Data Driven Results
Guidance for Determining Teacher Designations

To support accurate analysis and transparent designation decisions, TEA has created a step-by-step approach recommended for districts to follow. By completing each step, districts can ensure their designation decisions are data driven, aligned with their local system, and applied consistently across all eligible teaching assignments.

Designation Determination Framework PDF

Designation Determination Framework

To demonstrate how to engage with the framework using the Designation Determination Tool, we will follow District A’s decision-making process throughout the framework.

Designation Determination Tool

Review Data for Accuracy and Enter Data

Pre-work Review Data for Accuracy and Enter Data

Districts must ensure all data has been collected and checked for accuracy and completion. Using multiple teacher identifiers (like a local or unique ID and date of birth) in district data management systems reduces errors in tracking teacher identification, student linkages, as well as observation and student growth data. Once the file is accepted, TEA cannot modify district’s data submission. Many districts consult with a data analyst or technology systems manager for assistance with data compilation and analysis.

TIA Designation Determination Tool: “Data Entry” tab (“Multiple Student Growth Measure” and “Optional Component Data” tabs if applicable)

Once the data has been reviewed for accuracy, enter the data in the Data Entry tab (and “Multiple Student Growth Measure” and “Optional Component Data” tabs if applicable).

For every category, determine the weights for each component

Step 1 For every category, determine the weights for each component

TIA Designation Determination Tool: “Weights” tab

Districts can establish any weights for their components that total to 100% when combined. Weights are determined by districts’ priorities, alignment to strategic plans, among other determining factors. Here are a few examples:

  • District Z is prioritizing accurate appraisal ratings and investing more time in their calibration protocols. They have decided to weigh teacher observation at 60% and student growth at 40%.
  • District Y has heard overwhelmingly from their stakeholders that student growth is more demonstrative of a teachers’ performance as opposed to appraisal ratings. They have decided to weigh teacher observation at 40% and student growth at 60%.
  • District X believes that some growth measures hold stronger weight than others, so there is a mix of weights in each category. Category 1 weighs teacher observation and student growth at 50% while Category 2 and 3 weight teacher observation at 45% and student growth at 55%.

Example: District A’s stakeholders believe strongly that both their teachers’ appraisal ratings and student growth scores evenly represent teacher performance. They have chosen to weigh teacher observation and student growth equally at 50% each for every category.

Teacher Observation
50%
Student Growth
50%
Establish performance standards

Step 2 Establish performance standards

TIA Designation Determination Tool: “Designation Decisions” tab

In this step, districts set the performance standards that determine what level of performance earns each designation level for each component (i.e. teacher observation, student growth, and the optional component if applicable).

Districts may use statewide performance standards or create their own local standards.

Example: District A (an average performing district) uses the statewide performance standards and does not implement an optional component.

AcknowledgedRecognizedExemplaryMaster
Teacher Observation3.53.73.94.5
Student Growth50%55%60%70%

TIA Designation Determination Tool: “Analysis” tab

The “Analysis” tab will provide a table breaking down the designated teacher proportions by teacher observation performance standards and student growth performance standards. These are not final designations.

Note: The visuals below are meant to serve as an illustration for these breakdown proportions and are not available in the tool.

Teacher Observation Performance
Student Growth Performance
Determine the decision-making approach and if the district will use minimum cut points

Step 3 Determine the decision-making approach and if the district will use minimum cut points

Districts often use one of the following three decision-making approaches.

  • A non-compensatory approach sets minimum cut points for specific measures, and any teacher who does not meet those minimums is not eligible for a designation.
  • A compensatory approach values high performance, allowing strengths in one area to offset weaker performance in another.
  • A hybrid approach combines both non-compensatory and compensatory methods. For example, teachers must first meet the minimum criteria to be considered. Then, a compensatory method is then used to weigh multiple data points and determine the final designation level.
Non-Compensatory
Click to enlarge image
Benefits
Forces alignment with statewide performance standards.

Simple to communicate
Costs
Teachers who perform high in one component but lack in another may be awarded no/lower designation
Tool Notes
Uses performance standards and cut points
Compensatory
Click to enlarge image
Benefits
More inclusive of high teacher performance (don’t need to meet minimums in both components)
Costs
Possibly misalign with statewide performance standards on one component if data is inflated

Possibly misalign teachers’ performance across campuses and across assignments

Standards are less transparent
Tool Notes
Only uses performance standards
Hybrid
Click to enlarge image
Benefits
Has some safeguards to meet statewide performance standards

More flexible for nuanced performance
Costs
Possibly misalign with statewide performance standards on one component if data is inflated

Standards are less transparent
Tool Notes
Uses performance standards and cut points

TIA Designation Determination Tool: “Designation Results” and “Analysis” tabs

Examine each approach using the Designation Determination tool, and determine how it would impact the proportions of designations. The “Designation Results” tab will display which teachers received a designation. The “Analysis” tab will provide a dashboard that will breakdown designation proportions by campuses and categories.

Example: District A’s decision-making approaches are illustrated below for the entire district’s submitted data.

Note: Districts will see a funnel chart in the tool. This type of holistic illustration is not currently available in the tool.

Non-Compensatory
Minimums ✓
No performance flexibility
Click to enlarge image
No Designation: 53%
Acknowledged: 12%
Recognized: 11%
Exemplary: 16%
Master: 8%
Compensatory
No minimums
Performance flexibility ✓
Click to enlarge image
No Designation:61%
Acknowledged: 10%
Recognized: 8%
Exemplary: 16%
Master: 5%
Hybrid
Minimums ✓
Performance flexibility ✓
Click to enlarge image
No Designation: 55%
Acknowledged: 16%
Recognized: 10%
Exemplary: 10%
Master: 9%

Districts that use different weights for categories will need to examine each category separately. However, only one decision making approach should be used for an entire system.

Districts can analyze the data and decide which decision-making approach best fits their system. Questions to consider before making a final decision include:

  • Which approach is identifying my most effective teachers?
  • Which approach aligns with previous stakeholder engagement?
  • Am I using cut point minimums?
  • Do my performance standards, weights, or cut points need to be adjusted?
  • How do these designations align with implied Texas VAM designations?
  • What are the data validation and stakeholder engagement impacts of adjustments to my system?
  • Can I justify any changes from what has been previously shared with stakeholders?
  • Is this a change I can make now, or do I need this for next year?

It is also important to keep in mind what can and cannot be changed after the data has been collected but before the data has been submitted:

Districts can


Change the weighting of approved components. Ex: the system application weighted teacher observation at 30% and student growth at 70%. The LEA can change weighting to 50/50.

Remove other optional, non-statutory components. Ex: attendance, student surveys

Adjust the standard at which a teacher is able to earn a designation

Adjust or establish a minimum requirement to earn a designation

Districts cannot


Exclude teacher observation or student growth when determining designations

Add or remove eligible teaching assignments

Add or remove student growth measures

Add or remove teacher observation component

Once a district selects a decision-making approach and any necessary changes, it will be applied across the system to determine designations.