�Data Corner (Data Driven Decision Making)�

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Program Improvement Part I: MSGs

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Katya Backhaus

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April 23, 2024

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Contents

Data Driven Decision Making

Example of MSG Improvement

Conclusions

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Step by step illustration of the process

Introduction

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What is Data-driven decision-making?

Data-driven decision-making refers to the process where educators, administrators, and policy makers use collected data to guide and inform their decisions regarding educational practices, policies, and management.

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"Without data, you're just another person with an opinion."

—W. Edwards Deming

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What is so good about Data-Driven Decisions?

�Data-driven decision-making enhances accuracy and objectivity in strategizing, enabling organizations to optimize performance and outcomes based on empirical evidence rather than conjecture.

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Data-Driven Decision Making allows

• Minimize bias

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• Reduce reliance on guesswork

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• Speed up the process

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• Align results with goals

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• Enhance transparency

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• Help to anticipate future trends and issues

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Process Cycle

1. Identify goals

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2. Gather data

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3. Analyze

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4. Interpret

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5. Make informed decisions

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6. Implement

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7. Monitor outcome

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8. Refine objectives

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Step One: Identify Goals and Objectives

• Specific

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• Measurable

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• Achievable

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• Relevant

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• Time-bound

Example: MSG Improvement. Step 1

Overall Objective: Improve MSGs

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Checklist:

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• Specific: Improve specific targeted outcome
• Measurable: Last row of Column O on Table 4
• Achievable: Improve MSGs by 4%
• Relevant: State priority
• Time-bound: By the end of next FY

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SMART goal: Improve MSG by 4% in next FY

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Example: Step 2, Gather Data

Background: MSG is a compound outcome, consisting of

3 components (or 5 types)

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Need data for last 4-5 FY:

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1. Total MSGs, Table 4, last row column O
2. Level Gain Percentage, Table 4, column L/column B
3. HSE Rate, Table 4, column M/column B
4. IET outcomes, Table 4, column N/column B MSG = Level Gains + HSE + IET
5. MSGs for different EFLs, Table 4 (only 1 per PoP)
6. MSGs for different populations from SPR report

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Example: Steps 3-4, Analyze and Interpret, Components

State MSGs have been increasing

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Main component – Level Gains. Proportion of HSE attainment increased from 18% to 26%. Very low proportion of IET MSGs

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All 3 components have been increasing

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Example: Steps 3-4, Analyze and Interpret, Levels

ABE has higher MSGs than ESL. Difference keeps increasing.

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Not much variation between ABE levels.

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Big difference between ESL levels.

MSGs are a very low for L5 and L6.

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Example: Steps 3-4, Analyze and Interpret, MSGs for ABE

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ABE L4 historically had highest MSGs. MSGs has increased in all levels in last 3 FYs

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Level gain drops for higher levels, HSE rate is increasing. Both are high for L4

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ABE L3 is highest in enrollment, followed by L2. MSGs for L2 and L3 contribute the most.

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Example: Steps 3-4, Analyze and Interpret, MSGs for ESL

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ESL L2 historically had highest MSGs. MSGs have dropped in L2, L3, L5 and L6 in last year.

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ESL L1 historically has highest enrollment. L5 and L6 with its low MSGs have high enrollment. Enrollment has been increasing in all levels.

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Example: Steps 3-4, Analyze and Interpret, MSGs by Gender

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MSGs are about the same for both genders

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About 60% of participants are females. Enrollment has been increasing for both.

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Example: Steps 3-4, Analyze and Interpret, MSGs by Ethnicity

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MSGs are historically higher for White students. Hispanic students have lowest MSGs

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71% of participants are Hispanic

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Example: Steps 3-4, Analyze and Interpret, MSGs by Age

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MSGs are historically higher for minors. MSGs are often lowest for 25-44 range

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Almost half of the students are 25-44. Enrollment is growing for all ages. Largest increase is in minors.

Example: Steps 5-6, Make and Implement Decision

Use identified areas of weakness to make changes. Increase MSGs in:

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1. ABE L2, L3 (highest enrollment , lower MSGs). Investigate and make changes in post testing and classes.

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2. ESL:

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• ESL L1 (highest enrollment, lower MSGs)
• ESL L5 and ESL L6 (very low MSGs)

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3. 25 – 44 age group.

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Example: Step 7, Monitor Outcome

• Pay special attention to MSGs, HSE rate, EFL gains, post-testing rate for ABE L2 and L3, ESL L1, L5 and L6

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• Watch outcomes for 25-44 age group

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• We identified areas of weakness but need to dive deeper to make a specific plan for improvements

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Example: Step 8, Refine

• Need to look deeper to understand how we can improve MSGs in the areas of weakness

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• Need to make another full cycle in our data driven process

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• Refine the process to determine the plan of achieving the goal of 4% MSG increase.

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• This refinement is going to be the topic of Data Corner #2: EFL Gains and post-testing rate and IET specific MSGs

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Data Corner 2:

EFL Gains and Post Testing Rate

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May 22, 2024