Are Students on Track?: Comparing the Predictive Validity of Administrative and Survey Measures of Cognitive and Noncognitive Skills for Long-Term Outcomes. EdWorkingPaper No. 24-900

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Bibliographic Details
Title: Are Students on Track?: Comparing the Predictive Validity of Administrative and Survey Measures of Cognitive and Noncognitive Skills for Long-Term Outcomes. EdWorkingPaper No. 24-900
Language: English
Authors: Christopher Cleveland, Ethan Scherer, Annenberg Institute for School Reform at Brown University
Source: Annenberg Institute for School Reform at Brown University. 2024.
Availability: Annenberg Institute for School Reform at Brown University. Brown University Box 1985, Providence, RI 02912. Tel: 401-863-7990; Fax: 401-863-1290; e-mail: annenberg@brown.edu; Web site: https://annenberg.brown.edu/
Peer Reviewed: N
Page Count: 31
Publication Date: 2024
Sponsoring Agency: Institute of Education Sciences (ED)
Contract Number: R305B150010
Document Type: Reports - Research
Education Level: Elementary Education
Grade 8
Junior High Schools
Middle Schools
Secondary Education
Descriptors: Surveys, Thinking Skills, Self Management, Student Behavior, Performance, Scores, Grade 8, Middle School Students, Outcomes of Education, Predictor Variables, Track System (Education), Cognitive Processes
Abstract: Education leaders must identify valid metrics to predict student long-term success. We exploit a unique dataset containing data on cognitive skills, self-regulation, behavior, course performance, and test scores for 8th-grade students. We link these data to data on students' high school outcomes, college enrollment, persistence, and on-time degree completion. Cognitive tests and survey-based self-regulation measures predict high school and college outcomes. However, these relationships become small and lose statistical significance when we control for test scores and a behavioral index. For leaders hoping to identify the best on-track indicators for college completion, the information collected in student longitudinal data systems better predicts both short- and long-run educational outcomes than these survey-based measures of self-regulation and cognitive skills.
Abstractor: As Provided
IES Funded: Yes
Entry Date: 2025
Accession Number: ED664524
Database: ERIC
Description
Abstract:Education leaders must identify valid metrics to predict student long-term success. We exploit a unique dataset containing data on cognitive skills, self-regulation, behavior, course performance, and test scores for 8th-grade students. We link these data to data on students' high school outcomes, college enrollment, persistence, and on-time degree completion. Cognitive tests and survey-based self-regulation measures predict high school and college outcomes. However, these relationships become small and lose statistical significance when we control for test scores and a behavioral index. For leaders hoping to identify the best on-track indicators for college completion, the information collected in student longitudinal data systems better predicts both short- and long-run educational outcomes than these survey-based measures of self-regulation and cognitive skills.