Novice Teen Driver Education Data Collection Guide
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Summary
This document, published by the National Highway Traffic Safety Administration (NHTSA) in October 2024, serves as a comprehensive guide for State driver education program administrators to standardize and improve data collection practices. The primary motivation is to assist States in meeting the Novice Teen Driver Education and Training Administrative Standards (NTDETAS), specifically Section 1.3, which mandates the collection of accurate and relevant data to evaluate program effectiveness. The guide addresses the challenge that data collection in driver education has historically been inconsistent, making it difficult to measure performance, identify areas for improvement, and demonstrate program impact. By aligning with the Safe System Approach, which elevates education as a pre-crash safety effort, the document aims to provide a foundation for evidence-based decision-making and potential future research. The guide provides methodological frameworks and tools rather than presenting original empirical research. It introduces a Data Inventory Tool, located in Appendix A, which helps administrators catalog existing data assets, including data sets, elements, owners, and update frequencies. The document outlines strategies for forming partnerships with agencies such as State Highway Safety Offices and Traffic Records Coordinating Committees to leverage existing data infrastructure and analytical expertise. It also emphasizes compliance with data privacy laws, such as the Driver’s Privacy Protection Act. A significant portion of the guide is dedicated to sample scenarios divided into "Operations" and "Program" categories. These scenarios provide specific questions administrators might ask, identify potential data sources, and offer equations or guided solutions for analysis. For example, it details how to calculate student enrollment rates, average ages, pass/fail rates for knowledge tests, and provider distribution across counties. The findings presented are instructional, offering standardized methods for administrators to derive metrics from their own state data. The guide demonstrates how to calculate operational statistics, such as the percentage of students failing due to attendance, the average number of attempts to pass license knowledge tests, and the frequency of provider sanctions. It also provides frameworks for assessing program impact, such as linking student information to driver records to analyze crash or citation data. The document highlights that rigorous evaluation is complex due to confounding variables like socioeconomic status and prior driving experience, advising administrators to consult experts for advanced statistical methodologies. The significance of this guide lies in its role as a practical resource for enhancing the quality and consistency of novice teen driver education programs across the United States. By providing a structured approach to data collection and analysis, it enables administrators to move beyond anecdotal evidence and make informed decisions to improve program outcomes. The guide supports the broader goal of producing safe and responsible teen drivers by ensuring that driver education programs are accountable, measurable, and integrated into State Strategic Highway Safety Plans. It facilitates the identification of program strengths and weaknesses, ultimately supporting efforts to reduce crashes, injuries, and fatalities among novice drivers.
Key finding
The guide provides a structured framework and tools for State administrators to systematically collect and analyze driver education data to evaluate program performance and compliance with administrative standards.
Methodology
dataset
Provenance
The full processing record for this entry. Every stage of this paper's journey through the pipeline is logged — what ran, with which tool and model, how many attempts it took, and when it last completed. Discovered via bulk_ingest_rosap on 2026-05-23 (25 acquisition events logged).
| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | rosap | — | — | 2 | 2026-05-23 |
| archive | success | — | — | — | 1 | 2026-05-23 |
| extract | success | cached | — | — | 2 | 2026-06-10 |
| clean | success | — | — | — | 1 | 2026-06-01 |
| chunk | success | — | — | — | 1 | 2026-06-01 |
| embed | success | — | — | — | 1 | 2026-06-02 |
| enrich | success | — | — | — | 1 | 2026-05-23 |
| promote | success | — | — | — | 1 | 2026-05-23 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 22 | 2026-06-10 |
| tag | success | vector_similarity | — | — | 19 | 2026-06-11 |
| verify | success | — | — | — | 2 | 2026-06-10 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.
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- Applied Guidance: countermeasure evaluation
- Empirical Findings: observational prevalence
- Methodological Resource: dataset resource