1. NU Data Building Data-Use in Nebraska Schools Dr. Beth Doll bdoll2@unl.edu

2. Background… Previous study examined why special education teams in Nebraska aren’t using research-based team consultation practices. ( Doll, B., Haack, K., Kosse, S., Osterloh, M., Siemers, E., & Pray, B., 2005). Teams stated the following concerns: They had limited access to reliable, effective, and practical strategies for gathering data on student learning and behavior They lacked training and experience using data to make educational decisions about their students They struggled to determine whether the interventions they were using empirically sound

3. Striking Mismatch… Between growing expectations that schools have data to document student outcomes and teachers’ sense that they were unprepared to use data-based problem-solving in their daily work (Doll et al., 2005) Six Barriers Identified by Teachers: 1.Knowing of a variety of was to collect data 2.Selecting protocols that are best suited to answer the teams’ questions 3.Collating and graphing data 4.Discerning trends and differences in data 5.Using data and data trends in team problem-solving and planning 6.Selecting evidence-based interventions

4. The Solution… Train teachers and other educational professionals to be the “data experts” in their schools by addressing the barriers to effective data-based problem-solving.

5. Assess needs Make a plan for change Gather data while making the changes Evaluate & refine the plan Data-Based Problem-Solving

6. The Plan… Develop and field test a professional development program that focuses on strengthening the six data pragmatic skills identified by teachers (NU Data) Develop a range of online resources that will help enhance data-based decision-making skills of teachers and other educational professionals Train special education teams to use student data and data-based decision-making effectively in their schools Improve the academic achievement, behavioral success, and academic engagement of students with disabilities Examine the program’s feasibility and impact

7. A Blended Distance Intervention Provide guided practice in diverse strategies for data use and data-based decision-making (Seminars) Coach teams as they practice data-based decision- making (Weekly Virtual Check-Ins) Develop and refine online resources on data use and data-based decision-making that teams can use as ‘just-in-time’ references Foster colleague networks among the teams Refine and improve the NU Data intervention through a cycle of quantitative and qualitative evaluation (See the NU Data Timeline)

8. NU Data Participants Each year, 5 three-member special education teams are chosen to participate (For a total of 15 teams across Years 1, 2, and 3) Each team will receive expert coaching on the six data pragmatics: 1.Knowing of a variety of was to collect data 2.Selecting protocols that are best suited to answer the teams’ questions 3.Collating and graphing data 4.Discerning trends and differences in data 5.Using data and data trends in team problem-solving and planning 6.Selecting evidence-based interventions Each team will try out their data-use strategies with six students with disabilities

9. Year 1 Teams Belmont Elementary Beatrice Public Schools Aurora Public Schools (Elem.) Aurora Public Schools (MS/HS) Ord Public Schools Year 2 Teams Clinton Elementary Beatrice Public Schools Tri-County Public Schools Syracuse Public Schools Schuyler Public Schools Year 3 Teams Confirmed: Westside Middle School Seward Public Schools Interested Districts: Waverly Public Schools Gibbon Public School Kearney Public Schools Lexington Public Schools School Teams Participating in NU Data

10. What Participating Teams Do… Apply the NU Data principles and strategies of data collection with at least six students with disabilities Attend three face-to-face seminar meetings (beginning, mid-year, and end of year) Access online resources as needed to support teams’ work with the students Participate in weekly peer and expert coaching on teams’ use of data Participate in the NU Data evaluation (See the NU Data Timeline)

11. A Snapshot of Seminar 1 Activities Data pragmatics activity – what’s needed and what gets in the way Case example of applying the NU Data pragmatics to a student Guided practice in organizing and graphing data Teams graph and analyze screening data for their six students and identify their goals for change Measures menu – shown nine different ways to collect data on behavior, engagement, or learning Involving parents – an NU Data newsletter for stakeholders

12. A Snapshot of Seminar 2 Activities Show-and-tell – sharing great strategies, measures, or successes Practice with an analog case Teams graph and analyze data for the six students and make decisions about next steps Sharing casework across teams Guided practice in discerning trends in data Intervention strategy sheets Sometimes it’s not the student – extending interventions to classrooms or schools What makes an intervention ‘evidence-based’

13. A Snapshot of Seminar 3 Activities Teams graph and analyze intervention data and decide whether their goals were met Show-and-tell Discuss the importance of collaboration and consultation in schools Sharing casework across teams Practice with an analog case Evaluating the evidence for interventions Fostering students’ friendships Sustaining NU Data over time

14. Early Outcomes – Quantitative Data Participants believe that their knowledge of data use is substantially stronger Participants’ scores on a data knowledge measure increases Participants rate their data-based decision-making as more acceptable Participants personal efficacy for using student data is stronger

15. Early Outcomes - Qualitative Relevance and Value : Participants felt the six data pragmatics were relevant to their work and would help them be better at their jobs. Hands-on Preparation Time : Participants valued the hands- on time during the seminars to prepare their materials for use back at school. Teamwork and Expanding Participation : Participants benefited from working with their team members, the other teams and other teachers and staff at their schools. Confidence : Participants expressed an increased confidence in using the six data pragmatics with their students. Interest in Continuing Participation : Several participants expressed an interest in continuing with the NU Data program beyond the requirements of their cohort.

16. Testimonials from Year 1 Participants: “What I really like though is it’s very practical and we can actually use it. I think this allowed us to go to a place where we can work on it too, where it won’t just go on a shelf and say, oh, I’ll get to that eventually.” “Having the time to work with team members was really valuable so that we could actually—you know, when you go back to school you don’t have the time to sit down and do this so that was really valuable. We were even able to start—we’ve got our goals, we’ve got some of the data sheets ready to go. We’re ready to go back and start putting it into play.” “I feel pretty comfortable with the data. I’m not as good at putting it in; graphing it. I think making decisions from the data, I feel comfortable with, but graphing it and yeah, just following through. Making sure you’re consistent, using it all the time.”

17. Expected Outcomes at the End of Three Years: An NU Data intervention manual that specifies the content, materials, and implementation procedures of the NU Data program Curricular materials including podcasted lectures, narrated PowerPoints, video examples of data-based decision-making, case examples from the NU Data participants, web-based ThinkAboutIt © case modules A summary of the results describing the feasibility and usefulness of NU Data Field-tested measures that can be used to evaluate the effectiveness and feasibility of NU Data in a subsequent proposal Technical information about NU Data and the measures necessary to craft a solid research design for a Goal 3 project