Displaying results 1 - 20 of 44 (Go to Advanced Search)
Project
Description: In this study, we wanted to explore the support networks of instructional supervisors of beginning special education teachers and those of rural special education teachers, and how these networks relate to intent-to-stay in the field as a coach or special education teachers. We collected data across two waves.
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.
Project
Description: In this study, we examined if data analytics gleaned from an online literacy application could inform teachers of student reading progress above and beyond their progress monitoring scores. Participants were all K-1 students in one elementary school in a southeastern state.
Project
Description: The purpose of this project was to develop a small-group literacy intervention for kindergarteners who are at-risk for reading and writing difficulties.
Project
Description: This project page describes the creation of a latent reading score created by combining data across 12 datasets available on LDBase. This variable is the product of an Integrated Data Analysis (IDA) which was modeled using a multiple-group measurement model described below.
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.
Dataset
Part of Project: Project FOCUS: Exploring RTI Implementation with a Focus on Students Receiving Tier 3 and Special Education
Description: These are data from 1000 teachers and 77 administrators. Data include demographic variables on teachers, and their perceptions of RTI implementation in their school. This is a cross-sectional dataset.
Dataset
Part of Project: Headsprout Data Analytics
Description: Data from the Headsprout data analytics project. This dataset includes student progress monitoring data (DIBELS, TOWRE, CELF), data analytics from their engagement in Headsprout, and information about their home literacy environment (UFLI Home Literacy survey.
Dataset
Part of Project: Reading RULES! Kindergarten (RRK)
Description: These data are for teachers in the RRK study who were randomized to a treatment or control condition.
Dataset
Part of Project: Reading RULES! Kindergarten (RRK)
Description: Data for student participants including achievement data and demographics. Dataset is in wide format.
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: Project FOCUS: Exploring RTI Implementation with a Focus on Students Receiving Tier 3 and Special Education
Description: These are data from 65 school administrators.
Code
Part of Project: Project KIDS
Description: This code calculates internal consistency for 6 ISI projects. Including alpha for the total sample, and by project. This code accompanies "Examining differential intervention effects: Do Individualized Student Intervention effects vary by student abilities and characteristics?"
Code Type: Analysis
Code
Part of Project: Project KIDS
Description: This code takes 4 different datasets generated through the R code (EC_IDA_QR) and runs the MNLFA procedures in Mplus. All aspects of MNLFA are included, CFA, initial measurement variance, calibration, and scoring. Each section should be run separately in Mplus. There are 24 individual Mplus files included.
Code Type: Analysis
Code
Part of Project: Project KIDS
Description: This code generates descriptive statistics, Mplus input files for MNLFA models, multiple imputation, and multi-level quantile regression models that accompany the paper "Examining differential intervention effects: Do Individualized Student Intervention effects vary by student abilities and characteristics?"
Code Type: Analysis
