Scalable Student Data Solution that Leads to Customized Student Programming Presenters: Jeff Dickert Agency Administrator Jeff Carew Managing Director
Data Literacy
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 http://dpi.wi.gov/strategic-assessment/foundational-resources/charts-terms
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 http://dpi.wi.gov/strategic-assessment/foundational-resources/charts-terms
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 http://dpi.wi.gov/strategic-assessment/foundational-resources/charts-terms
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 http://dpi.wi.gov/strategic-assessment/foundational-resources/charts-terms
Data Literacy 1990’s & 2000’s DATA RETREATS
Data Literacy 1990’s & 2000’s DATA RETREATS
Data Literacy 1990’s & 2000’s DATA RETREATS Never Happened!!
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
Data Literacy 1990’s & 2000’s DATA RETREATS SCHOOL YEAR SUMMER
Data Literacy 1990’s & 2000’s DATA RETREATS SCHOOL YEAR SUMMER
Data Literacy 1990’s & 2000’s DATA RETREATS SCHOOL YEAR SUMMER
Data Literacy 1990’s & 2000’s DATA RETREATS SCHOOL YEAR SUMMER
Data Literacy 1990’s & 2000’s DATA RETREATS SCHOOL YEAR SUMMER
Data Literacy 1990’s & 2000’s DATA RETREATS SCHOOL YEAR SUMMER Rarely Made it to the Classroom!! Rarely Changed Instruction!!
Data Literacy 1990’s & 2000’s DATA RETREATS SCHOOL YEAR SUMMER
Data Literacy 1990’s & 2000’s DATA RETREATS SCHOOL YEAR SUMMER
Data Literacy Join The Future – 2020’s Data Inquiry Model
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!!
Data Literacy Join The Future – 2020’s Data Inquiry Model
Data Literacy Join The Future – 2020’s Data Inquiry Model
Data Literacy Join The Future – 2020’s Data Inquiry Model
Data Literacy Join The Future – 2020’s Data Inquiry Model
Data Literacy Join The Future – 2020’s Data Inquiry Model
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
Data Literacy Join The Future – 2020’s Data Inquiry Model
Data Literacy Join The Future – 2020’s Data Inquiry Model
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!!
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
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.
Metric definition Data collection ESA Support Research based State defined Regional ideas Data collection Uniform (efficiency) ESA Support Action / monitoring Collaboration Report cards
state accountability - 2013
state accountability - 2016
strategic investment
student growth 2014 - 2016
attendance dashboard
student growth 2014 - 2016
TO JOIN THE DATA LITERACY 2020 MOVEMENT Contact: Jeff Dickert Agency Administrator 920-617-5612 jdickert@cesa7.org Jeff Carew Managing Director 920-000-000 jcarew@forecast5analytics.com
Grade Book Analytics and Dashboards Guided Analysis Example Forecast5 Analytics, Inc. Copyright 2017
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
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
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
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
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
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