Displaying results 1 - 11 of 11 (Go to Advanced Search)
Project
Description: Mathematical thinking is in high demand in the global market, but approximately six percent of school-age children across the globe experience math difficulties (Shalev, et al., 2000).
Project
Description: We will build out the comprehensive infrastructure (data integration, research products, training, and policy relevant recommendations) which is required to support informed and open-source use of the new US Report And Placement Integrated Data System (RAPIDS).
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: The Home Math Environment and Children's Math Achievment: A Meta-Analysis
Description: The data are in long form, with some studies having multiple lines and includes a sample of children ranging from 3.54 to 13.75 years old. The main effect size is the r, correlation coefficient, and the accompanying sample size is also included.
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.
Code
Home Math Environment and Children's Math Achievement Meta-Analysis R code using the Metafor package
Part of Project: The Home Math Environment and Children's Math Achievment: A Meta-Analysis
Description: This code was written in R version 3.5.3 using the metafor package (Viechtbauer, 2010). First the dataset is called in, then the variables are converted to the correct formats for analysis, then the escalc() function is used to calculate an overall Fisher's Z effect size, which is then converted to an R correlation coefficient.
Code Type: Analysis
Document
Part of Project: Building Infrastructure and Training Tools to Understand CPS Involvement and Trajectories: An Open-Source Approach
Description: National individual-level longitudinal data: Compiling report records and foster care records spanning from 2005 to 2021.
Document Type: Other
Document
Part of Project: Building Infrastructure and Training Tools to Understand CPS Involvement and Trajectories: An Open-Source Approach
Description: National county-level longitudinal data: Linking report records and census data covering the period from 2007 to 2019.
GETTING STARTED WITH RAPIDS
Document Type: Other
Document
Part of Project: Building Infrastructure and Training Tools to Understand CPS Involvement and Trajectories: An Open-Source Approach
Description: RAPIDS Supplemental Policy Dataset and Documentation
Document Type: Other
