 (Go to Advanced Search)
Project
Description: Science of reading experts have called for increased attention on oral academic language. Specifically, interventions need to integrate multiple dimensions of academic language—word-, sentence-, and discourse-level patterns—to impact listening comprehension.
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: Researchers will evaluate the impact of two theoretically distinct versions of an intervention called Developing Talkers (DT) that uses whole-group read alouds in kindergarten and Grade 1. DT is designed to improve teacher facilitation of academic language skills and the academic language skills of students.
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.
