1. June, 2013
SECTION 1
Executive Summary
1-1
The Analysis Portal includes a variety of Key Performance Indicators (KPI’s) that were developed
specifically for K-12 education customers, using Tableau with a variety of data sources:
• Student Information & Performance Data
• Financial Information
• Human Resources Information
• Survey Data
With the Analysis Portal, customers can quickly learn best practices and tips / techniques for developing
powerful visualizations with K-12 information. These templates can be modified to match district data
sources, and district requirements quickly and easily, allowing you to have results available for your
information consumers in minutes vs. weeks or
months with other systems.
GROWTH MODEL DEVELOPMENT
We propose development of the Growth Model
detailed specifications with a three (3) step
approach, including:
I. Growth Model Requirements Discovery
/ Needs Analysis
II. Data Visualization Technical
Requirements and Design
Specifications
III. Data Visualization Implementation &
Documentation
Growth Model Requirements Discovery / Needs Analysis Methodology
The methodology for completing the Growth Model Discovery / Needs Analysis phase is based on onsite
observations and structured interviews with school board members, school district and school-based
leaders, teachers and parents.
We will provide a structured checklist of discussion points to facilitate the
onsite meetings (expected to be ½ day to 2 days per school) and to facilitate
comparison of requirements to identify common elements desired by all
schools. Deliverables will be provided with a summary of requirements for
each school, along with a comparison of requirements between schools to
identify common and unique requirements.
Deliverables will be
provided with a
summary of
requirements for each
school, along with a
comparison of
requirements between
schools to identify
common and unique
requirements.

2. June, 2013
SECTION 1
Executive Summary
1-2
Data Visualization Technical Requirements and Design Specifications
A key element of data analysis is the ability to disaggregate data based on district needs, such as by
school, year, grade, class, and teacher. In addition, demographic elements (such as gender, race /
ethnicity, meal status, LEP status, SPED status, etc.) are normally desired to perform subgroup analysis.
Supporting information, such as attendance, behavior / discipline,
student grades are normally included as well as assessment scores in
the data sets to analyze underlying factors that affect student learning.
Once the initial requirements have been defined (what data needs to
be analyzed), technical requirements and design specifications will be
provided, identifying the data elements to be maintained in the data
warehouse, and formats will be provided to each school for extracting
the desired data (in common formats) from their student information
system(s), data files from testing vendors, and other sources.
Import routines will be provided to systematically load the data initially, and refresh the data warehouse
data periodically throughout the year.
Separate databases will be provided for each school (with unique security rights), as well as a
comparison database which aggregates data from the elementary feeder schools, omitting personally
identifiable information. This approach allows for comparisons to be made across schools by grade level
and other categories, while still ensuring student privacy.
Data visualizations and dashboards will be
designed as well, summarizing the data
desired along with disaggregators available
for subgroup analysis and drill-down into
detailed data. Security rights will be setup to
provide school building level, and teacher
level access to the data for the district, along
with aggregations across schools and
comparisons between schools, comparisons
to state level averages, etc.
Data Visualization Implementation & Documentation
The final step will be development and deployment of the data warehouse and data visualizations /
dashboards, hosted at our hosting center. Secure, web-based access will be provided to authorized
individuals at each school, available via the Internet 24 x 7 x 365, using web-browser enabled devices,
such as PC workstations, Mac workstations, iOS devices (iPads) and Android tablets.
Supporting information,
such as attendance,
behavior / discipline,
student grades are
normally included as
well as assessment
scores in the data sets
to analyze underlying
factors that affect
student learning.

3. June, 2013
SECTION 1
Executive Summary
1-3
Training will be provided (a combination of
onsite, web-based, and recorded videos) for
end users and system administrators at each
school.
Data Teams will be established at each school
to assist each school with understanding the
data and the visualization tools to use the
information to evaluate programs and
improve student learning using the data
provided.
In working with other districts and similar
scenarios, one common theme that occurs is that as users have more and better information available,
they continue to learn and ask more sophisticated questions about their data. Accordingly, a District
Data Team will be asked to submit requests for enhancements to data visualizations, new / improved
dashboards, etc.
We will facilitate these discussions and train staff members at each school on making changes to the
data visualizations and dashboards over time, with our staff serving as a backup resource to make any
changes that relate to data available in the data warehouse and other technical / complex changes
desired, loading software updates, etc.

4. June, 2013
SECTION 2
Growth Model Requirements
1-4
Introduction
In a time of increasing accountability, education leaders and teachers understand the importance of
growth models and the use of data to drive instruction and increase student achievement. Often,
educational leaders point to school culture built around data collection, interpretation, and informed
decision making as essential for improving student achievement. School leaders cite an emphasis on
targeted assessments for measuring academic ability, establishing learning goals, and using tools to
analyze data and visually communicate results to the education community. Accomplishing these
objectives requires timely, accurate, and up-to-date data and tools that can translate data into
understandable graphic representations.
As data systems evolve to include increasing amounts and different types of data, a strong set of
policies and procedures must be implemented to guide the way in which data are managed and used.
Data that is collected, stored, analyzed, and/or disseminated in a school district must be managed from
an enterprise perspective.
Data management establishes the foundation for collecting, managing, and reporting high quality and
trustworthy data. It includes the standards, policies, and procedures for managing data, and it is the
reference to which all school district personnel should go when a decision needs to be made regarding
the management of data within the organization.
Scope of Work
We propose the following three-phase Student Growth Model development process:
I. Growth Model Requirements Discovery / Needs Analysis
II. Data Visualization Technical Requirements and Design Specifications
III. Data Visualization Implementation & Documentation

5. June, 2013
SECTION 2
Growth Model Requirements
1-5
Below we have outlined basic steps to follow for each component of the RFP, based on our
understanding of your requirements at this time. These may change as we learn more about specific
requirements and needs during the initial phase of the project, but include steps we have used
successfully with other similar implementations.
Growth Model Requirements Discovery / Needs Analysis Methodology
The methodology for completing the Growth Model Discovery / Needs Analysis phase is based on onsite
observations and structured interviews with school board members, school district and school-based
leaders, teachers, and parents.
Growth Model Requirements
1. Provides the district with advice/recommendations for projecting individual student academic
growth that takes into account previous levels of knowledge and other relevant student level
variables:
 Interviews with school district and school-based leaders to identify and document
which, if any, growth models (Simple Growth, Improvement, Performance Index,
Growth to Proficiency, or Value-added) have been selected and/or implemented.
 Interviews with school district and school-based leaders and onsite observation to
identify and document current classroom grading practices, formative and summative
assessments (by grade and subject), data analyses procedures, and reporting protocols.
 Interviews with school district and school-based leaders and onsite observation to
identify and document data management policies and procedures.
 Interviews with school district and school-based leaders and onsite observation to
identify and document other relevant student variables (i.e., gender, special services,
attendance, behavior, etc.) currently used and/or anticipated for disaggregating,
analyzing, and projecting student academic growth.
 Identification and documentation of curriculum alignment and assessment articulation
policies and procedures horizontally across grade spans and vertically to the high school.
 Interviews with education community members (board of education members,
teachers, and parents) to identify and document growth model implementation and
communication plan expectations.

6. June, 2013
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Growth Model Requirements
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 Provide documentation comparing existing growth model expectations with existing
assessment protocols as well as other relevant student variables and recommend
strategies for calculating and communicating individual and aggregate student academic
growth data.
 Provide documentation on existing data management practices and provide
recommendations to maximize data consistency, accuracy, and availability.
2. Indicates how measures of individual student growth function in district, school and classroom
accountability measures:
 Interviews with school district and school-based leaders and teachers to identify and
document how current measures of student growth are collected, analyzed,
disseminated, and used at school district, building, and classroom levels.
 Interviews with school district and school-based leaders to identify and document
current data management policies and procedures. (Establishing up-to-date, accurate,
and complete data for measuring and projecting academic progress)
 Provide documentation detailing how classroom grades, formative and summative
assessments, and other student variables are used to measure and report on student
growth at district, school, and classroom levels.
 Provide recommendations for policies and procedures to ensure consistent, accurate,
up-to-date, and complete data for decision making.
3. Describes how measures of individual student growth can be used to evaluate programs:
 Identify appropriate measurements of student growth associated with specific
programs, and utilize data to set goals and evaluate program(s) at the end of each year
to determine the success of each program and whether or not the program should be
continued or replaced.
 Prepare and conduct workshops at multiple levels: district leadership, building
leadership, and classroom teacher to demonstrate how Tableau data visualizations are
interpreted and translated into decision-making actions at each level.
4. Allows for flexibility in the disaggregation of student data based on district needs:
 The data analysis and visualization tool proposed will provide flexibility for the
disaggregation of student data based on school district, building, teacher, and education
community needs.

7. June, 2013
SECTION 2
Growth Model Requirements
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 The tool will provide for disaggregation of student data at the development level by
selecting the disaggregation variables that will be available to end users.
 The tool will provide an easy-to-use interface so that end users will be able to model the
data by selecting (with the click of a mouse button) disaggregation variables.
Disaggregation variables may include but not be limited to:
o Year
o Assessment
o School
o Grade
o Teacher
o Gender
o Ethnicity
o Attendance
o Behavior
o Special Services (IEP, 504, G&T, etc.)
o Meal Status
o LEP

8. June, 2013
SECTION 2
Growth Model Requirements
1-8
5. Allows the district the ability to modify and/or add customized components as necessary (e.g.,
common formative assessment data, customized reports, etc.):
 The data management solution proposed will provide the ability for the district to
modify or add customized components as required to the data base and to the reporting
tool.
 The data analysis and visualization tool will provide the district with the ability to modify
the assessment data and customize reports with an easy-to-use interface that allows
users to model data results with the click of a mouse button.
6. Provides correlation statistics between common assessments, standardized assessments and
State assessments:
 The data analysis and visualization tool will provide graphics presenting the correlation
between:
o State assessment results correlated with aggregated consortium results
o State assessment results correlated with disaggregated consortium school
district results
o State assessment results correlated with individual schools

9. June, 2013
SECTION 2
Growth Model Requirements
1-9
o Classroom grades correlated with State assessment results
o Classroom grades correlated with common assessments
o Classroom grades correlated with standardized assessments
o Disaggregation of multiple same grade classes correlation with assessment
results and displayed on one graphic visualization

10. June, 2013
SECTION 2
Growth Model Requirements
1-10
7. Includes a communication plan comprehensible to all stakeholders to include charting and
graphing functions integrated into the system:
 Interviews with school district and school-based leaders and education community
members to identify and document student assessment results information
expectations.
 Development of a communication plan that includes identifying visualization access
defined by role. For example, district administrators can access data for all schools;
principals may access data for their school, teachers may access data for students in
their classes.
8. Clearly define all technical requirements for district utilization (e.g., hardware, network, file
format, software interface, etc.):
 Data elements to be used in the data warehouse will be defined, including student
demographics (examples listed above in item 4), teacher rosters, and other related
information (e.g. attendance data, behavior data, program participation, intervention
strategies, individualized learning plans, etc.).