Evaluation of Math-Science Partnership Projects

1. Evaluation of Math-Science Partnership Projects (or how to find out if you’re really getting your money’s worth)

2. Why Should States Require Good Evaluations of MSP Projects? • To determine if the project’s objectives contribute to State education goals • To find out how activities are implemented during the year • To monitor the project’s progress toward achieving its objectives • To determine if the project ultimately reaches its objectives (and if not, why not)

3. A good local evaluation can ... • Provide evidence that is directly relevant to the district’s students, teachers, and schools • Provide immediate feedback to improve on-going projects • Provide information for making informed decisions about allocating local resources

4. Developing the State RFP (Or, how to ask for something so that you get what you want )

5. What do you want to see in a good evaluation? • Clear objectives with measures that directly assess the targets of each objective • Documentation of program implementation and progress • An evaluation design that can clearly show whether program activities themselves are the cause of any changes in target outcomes

6. Teacher-Focused Objectives • Increase the number of mathematics and science teachers who participate in content-based professional development activities • Increase teachers’ content knowledge in mathematics or science

7. Student-Focused Objectives • Improve student academic achievement on the state mathematics and science assessments

8. Measuring Progress For each objective, there should be at least one measure (or indicator) that directly assesses the objective’s target outcome

9. To measure an increase in teachers’ math content knowledge, there must be a direct measure of teachers’ math content knowledge. Measuring Progress: Example • Course specific content test – YES • Teacher certification math content test – YES (but not a math pedagogy test) • Teacher self-report of learning or course satisfaction – NO

10. To measure improvement in students’ mathematics achievement, there must be a direct measure of students’ mathematics achievement. Measuring Progress: Example • State mathematics achievement test – YES • Student self-report of learning or interest in mathematics – NO

11. Documenting the Program’s Implementation and Progress • Who are the participants? • Were activities carried out as planned and on what timeline? • If problems were noted, how were they corrected? • Do early data show progress toward the expected outcomes?

12. How do you determine whether the project activities themselves actually produce changes in the target outcomes? (Where’s the beef?)

13. Evaluation Design • Baseline data are essential • A comparison group is important • Random assignment is the only sure method for determining program effectiveness

14. What is random assignment? • Intervention and comparison groups are constructed by randomly assigning some teachers, schools or districts to participate in the program activities and others to not participate • Random assignment is not the same as random selection (e.g., randomly choosing 5 schools that use Curriculum X out of schools that already use Curriculum X to compare with 5 randomly chosen schools that use Curriculum Y out of schools that already use Y)

15. The Random Assignment Difference: The Career Academy Study • In a recent study, 73% of students voluntarily enrolling in a high school technical education program called Career Academy graduated on time. • Completion rates for students from the National Education Longitudinal Survey who followed a career technical curriculum or a general curriculum in high school were 64% and 54%, respectively. • BUT students in the Career Academy study who had been randomly assigned to the control condition graduated at the rate of 72%, not significantly different from the students in the Career Academy intervention

16. Career Academies

17. If not random assignment, then what • Use a comparison group of students, schools or districts that are carefully matched to the targeted population in academic achievement levels, demographics, and other characteristics thought to be relevant to the intervention (e.g., teachers’ years of classroom experience) prior to the implementation of the intervention

18. If not random assignment, then what • Be sure to identify both the intervention and comparison groups and the outcome measures before the intervention is administered • Finally, be sure that the comparison group is not comprised of students or schools that had the opportunity to participate in the intervention but declined.

19. Writing the Evaluation Component: Measures and Data Collection • Require objectives with measures (indicators) that directly relate to the objectives • Require baseline data (existing or a project administered pre-test) • Require data that documents what was implemented and how the program was implemented

20. Writing the Evaluation Component: Evaluation Design • Require an evaluation design that can determine whether the project activities themselves produce changes in the target outcomes • Encourage use of random assignment designs • Encourage applicants to seek assistance from consultants who have experience in conducting impact evaluations of programs

21. Review of Plans: Are Outcomes Linked to Objectives? • Are objectives stated in measurable terms? • Is progress toward each objective measured by a specific indicator or indicators that directly relates to the objective? • Do the identified indicators cover all of the key outcomes?

22. Review of Plans: Will Data Be Used to Improve Program? • Will evaluation data be collected throughout the project? • Will evaluation data be used to inform project activities? • Is the timeline for collection of evaluation data integrated with the overall project timeline? • Will the data they plan to collect provide information about various components of the project?

23. Review of Plans: Will the Evaluation Assess the Impact of the Program? • Does the design allow the applicant to determine that observed changes in outcomes are due to the program? • Do they collect or use baseline data? • Do they include a control or comparison group in their evaluation design? • Do they use random assignment?

24. Review of Plans: Do Project Personnel Have Expertise in Impact Evaluations? • Do they involve an experienced evaluator, or does someone on their staff with sufficient experience in quantitative program evaluation? • Does the evaluator have a sufficient time investment to carry out the evaluation over the life of the program?

25. Who can help review the evaluation component of the MSP proposals? • University faculty with expertise in quantitative program evaluation • Public policy • Public health • Prevention science • Psychology • Evaluators with expertise and experience with random assignment evaluations