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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.

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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.

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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.

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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

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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.

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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.

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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

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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

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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

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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.).