Displaying results 1 - 9 of 9 (Go to Advanced Search)
Project
Description: Word knowledge is critical for speaking, reading and writing, yet a substantial proportion of children with language impairment demonstrate poor word learning and consequently poor vocabulary. Because vocabulary has a causal relationship with reading comprehension, this presents a significant national health concern.
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: Profiles of Working Memory and Word Learning for Educational Research (POWWER)
Description: This dataset incudes data from 248 second graders (7- to 8-year-olds) with typical development from three states. One hundred sixty-seven were monolingual English-speaking and 81 were dual Spanish- and English-speaking.
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: Support networks for instructional supervisors and special education teachers
Description: These data include demographic information on the participating coaches (n = 15) and their coaching context. These data are crosssectional and include specific ego-IDs that can be used to merge with Alter and Tie datasets.
Dataset
Part of Project: Support networks for instructional supervisors and special education teachers
Description: These data contain perceived demographic data on named alters (by Ego) and perceived support provision. These data can be combined with Ego level data to make a nested set using key ID variables.
Dataset
Part of Project: Support networks for instructional supervisors and special education teachers
Description: These data are ties between alters in the network. These data are needed to visualize the complete network using ego and alter data as well.
