Data, Exhibits and Performance-based Assessment Systems

1. Data, Exhibits andPerformance-based Assessment Systems David C. Smith, Dean Emeritus College of Education University of Florida Email: smithdc@aol.com

2. Accountability and Assessment are With Us It is obvious that we are in an age of accountability and that inevitably involves assessment. Like the Ice Age, there is little reason to believe that it will pass quickly.

3. We Need Data • What data do you have to support your convictions and contentions? • How do you respond to important questions regarding program effectiveness and efficiency when you are asked, “what evidence do you have”, and “how do you know”?

4. Often the Right Behavior for the Wrong Reasons Think about assessment and the development of an assessment system as an opportunity rather than a problem or burden. (Not NCATE.)

5. Your Mind-set • An assessment system will not tell you what you should do. • Data-driven decisions. • Data-informed decisions.

6. Essential Considerations in Design • Have you thought deeply about the purpose, mission and vision of the organization and related them to the assessment system? • Is your conceptual framework reflected in your assessment system? • There are implications for what you choose to include in your assessment system.

7. Assessment System Issues to Consider • Will (do) you have a blueprint or framework (design) for your assessment system? • What criteria will you use for creating it? • What does (will) it look like? • How was (will) it be created? • Consider the language in Standard 2.

8. Inevitable Tension in Assessment • The need to accommodate your situation. • The need to compare with others in similar situations. • It is necessary to compare within and across institutions. (Internal relative productivity and comparison with counterpart units.)

9. Multiple Measures • Multiple measures can be valuable. Intentional redundancy can be critical. (Aircraft instruments.) • Sometimes a matter of perspective. It is valuable to look at a problem from more than one angle. (Headcount and FTE faculty and candidates.) • Sometimes a matter of timing. What are key points at which to access? (At a minimum, entrance, exit and follow-up.)

10. Problems that I see regularly see: • People have difficulty in creating an assessment system. • People think more about collecting data than they do about the structure of their assessment system and the kinds of data that they include in their assessment system. • They often want to do it for the wrong reason – for accreditation rather than seeing it as a tool to evaluate and improve what they are doing.

11. Other Problems • They have difficulty in using candidate three ways. (The aggregation issue.) • People are not aware of meaningful data that already exists and can be imported into their assessment system. Then they can focus their effort on data that they need to generate. • People often do not know how to use data well.

12. People often do not consider examining relationships among data sets. (FTE and headcount enrollment, enrollment and cost to generate a SCH). • Time is a problem. It is not realistic to expect that busy people can create and maintain an assessment system “on top of everything else”. It is very difficult to develop, implement, maintain and revise an assessment system without additional resources. • Resources – human and technological, are needed. The allocation of resources is a measure of institutional priority.

13. Collecting and Using Data • It is one thing to collect data. • It is another thing to be discriminating in collecting data. • And still another thing to know how to use data.

14. Proactive Data • We are not good at being proactive in generating data and we are not good at being creative in generating data. • Be proactive – give people information that they do not ask for but informs them more deeply about the effectiveness of your organization. • Think carefully about what creative and informative data you might want to include in your assessment system.

15. Aggregation and Design Issues - Timing • Admission. • Early in the program. • Mid-program. • Pre-student teaching. • Exit. • Follow-up.

16. Candidate. Demographic. Qualitative. Performance. Knowledge Skills Dispositions Evidence of a positive effect on student learning. Resources and Productivity People. Budget. Space. Equipment. Aggregation and Design Issues - Content

17. Aggregation and Design Issues – Levels of Data Course/Faculty Program Department / Cost Center Unit Institution

18. Aggregation and Design Issues – Sets and Sub-sets Course Faculty Course Faculty Course Faculty Program Program Program Program Program Department Unit Department Department Cost Centers Unit Unit Unit Support Centers Institution

19. Candidate Performance Assessment • Choose a question. (K – S – D) • How would you measure individual performance? • How would you aggregate the data to the program and the unit? • If appropriate, how would you compare the unit data with parallel institutional data?

20. Knowledge • The candidates are well-grounded in the content they teach. • The candidates possess the professional knowledge to practice competently. • The candidates possess technological knowledge for professional and instructional purposes.

21. Skills • The candidates can plan an effective lesson. • The candidates can give timely and effective feedback to their students. • The candidates appropriately address the needs of diverse and special needs students. • The candidates have a positive effect on student learning.

22. Dispositions • The candidates have a passion for teaching. • The candidates genuinely care about their students. • The candidates believe that all their students can learn. • The candidates are reflective practitioners.

23. Informing Through Exhibits • Provide data through exhibits. • The conceptual framework. • Evidence of candidate performance. • Portfolios. • Evidence of a positive effect on student learning. • Pictures are worth 1000s of words: • Clinical sites. • Maps. • Posters of events.

24. Exhibits Reflect a Climate • Exhibits can be user-friendly. • Access to documents. • Electronic support. • Video tapes. • Work stations. • CDs. • Creature comforts. • Pictures of campus events. • Faculty publications. • Location, location, location.

25. Everything is not easily measured. “It doesn’t make sense to think that you have to measure with a micrometer if you are going to mark with a piece of chalk and cut with an axe.”

26. Do not make high-stakes decisions based on soft data.  • Consider “directionality” in analyzing data.

27. What matters and what matters most? (The need to know and the nice to know.) • There are major implications for assessment system design and data elements.

28. Some of the least valuable data are the most easily gathered. • Some of the most important things may be the most difficult to measure.

29. What you do not measure is a profound statement about what you do not value.

30. People in an organization focus on what is measured not what is said to be important. • Consider the impact on single measures of performance in P-12 schools.

31. Assessing Your Assessment System • What data will you include? • How essential is it? • How important is it? • In considering your data. • How will you collect it? • How will you analyze it? • How will you use it?

32. Assessing Your Assessment System • Is your assessment system too large? • Is your assessment system too small? • Does it have the data you need? • Does it have data you do not use?

33. Creating an assessment system is a creative task; it is also tedious and time-consuming.