Your SlideShare is downloading. ×
Data informed decision-making
Upcoming SlideShare
Loading in...5
×

Thanks for flagging this SlideShare!

Oops! An error has occurred.

×

Introducing the official SlideShare app

Stunning, full-screen experience for iPhone and Android

Text the download link to your phone

Standard text messaging rates apply

Data informed decision-making

1,223
views

Published on

A PowerPoint on Data informed decision making

A PowerPoint on Data informed decision making

Published in: Education, Technology

0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total Views
1,223
On Slideshare
0
From Embeds
0
Number of Embeds
2
Actions
Shares
0
Downloads
21
Comments
0
Likes
0
Embeds 0
No embeds

Report content
Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
No notes for slide
  • We care about data because we care about children learning and succeeding. Data can sound the alarm when someone is not learning and activate an immediate response. The data give us a powerful entrée into dialogue about the toughest issues, such as confronting how well our schools are working for all children and the inequitable practices that persist. They challenge and help us rethink basic assumptions.They hold a mirror up to instructional practice to pinpoint what is and is not working. Data help to set the right goals for action and, once changes are implemented, they provide constant feedback to guide mid-course corrections and monitor results.Data also give us cause for celebration and opportunity to recognize teachers and students for a job well done. Using data to guide action is the most powerful lever we have to improve our schools; and yet, despite the increasing quantity now available, data are woefully underutilized as a force for change.Schools are gathering more and more data, but having data available does not mean that data are used to guide instructional improvement. Many schools lack the process to connect the data that they have with the results they must produce. - Love, 2004, p. 23
  • Do individual then groups transfer to chart paper.
  • Full demoBuilding AYP – figured by building configurationDistrict AYP – figured by bands, 3-5, 6-8, 9-12
  • Full demo using data director site
  • Facilitation - Continuation of the work – sub-group analysis – Check time…check for understanding if comfortable…move onAt table generate a list of question based on this dataChart out
  • Full demo using data director site
  • Facilitation - Continuation of the work – sub-group analysis – Check time…check for understanding if comfortable…move onAt table generate a list of question based on this dataChart out
  • Transcript

    • 1. Learning-Centered Leadership Development Program for Practicing and Aspiring Principals Western Michigan University Kalamazoo, MI 49008 A Project funded by the United States Department of Education (USDOE), Washing, DC: 2010 Module 1: Data-Informed Decision-Making
    • 2. INTRODUCTION • Do you Believe in me? - Dalton Sherman • Reflections • Learning Objectives 2
    • 3. Dalton Sherman http://www.youtube.com/watch?v=HAMLOnSNwzA 3
    • 4. Reflection Upon Dalton Sherman’s Speech • Do staff in your school believe that all students can learn? • What does this belief look like in your school? • How do you know that all students are learning? • What changes do you need to make to align practices with beliefs? 4
    • 5. Learning Objectives As a consequence of participating in this module, participants will: • Understand and experience the importance of data in a continuous improvement cycle; • Utilize a data mining tool D4SS (Data for Student Success, MDE) that will equip you with an understanding about how to disaggregate student data and identify learning gaps in students performance by gender, ethnicity, SES, and learning impairments; • Learn from other practicing and aspiring school leaders about the effect and challenges of their evidence-based instructional initiatives; and • Develop and implement a renewal activity in a high priority content area that is designed to improve student achievement. 5
    • 6. Conceptual Framework for Date-Informed Decision-Making • What is Data? • Putting your Fear on the Table Regarding the Use of Data • Conceptual Model for Data Use • Collaborative Inquiry Process 6
    • 7. What is Data? 7
    • 8. Examples of Data  Numbers  Opinions  Observations  Essays  Science projects  Demonstrations  and ……. 8
    • 9. Data Can Answer These Questions 1. How are we doing? 2. Are we serving all students well? 3. In what areas must we improve? Other Questions? 9
    • 10. Principals’ Perception Regarding the Use of Data Of Teachers  Teachers uncomfortable with data  Teachers cannot read data  Data has meaning to classroom  Do not know what to do with data  Data not part of teacher training  Lack of knowledge data- instruction  No data link to teaching practices Of Themselves  Do not understand data use  What data do you use  Teacher collection of data  Need systematic disaggregation  Find better assessment tools  PD for teachers and principals 10
    • 11. Principals’ Perception of Time Constraints to Analyze Data  Time to complete tasks  Data vs. classroom duties  Limited instructional time  Time to analyze data  Time for collaboration  Time to monitor teacher use  Time in getting test results  Time in getting data back  A year behind-results  Holistic approach in working with teachers 11
    • 12. Principals’ Perception of Teacher and Student Issues  Teacher cooperation in assessment  Teacher- team cynicism  Teachers see data as important  Teacher-staff cooperation in data assessment  Inconsistent teacher collection of student data  Student do not take testing seriously  A few teachers see testing as a fad  Utility of data  No relevance to individual students  Results do not reflect current students  Needs to make sense  Students mirror teacher attitude  Too much student testing  Teacher buying into data  Unsure if data use beneficial  Quality of instruction  No consistence in teacher use of tools 12
    • 13. Put Your Fears on the Table What concerns you most about using data to make school decisions? Internal? External? Do the following concerns sound familiar? 13
    • 14. “Putting data on the table will damage union negotiations.” Fear of Data “My questions about data will sound silly.” “Will we get sued if we look at student data? What about privacy issues?” “Can we trust the data? What if the numbers are „cooked‟?” “If people know the truth about how our district is doing, we‟ll get pummeled.” 14 “I don‟t understand the data.” “People will take the data out of context to further their own agendas.”
    • 15. Take Away Your Fear  You don’t have to be a statistician  Data are actionable  Data must be viewed in relationship to something else  Data should be used to establish a focus of inquiry 15
    • 16. 16 School Processes Description of School Programs and Processes Perceptions Perceptions of Learning Environment Values and Beliefs Attitudes Observations Enrollment, Attendance, Drop-Out Rate Ethnicity, Gender, Grade Level Demographics Standardized Tests Norm/Criterion-Referenced Tests Teacher Observations of Abilities Authentic Assessments Student Learning Multiple Measures of Data
    • 17. Demographics Perceptions Student Learning Demographics - Gender - Grade - Teacher - Age - Time in Building - Behavior - Attendance - Poverty Level - Racial/ethnic - Socioeconomic - Single Parent - Siblings in household - Free/Reduced Lunch Parental Involvement - Preparedness - Transience -Out of school experiences Community Support - Programs e.g., Head Start - Services e.g, FIA Opportunity to Learn - Current Offerings - Extra Curricular Activities Teacher quality - Qualifications & Credentials - Instructional Practices - Professional Development - Collective Efficacy - Learning Communities - Professional Affiliations Leadership - Vision, Mission, Goals - Staff Engagement & Perceptions - Parent Engagement & Perceptions - Supervision Practices - Professional Affiliations Resource Allocation - Budget Allocation - Staffing Patterns - Professional Development - Facility Usage/Maintenance - Technology Distribution Results Data (Static Data) - MEAP/MME - ACT - AP Testing - District Benchmark Assessments - Standardized Assessments - Graduation Rate - Postgraduate Follow-up Process Data (Real-Time Data) - Instructional Strategies - Classroom Assessments - Instructional Time on Task - Behavioral Referrals - Books - Writing Samples - Homework Assigned/Completed - Positive Parent Contacts School Processes Perception Data - Student Engagement - Student motivation - Student perceptions of success - Values - Beliefs - Culture - Attitudes - Observations 17
    • 18. 1. My school has a written vision that focuses on student achievement. Yes No I Don’t Know 6. My school is willing to explore ways to use data to measure progress. Yes No I Don’t Know 2. My school has a general awareness about why data are significant. Yes No I Don’t Know 7. Everything my school does aligns with our vision. Yes No I Don’t Know 3. My school has a mission statement that reflects core values and beliefs. Yes No I Don’t Know 8. My school knows that staffs role is using data to improve student achievement. Yes No I Don’t Know 4. My school agrees data shows evidence of progress in achieving student goals. Yes No I Don’t Know 9. My school uses data to set goals? Yes No I Don’t Know 5. My school has stated, measurable goals that are tied to our vision. Yes No I Don’t Know 10. My school make decisions based on data based research? Yes No I Don’t Know Are We Ready To Use Data More Effectively? 18
    • 19. Types of data •Input •Process •Outcome •Satisfaction *Set and assess progress toward goals *Address individual or group needs *Evaluate effectiveness of practices *Assess whether client needs are being met *Reallocate resources in reaction to outcomes *Enhance processes to improve outcomes Information Actionable knowledge District School Classroom SOURCE: Marsh, J. A., Pane, J. F., and Hamilton, L. S. (2006). Making sense of data-driven decision making. Rand Corporation. p. 3. Conceptual Framework for Data Use 19
    • 20. 20
    • 21. Establishing the Bridge for Student Improvement Collaborative Inquiry 21
    • 22. Enabling Collaborative Work • Schools have an abundance of data. There is the propensity of school officials to relegate the technical work in organizing data to a small group of individual – i e., principal, principal and select teachers, or data specialist. • This responsibility needs to be shared among all teachers, and ideally, among all members of the school community. • It is quite apparent that when people are involved in analyzing and interpreting data collaboratively, they become more invested in the school improvement efforts that are generated out of those discussions. • The more people involved in data analysis and interpretation, the more effective the resulting school improvement efforts will be. 22
    • 23. John Dewey 23 What the best and wisest parent wants for his own child, that must the community want for all of our children.
    • 24. It’s Easy to Get Lost in the Numbers 24 63542 63542 37620 60915 5629098625 8762987620 980098 89365and forget that the numbers represent the hope and future of real children with strengths as well as challenges, each deserving the kind of education we want for our very own children
    • 25. Bridging the Data Gap  Imagine two shores with an river in between.  On one shore are data—the masses of data now overwhelming schools:  On the other shore are the aspiration, intention, moral assurance, and directive to improve student learning and close repetitive achievement gaps.  course-taking patterns  attendance data  survey data  and on and on  graduation rates  state test data sliced and diced  local assessments  demographic data  dropout rates 25
    • 26. 26 I saw this new reading program at the State conference, let’s try it, it can’t hurt! If we put more resources into “Bubble Kids” our scores will improve It is evident that those kids cannot learn as efficiently as others ? ? ?
    • 27. 27 Build and identify the parts of a bridge that is needed to get Data to the Results?
    • 28. Collaborative Inquiry Is The Bridge • Schools know that they have to improve • But they often do not know how to improve • Collaborative inquiry is the how • As collaborative inquiry grows, schools shift away from traditional data practices and toward those that build a high- performing culture of data use • When engaged in collaborative inquiry, Data Teams investigate the current status of student learning and instructional practice and search for successes to celebrate and amplify. 28
    • 29. Setting the Stage for Collaborative Inquiry Participants’ Activity: • In this particular activity, participants will discuss the type of external and internal data they use in their schools. After this participants will identify trends associated with these administrations. • On post- it notes, participants will be asked to make the following observations and report out in groups the following questions: 29
    • 30. Group Activity Question: 1. Is there one particular data type (external or internal) used more often than the other? If so, why? 2. What decisions are made by the use of external and internal data tools? Who make these decisions (by data type)? 3. To what extent is there a close relationship between the gaps in student learning, as identified by the data types, and the initiatives that were developed? 4. What challenges are you facing implementing the initiative in your school? 5. To what extent is the initiative producing the intended results you originally sought? How do you know? What data are you using? If you are not getting the desired results, what are you doing about it? 30
    • 31. Demographics Perceptions Student Learning Demographics - Gender - Grade - Teacher - Age - Time in Building - Behavior - Attendance - Poverty Level - Racial/ethnic - Socioeconomic - Single Parent - Siblings in household - Free/Reduced Lunch Parental Involvement - Preparedness - Transience -Out of school experiences Community Support - Programs e.g., Head Start - Services e.g, FIA Opportunity to Learn - Current Offerings - Extra Curricular Activities Teacher quality - Qualifications & Credentials - Instructional Practices - Professional Development - Collective Efficacy - Learning Communities - Professional Affiliations Leadership - Vision, Mission, Goals - Staff Engagement & Perceptions - Parent Engagement & Perceptions - Supervision Practices - Professional Affiliations Resource Allocation - Budget Allocation - Staffing Patterns - Professional Development - Facility Usage/Maintenance - Technology Distribution Results Data (Static Data) - MEAP/MME - ACT - AP Testing - District Benchmark Assessments - Standardized Assessments - Graduation Rate - Postgraduate Follow-up Process Data (Real-Time Data) - Instructional Strategies - Classroom Assessments - Instructional Time on Task - Behavioral Referrals - Books - Writing Samples - Homework Assigned/Completed - Positive Parent Contacts School Processes Perception Data - Student Engagement - Student motivation - Student perceptions of success - Values - Beliefs - Culture - Attitudes - Observations 31
    • 32. BREAK 15 Minutes 32
    • 33. Creating A Data Team A data team is a team that meets regularly to analyze data and make educational decisions to improve student achievement. 33
    • 34. THE DATA TEAM PROCESS 1. Collect and Chart Data 2. Analyze Data and Prioritize Needs 3. Establish SMART Goals 4.Select Instructional Strategies 5. Determine Results Indicators Source: Allison, E. et al.. (2010). Data teams. Lead + Learn Press.: Englewood, CO. 6. Monitor and Evaluate Results
    • 35. Data Teams The data team members must: • Be seen as leaders. • Be willing to learn about data in depth. • Must have skills in collaboration, communication, and leadership. The functions of the data team are: • To develop expertise on data. • To share data information with staff members of the schools. • To assist is setting up support systems at the schools. • To create and complete action plans based on the data. The data team members need: • Information about systems to support data based decision making. • Training in the problem solving process. Data team members should be expected to: • Meet regularly as a team to develop a plan to establish using data to improve student performance. • Have conversations about student achievement. • Show examples of successful schools. • Set up a system that supports the collection and use of student data. • Help staff members understand how to use student data to guide decision making. • Work to secure commitment from staff 35
    • 36. D4SS Data Analysis Activity 36 http://www.data4ss.org User Name: demo_test1 Pass Word: fall_01
    • 37. Data Narrative Statements Criteria Data Narrative Statements are objective statements of FACT about the school data They: 1. Represent student achievement, demographics, school programs, school processes, and stakeholder perceptions 2. Communicate a SINGLE idea 3. Are clear and concise – written in sentences or phrases 4. Describe the data; they do not evaluate the data! 5. MUST stand alone; they do not require the data source to accompany them in order to be understandable. 37
    • 38. Data Narrative Statements Do they meet criteria from Previous Slide? Narrative Statement 1 2 3 4 5 1- Spring 2010 Math Assessment shows that our girls do slightly better than the boys. 2- The Spring 2010 Math Assessment shows that 20.5% of our 11th grades students were proficient and 79.5% were not. 3- The Spring 2010 Math Assessment shows that we really need a new math series. 4- In 2009-2010 21.4 % of all our students taking the Math Assessment are proficient; while 20.5% of our 11th graders are proficient and 33.3% of our 12th graders are proficient. 5- Parents do not like the math program. 38
    • 39. Year: _________ Building: _________________________ Which grade level(s) is not meeting the criteria for grade level proficiency? What do we need to know more about? % Proficient % Proficient % Proficient AYP Target (see slide 47 for AYP Target) % Proficient AYP Target (see slide 47 for AYP Target) Content Area Reading Writing Total ELA ELA Math Math Overall Building – All Students Grade 3 Grade 4 Grade 5 Grade 6 Grade 7 Grade 8 39 MEAP Building: Content Analysis
    • 40. MEAP Overall Building Sub-Group Level Achievement Analysis Grade Number of Students % Proficient Number of Students % Proficient Number of Students % Proficient AYP Target (see slide 47 for AYP Target) Number of Students % Proficient AYP Target (see slide 47 for AYP Target) Content Area Reading Reading Writing Writing Total ELA Total ELA ELA Math Math Math American Indian or Alaska Native Black or African American Hispanic or Latino White Asian American, Native Hawaiian or other Pacific Islander Multiracial Economically Disadvantaged Students with Disabilities Limited English Proficient Non AYP- Migrant 40
    • 41. MEAP Sub-Group Analysis Sub-Group Level Achievement: Choose sub-group for analysis Year ___________ Group: ____________________ Grade Number of Students % Proficient Number of Students % Proficient Number of Students % Proficient AYP Target (see slide 47 for AYP Target) Number of Students % Proficient AYP Target (see slide 47 for AYP Target) Content Area Reading Reading Writing Writing Total ELA Total ELA ELA Math Math Math Building 3 4 5 6 7 8 41
    • 42. Date: _______________________________ Building: ____________________________ Data Team Members: __________________________________________________________________________________ ______________________________________________________________________________________________________ __________ 42 Narrative Statement: MEAP Data Narrative Statements for Sub-Group Analysis
    • 43. Year: _________ Building: _________________________ MME Building: Content Area Proficiency In which subject area(s) is your building not meeting the criteria for proficiency? What do you need to know more about? % Proficient % Proficient % Proficient AYP Target (see slide 47 for AYP Target) % Proficient AYP Target (see slide 47 for AYP Target) Content Area Reading Writing Total ELA ELA Math Math Overall Building – All Students Grade 11 Grade 12 43
    • 44. MME Overall Building Sub-Group Level Achievement Analysis Grade Number of Students % Proficient Number of Students % Proficient Number of Students % Proficient AYP Target (see slide 47 for AYP Target) Number of Students % Proficient AYP Target (see slide 47 for AYP Target) Content Area Reading Reading Writing Writing Total ELA Total ELA ELA Math Math Math American Indian or Alaska Native Black or African American Hispanic or Latino White Asian American, Native Hawaiian or other Pacific Islander Multiracial Economically Disadvantaged Students with Disabilities Limited English Proficient Non AYP- Migrant 44
    • 45. MME Sub-Group Analysis Sub-Group Level Achievement: Choose sub-group for analysis Year ___________ Group: ____________________ Grade Number of Students % Proficient Number of Students % Proficient Number of Students % Proficient AYP Target (see slide 47 for AYP Target) Number of Students % Proficient AYP Target (see slide 47 for AYP Target) Content Area Reading Reading Writing Writing Total ELA Total ELA ELA Math Math Math Building 45
    • 46. Date: _______________________________ Building: ____________________________ Data Team Members: ___________________________________________________________________________________ ______________________________________________________________________________________________________ __________ 46 Narrative Statement: MME Data Narrative Statements for Sub-Group Analysis
    • 47. Michigan Annual AYP Objectives 47
    • 48. Reflections On Today’s Session 1. What do you remember from today's session (scenes, events, and conversations)? 2. What words are still ringing in your ears? 3. What image captures for you the emotional tone of today's session? 4. What is a key insight from today's session? 5. What name would you call today's session? (Try a poetic title that captures your responses.) 48
    • 49. End Session 1 49
    • 50. 50 Continuous ImprovementData UseCollaborationLeadership Capacity TrustCultureEquity
    • 51. PANEL DISCUSSION WITH DATA EXPERTS Kathryn Parker Boudett http://bcove.me/v4ccqy6q Joel Klein http://bcove.me/byt742or Rudy Crew http://bcove.me/emn3jz77 Martha Greenway http://bcove.me/4cf0bkzq Aimee Guidera http://bcove.me/trsldt2h Dan Katzir http://bcove.me/gy3wbiac http://www.edweek.org/ew/section/video-galleries/april10-event-data.html 51
    • 52. SmartGoals Data Teams Summative Assessments Perceptual Demographic Formative Assessment s Alignment QuestionsandInquiry Process Revised Instructional Strategies Revised Instructional Strategies Data Team s Data Feedback Model DistrictWrittenand TaughtCurriculum DataIntersection Analysis
    • 53. 53
    • 54. 54
    • 55. 55
    • 56. 56
    • 57. 57
    • 58. 58
    • 59. 59
    • 60. 60
    • 61. 61
    • 62. 62