Data Analysis - Tools and Processes (School Level) Food for Thought

1. Data Analysis - Tools and Processes(School Level) Food for Thought How does your school use data to inform instruction and improve student achievement? Tuesday, February 21, 2012 Hawaii Department of Education Office of Curriculum, Instruction and Student Support

2. 2. Use the “Hand” icon if you wish to speak or have a question. • Before the meeting starts, close • any other applications running • on your computer. 3. Mute your microphone to eliminate ambient noises. Webiquette Muted 7. Your collaboration is vital. Every perspective contributes to the whole picture. Unmuted 4. Make sure that your “Chat” box is set for “Everyone.” 6. Restrict the use of text-speak, please respond using standard English to text. 5. Use the “Chat” box for questions.

3. Group Norms for Webinar • Self-directed Learner • Make personal connections to your position • Community Contributor • Honor the expertise of ALL • Complex Thinker • Synergize – Collective thoughts • Quality Producer • Grow professionally • Effective Communicator • Seek first to understand, then to be understood • Effective & Ethical User of Technology • Remove all other distractions

6. Hawaii’s Five RTTT Pillars Systems of Support to enable schools to do their best work – reprioritize and reorganize State resources; establish Human Resources Unit in Zones of School Innovation; automate 5. Alignment and performance monitoring of organizational functions to support reform outcomes • Focused support on lowest-performing schools - • Zones of School Innovation • Flexibility • Great teachers and great leaders • Remove barriers to learning Common Core Standards Career & College Ready Diploma Curriculum Framework Common Instructional Materials Formative Assessments Interim Assessments Summative Assessments STEM Improved Student Outcomes Performance-based evaluation system New Teacher Induction & Mentoring Incentives Leadership development Alternative pathways Data for School Improvement Longitudinal Data System Balanced Scorecard Data Governance Using data to inform instruction 6

7. Essential Question How does data analysis help in school improvement efforts?

8. Desired Outcomes • A common understanding of the various purposes for analyzing data • An understanding of how to analyze data using a variety of tools and processes

9. Data Analysis (School Level) Agenda • Reason We Analyze Data • Basic Information We Use to Analyze Data • Processes We Can Use to Analyze Data • Finding Root Causes

10. Reason We Analyze Data • Why do we need to use data? • Why do we want to use data?

11. Why We Need to Use Data

12. Why We Need to Use Data

13. Why We Need to Use Data

14. Why We Need to Use Data

15. Why We Need to Use Data

16. Why We Need to Use Data =

17. Why We Need to Use Data • Formative Assessment / Instruction • Data for School Improvement (DSI) as a formative assessment tool • Using DSI Reports to inform instruction • Deconstructing the Standards Process K-12

18. Why We Want to Use Data • Pick a number from 1-10. • Multiply that number by 9. • Add up the digits of the answer. • Subtract 5 from the number. • Find the letter that corresponds to the number (example: 1=A, 2=B, 3=C, etc.) • Think of a country whose name starts with that letter and write it down. • Take the 2nd letter in the country's name, and think of an animal whose name starts with that letter and write it down. • Write down the color of that animal. • Take the last letter of the country's name and write down an animal whose name starts with that letter. • Use the last letter of that animal and write down a fruit that starts with that letter.

19. Types of School Teams

20. Take a Minute • How does your school use data to help students and teachers succeed? • What types of data teams do you have at your school?

21. State Goals Vision of a Hawaii high school graduate is that all public school graduates will: • Realize their individual goals and aspirations; • Possess the attitudes, knowledge and skills necessary to contribute positively and compete in a global society; • Exercise the rights and responsibilities of citizenship; and • Pursue post-secondary education and/or careers without need for remediation.

22. State Goals Vision of a Hawaii high school graduate There are six General Learner Outcomes (GLOs) that are the goals of standards-based learning in all content areas: • Self-Directed Learner: The ability to be responsible for one's own learning • Community Contributor: The understanding that it is essential for human beings to work together • Complex Thinker: The ability to be involved in complex thinking and problem solving • Quality Producer: The ability to recognize and produce quality performance and quality products • Effective Communicator: the ability to communicate effectively • Effective and Ethical User of Technology: the ability to use a variety of technology effectively and ethically.

23. Inverted Data Pyramid N. Love, 2010

24. Data Pyramid: What kinds of data do teams and coaches use? N. Love, 2010

25. Data Analysis (School Level) Agenda • Reason We Analyze Data • Basic Information We Use to Analyze Data • Processes We Can Use to Analyze Data • Finding Root Causes

26. Using Data at the School Level

27. Too much data? • Diverts attention away from the primary purpose: improving instruction. • Leads to overload– creating long, “comprehensive” plans that few read. • Reveals too many things to address – so too many goals and initiatives are created.

28. Multiple Measures Processes V. Bernhardt

29. Demographic Data • Clarifies who our “clients” are. • Builds on the context of the school • Helps to predict future conditions to best serve the needs of our future students. “Demographic information is crucial in data analysis as it helps us understand the context within which schoolwide change is planned and takes place.” (V. Bernhardt, 1998, p-25)

30. Examples of Demographic Data • Number of students in the school • Number of students with special needs • Ethnicities of the students in the school • Number of graduates • Number of disadvantaged students

31. Tools –Finding Data Demographic (Type any other places that you get this type of data into the Chat box) Longitudinal Data System (LDS) http://employees.hidoe.k12.hi.us

32. Tools –Finding Data Demographic (Type any other places that you get this type of data into the Chat box) United States Census http://quickfacts.census.gov/qfd/states/15000.html

33. Tools –Finding Data Hawaii Department of Business, Economic Development & Tourism http://hawaii.gov/dbedt/info/census/ Demographic (Type any other places that you get this type of data into the Chat box)

34. Tools –Finding Data School Documents Online http://iportal.k12.hi.us Demographic (Type any other places that you get this type of data into the Chat box)

35. Perceptual Data • A view, judgment or appraisal formed in the mind about a particular matter. • A belief stronger than impression and less strong than positive knowledge. • A judgment one holds as true. “ In organizations, if we want to know what is possible . . .we need to know the perceptions of the people who make up the organization.” V. Bernhardt, 1998,pg. 41

36. Examples of Perceptual Data • Observations • Person-to-person interviews • Telephone surveys • Focus groups • Parent surveys

37. Tools –Finding Data School Quality Survey (SQS) http://arch.k12.hi.us Perceptions(Type any other places that you get this type of data into the Chat box)

38. Process Data • Programs can include a wide variety of offerings, from specially funded programs to academic curricular sequences to extracurricular programs.

39. Examples of Process Data (Type any other places that you get this type of data into the Chat box) • Grant data • Program data • Comprehensive Needs Assessment (continuous improvement process) • Curriculum mapping • Data Teams

40. Student Learning Data Most important type of data to focus on. • Annual Large-Scale Assessment Data • Periodic Assessment Data • Ongoing Classroom Assessment Data

41. Examples of Student Learning Data • Hawaii State Assessment (HSA) • Terra Nova • DIBELS/DIBELS Next • Reading Inventories • Classroom Assessment Data LDS

42. Tools –Finding Data Student Learning(Type any other places that you get this type of data into the Chat box) Accountability Resource Center Hawaii (ARCH) http://arch.k12.hi.us

43. Tools –Finding Data School Status & Improvement Report (SSIR) http://arch.k12.hi.us Covers Demographic, Perceptual, and Student Learning(Type any other places that you get this type of data into the Chat box)

44. Disaggregating Data

45. Disaggregating Data Typically, student achievement data are reported for whole populations, or as aggregate data. It is not, however, until the data are disaggregated that patterns,trendsand other important information are uncovered. ** Disaggregated data simply means looking at test scores by specific subgroups of students.

46. Disaggregating Uncovers . . . • Achievement gaps are differences in academic achievement amongst different groups of students. • It is important to examine these differences in order to find ways that we can address some of the inequities. • The disaggregated data and the dialogue that arises can transform beliefs and practices.

47. Trend Data • Data that shows a pattern over time. • Time is the variable over which one constant is being compared. • The more years of data that you have, the more reliable are the trends and patterns. • Statistically three years of data just barely indicates a trend. Five years provides more confidence to your inferences. N. Love, 2008

48. Several Ways to Disaggregate Data • Gender • Socio-Economic Status • Mobility • Special Education and Disability • ELL – English Language Learners • Grade level • Classroom or course

49. Take a Minute • What student data/information does your school use to make decisions?

50. Data Analysis (School Level) Agenda • Reason We Analyze Data • Basic Information We Use to Analyze Data • Processes We Can Use to Analyze Data • Finding Root Causes