Conference: OSEP Leadership Conference Date: July 2023 Presenter: Sue Barlow, Michelle Lewis, Nicholas Ortiz, Kellen Reid In this poster from the 2023 OSEP Leadership Conference, DaSy highlights its mission, services, […]
Need practical strategies for evaluating how practitioners are doing with implementing evidence-based practices? Not sure what to do with all the data you’ve already collected? Not sure where to start? […]
For a Part C system, the fiscal data profile depicts demographic, service delivery, infrastructure, and administrative data in a variety of ways to make the fiscal data more comprehensible to users.
Compute response rates for your state’s family survey data and determine if the surveys you received are representative of the target population.
Developed to help states determine if all required fields in the SPP/APR platform have been completed and all relevant, required content is addressed for each indicator.
Many folks have been engaged in equity work at a personal and professional level. But what does it mean to have an equitable system? This session focused on how to intentionally include equity in your system through the indicators of quality in the revised ECTA/ DaSy System Framework. We shared how to use data at the leadership level to inform policy development, and provide opportunity to dig deep to identify needs, priorities, and opportunities for action at both the state and local level within your own system.
Have you wondered what to do to prepare for your state’s engagement in DMS 2.0? Representatives from three Part C Cohort 1 states that have completed the Office of Special Education Programs (OSEP) interviews and visits and have shared how they prepared for engaging in DMS 2.0, including successes, challenges, and lessons learned.
Conference: DEC & ISEI Joint Conference 2022 Date: September 28, 2022 Presenter: Patricia Blasco, Evelyn Shaw, and Kathryn Morrison This presentation described background information on children born at low birth […]
In this workshop, participants engaged in practical exercises to look for expected and unexpected patterns in their own data with the goal of increasing competence with data quality and prioritizing areas for data quality improvement.
In this session, participants engaged in practical exercises to look for expected and unexpected patterns in their own data with the goal of increasing competence with data quality and prioritizing areas for data quality improvement.
