1. Scalable Student Data Solution that Leads to Customized Student Programming
Presenters:
Jeff Dickert
Agency Administrator
Jeff Carew
​Managing Director  ​

2. Data Literacy

3. Data Literacy DATA LITERACY
Data literacy is knowing how, when, and why to examine student data to drive continuous improvement. Data literate educators:
Understand data;
Are confident working with data both independently and collaboratively; and
Embed data-driven decision-making into continuous improvement processes.
Wisconsin Department of Public Instruction – RVSD October 2016 – pg. 1

4. Data Literacy DATA LITERACY
Data literacy is knowing how, when, and why to examine student data to drive continuous improvement. Data literate educators:
Understand data;
Are confident working with data both independently and collaboratively; and
Embed data-driven decision-making into continuous improvement processes.
Wisconsin Department of Public Instruction – RVSD October 2016 – pg. 1

5. Data Literacy DATA LITERACY
Data literacy is knowing how, when, and why to examine student data to drive continuous improvement. Data literate educators:
Understand data;
Are confident working with data both independently and collaboratively; and
Embed data-driven decision-making into continuous improvement processes.
Use evidence to inform practice, adjust instruction, and make decisions to advance student learning, whether through classroom practices or policy decisions.
Use data to establish, adjust, and evaluate strategic goals.
Use the most appropriate data for the decision at hand, taking into account validity and reliability.
Embed the data inquiry process into an ongoing cycle of continuous improvement (e.g. the SLO process).
Transform data into information that can be applied strategically to improve student outcomes, such that the data leads to action-oriented next steps.
Wisconsin Department of Public Instruction – RVSD October 2016 – pg. 1

6. Data Literacy DATA LITERACY
Data literacy is knowing how, when, and why to examine student data to drive continuous improvement. Data literate educators:
Understand data;
Are confident working with data both independently and collaboratively; and
Embed data-driven decision-making into continuous improvement processes.
Use evidence to inform practice, adjust instruction, and make decisions to advance student learning, whether through classroom practices or policy decisions.
Use data to establish, adjust, and evaluate strategic goals.
Use the most appropriate data for the decision at hand, taking into account validity and reliability.
Embed the data inquiry process into an ongoing cycle of continuous improvement (e.g. the SLO process).
Transform data into information that can be applied strategically to improve student outcomes, such that the data leads to action-oriented next steps.
Wisconsin Department of Public Instruction – RVSD October 2016 – pg. 1

7. Data Literacy
1990’s & 2000’s
DATA RETREATS

9. Data Literacy
1990’s & 2000’s
DATA RETREATS

10. Data Literacy
1990’s & 2000’s
DATA RETREATS
Never Happened!!

11. Data Literacy 1990’s & 2000’s DATA RETREATS SCHOOL YEAR SUMMER
Used One-Time Stagnant Data:
Standardized Tests Attendance Data
District Reading Test Behavioral Data
Progress Tests (MAPS) Gender/Ethnicity Data

12. Data Literacy
1990’s & 2000’s
DATA RETREATS
SCHOOL YEAR
SUMMER

13. Data Literacy
1990’s & 2000’s
DATA RETREATS
SCHOOL YEAR
SUMMER

14. Data Literacy
1990’s & 2000’s
DATA RETREATS
SCHOOL YEAR
SUMMER

15. Data Literacy
1990’s & 2000’s
DATA RETREATS
SCHOOL YEAR
SUMMER

16. Data Literacy
1990’s & 2000’s
DATA RETREATS
SCHOOL YEAR
SUMMER

17. Data Literacy 1990’s & 2000’s DATA RETREATS SCHOOL YEAR SUMMER
Rarely Made it to the Classroom!!
Rarely Changed Instruction!!

18. Data Literacy
1990’s & 2000’s
DATA RETREATS
SCHOOL YEAR
SUMMER

19. Data Literacy
1990’s & 2000’s
DATA RETREATS
SCHOOL YEAR
SUMMER

20. Data Literacy
Join The Future – 2020’s
Data Inquiry Model

21. Data Literacy Join The Future – 2020’s Data Inquiry Model
For those that believe that all of the data we collect can and will be used to make daily educational decisions in our classrooms that will improve student success and the student experience!!

22. Data Literacy
Join The Future – 2020’s
Data Inquiry Model

23. Data Literacy
Join The Future – 2020’s
Data Inquiry Model

24. Data Literacy
Join The Future – 2020’s
Data Inquiry Model

25. Data Literacy
Join The Future – 2020’s
Data Inquiry Model

26. Data Literacy
Join The Future – 2020’s
Data Inquiry Model

27. Data Literacy Join The Future – 2020’s Data Inquiry Model
1990’s & 2000’s
DATA RETREATS
Move To the 2020’s
Real Time Student Data
Real Time Data Analysis
Daily Influence on Lessons & Learning

28. Data Literacy
Join The Future – 2020’s
Data Inquiry Model

29. Data Literacy
Join The Future – 2020’s
Data Inquiry Model

30. Data Literacy Join The Future – 2020’s Data Inquiry Model
Real Time Student Data
Real Time Data Analysis
Daily Influence on Lessons & Learning
One Stop Data Shop!!

31. Data Literacy Join The Future – 2020’s Data Inquiry Model
Real Time Student Data
Real Time Data Analysis
Daily Influence on Lessons & Learning
Daily Influence on Lessons & Learning
Universal Design Instruction
Differentiated Instruction
Tier 2 Instruction
Tier 3 Instruction
Daily Activities
Double Down Instruction
Personalized Learning

35. Forecast5 builds data discovery and decision support tools for K-12 education leaders.
Working with 1,200 school districts in 25 states.
Forecast5 is proud to partner with AESA.

36. Metric definition Data collection ESA Support Research based
State defined
Regional ideas 
Data collection
Uniform (efficiency)
ESA Support
Action / monitoring
Collaboration
Report cards

37. state accountability - 2013

38. state accountability - 2016

39. strategic investment

40. student growth

41. attendance dashboard

42. student growth

43. TO JOIN THE DATA LITERACY 2020 MOVEMENT Contact:
Jeff Dickert
Agency Administrator
Jeff Carew
​Managing Director ​

44. Grade Book Analytics and Dashboards
Guided Analysis Example
Forecast5 Analytics, Inc. Copyright 2017

45. Grade Book Update and Grade Distribution Analytics Example
District Level Dashboard
The dashboard to the right represents the District Level view of the distribution of grades after a reporting period. Several disaggregation's of the data are provided building and demographic/student group.
The next slide will show a Building View of the data, which can be accessed by clicking on of the school bars (red arrow).
Forecast5 Analytics, Inc. Copyright 2017

46. Grade Book Update and Grade Distribution Analytics Example
Building Level Dashboard
The dashboard to the right represents the Building Level view of the distribution of grades after a reporting period. This dashboard includes Pass/Fail rates by Department within the Building.
The next slide will show a Department View of the data, which can be accessed by clicking on of the department bars (red arrow).
Forecast5 Analytics, Inc. Copyright 2017

47. Grade Book Update and Grade Distribution Analytics Example
Department Level Dashboard
The dashboard to the right represents the Department Level view of the distribution of grades after a reporting period. This dashboard includes Pass/Fail rates by Class, as well as, breakdowns by demographics.
The next slide will show a Class View of the data, which can be accessed by clicking on of the class labels (red arrow).
Forecast5 Analytics, Inc. Copyright 2017

48. Forecast5 Analytics, Inc. Copyright 2017
Grade Book Update and Grade Distribution Analytics Example
Class Level Dashboard
The dashboard to the right represents the Class Level view of the distribution of grades after a reporting period. This dashboard includes Pass/Fail rates by Instructor, as well as, breakdowns by demographics within the Class subject of Geometry.
The next slide will show an Instructor View of the data, which can be accessed by clicking on of the Instructor label (red arrow).
Forecast5 Analytics, Inc. Copyright 2017

49. Grade Book Update and Grade Distribution Analytics Example
Instructor Level Dashboard
The dashboard to the right represents the Instructor Level view of the distribution of grades after a reporting period. This dashboard includes the grade distribution for that Instructor, as well as a class list and breakdowns by demographics.
The next slide will show a Student View of the data, which can be accessed by clicking on of the Student label bars (red arrow).
Forecast5 Analytics, Inc. Copyright 2017

50. Grade Book Update and Grade Distribution Analytics Example
Student Level Dashboard
The dashboard to the right represents the Student Level view with their grades across all classes they have taken and some other metrics including attendance rate.
This Guided Analysis for the Grade Book Update can be customized in almost any way to accommodate the needs of a district management team, accelerate insights or inform resource allocation decisions.
Forecast5 Analytics, Inc. Copyright 2017