data analysis and school improvement. background and information tais framework activities targeted...
TRANSCRIPT
TEAM DATA DIGData Analysis and School
Improvement
TODAY’S AGENDA
Background and Information
TAIS Framework
Activities
Targeted Improvement Plan Development
Welcome and Introductions
WHO DO WE HAVE IN THE ROOM?
Welcome
Introductions
Tools for Today
• Results: The Key to Continuous School Improvement, 2nd Ed.; Schmoker, 1999.
• Leading School Change; Whitaker, 2010.• Campus Data• 2014-15 Targeted Improvement Plan
WHY DATA ANALYSISData Analysis and School
Improvement
Data Analysis
• Important part of the Texas Accountability Intervention System (TAIS) Continuous Improvement Process– Not a one-time activity– Not ONLY an end-of-year activity
• Looking for patterns and trends• Important for decision-making on plans
Types of Data
• Qualitative –– characteristics,
attributes, properties, qualities, etc. of a thing is described
– It is data in language, not numbers.
– Categorical data– High validity
• Quantitative –– Numerically counted or
expressed is collected– Easily put into tables,
charts, and graphs– Often deals with scale
measurements– Lacks description and
explanation
Qualitative Data Example
• Freshman Class– Friendly demeanors– Civic minded– Environmentalists– Positive school spirit
Quantitative Data Example
• Freshman Class – – 672 students– 394 girls, 278 boys– 68% are on the honor
roll– 150 student accelerated
in mathematics
Qualitative and Quantitative
At your table, please spend a few minutes answering the following questions. We will share out answers in about 10 minutes.
1. What quantitative data do we have?2. What qualitative data do we have?3. What data that we do not currently have would
be helpful in developing our plans?4. Do we have enough data to do our work?
Why do We Analyze Data?
The purpose of analyzing data is to obtain usable and useful information. The analysis, irrespective of whether the data is qualitative or quantitative, may:
Why Do We Analyze Data?
• Describe and summarize data• Identify relationships between/among
variables• Compare variables• Identify the difference between variables• Forecast outcomes
Common Data Myths
• Complex analysis and big words impress people.
• Analysis comes at the end after all data are collected.
• Most appreciate practical and understandable analyses.
• We must think about the analysis before starting so that we have the data we want to analyze.
Common Data Myths
• Quantitative analysis is the most accurate type of data analysis.
• Data have their own meaning.
• Quality of analysis process is what matters.
• Data must be interpreted. Numbers do not speak for themselves.
STEPS IN THE PROCESS
Organizing the Data
Understanding the Data
Identify Limitations
Interpreting the Data
Organizing the Data
• All forms/questionnaires in one place.• Check for completeness and accuracy.• Remove incomplete data points• Record your decisions• Enter data– By hand– By computer
Understanding the data
• What do you want to know?• How will you use the data?• Compare/Contrast data?• Does data exist?– Worst case: develop plan and data cannot be
obtained• Is data relevant?
Interpreting the Data
• Numbers are not an end unto themselves– Must be interpreted by someone– Meaning given by people
• Attaching meaning to the data– What does it mean?• 55 students passed the test• 25% met the standard• 4 coded other
Interpreting the Data
• Fair and careful judgments– Often can be interpreted
in different ways– Articulate how
interpreted
• Not done in isolation– Meetings– Groups– Input
Interpretation of Data
• What did you learn?– Ah-has?– New?– Expected?
• Surprises?• Further study needed?
Identify Limitations
• Be explicit• Be prepared to discuss• Be honest• Do not make claims without real analysis and
design• Make sure you have adequate information to
make claims
TEAMWORK AND GOALS
Teams get results.-Katzenbach and Smith 1993
But what does “pretty well” mean?
-Byham 1992
What gets measured gets done.-Peters 1987
Teamwork
• Data Analysis is often done by ONE person• Data Analysis should be conducted by a TEAM• Collaboration is important• Different viewpoints are important in
addressing needs• All members of team must participate
Meetings Table Talk
• What is a major cause of "unproductive, unrewarding meetings" (p. 15)?• How well do your data meetings
match the structure presented on p. 119-120?
Effective Goals
• SMART– Specific– Measurable– Attainable– Realistic– Time-Bound
Effective Goals
• Few in number– Remember this is for a TARGETED IMPROVEMENT
PLAN– Address the areas identified in the data analysis
• Allows for collaboration and collegiality in meetings– Failure can result in ineffective meetings
Measurable Goals
“Far too many teams casually accept goals that are neither demanding, precise, realistic, nor actually held in common. . . . Teamwork alone never makes a team.” (p. 23)
- Katzenbach and Smith
Specific Goals
• Convey a message directly to teachers that they are capable of improvement
• Provide a basis for rational decision making
• Enable teachers to gauge their success
Targeted Improvement Plan Activity
• In your school teams, look over your targeted improvement plan.
• Look for your goals.• Identify the goals which are specific and
measurable.• Do these lead teachers to know exactly what to
do in their classrooms to affect change in the goal area?
• Rewrite one goal to be more specific.
CSF PLANNING GUIDE
Data Driven Instruction
Quality Data
Jigsaw
• Assigned Tables will review the appropriate section of the CSF document.
• Discuss the relevance of the section.• Report out to the group.
DATA TO USEData Analysis and School
Improvement
Reducing Threats
• Elimination of Staff• High Stakes Prematurely Implemented• Collective analysis• Pay for performance• Teacher autonomy in data choice• Create successes
PRELIMINARY DATA
• STAAR
STAAR-A
TELPAS
STAAR – Alternate 2
Preliminary Data
• Pearson Reports have a TON of data• Reports on all students tested– Important when looking at individual students– Important in comparing with accountability data
• Provides an “Early Indicator” of success or improvement needed
• Data files available as well as paper reports
• Reports available on TAMS• Data files available on TAMS as well• Updated files• Available to upload to other data systems to
assist in data analysis
Common Data Systems in Region 4
• eduphoria!
• DMAC
• On Data Suite
What to Look for?
• Trends• High/Low performance• Comparison of groups• Teacher/classroom level data• Student level data
Meaning of Data
• Remember, data only has meaning that we give it– Important to be honest about data– Important to be honest about trends– Important to be honest about specific
teachers/students– Important to determine the relationship of data to
school performance
Reminders
• Look for a variety of data points• Be sure to develop goals based on your data
information• Make sure you are able to analyze interim
data as you go through your year• Determine what data you need to determine
effectiveness of interventions
Look at your Reports/Data
• Take time to analyze your data.• Look for trends, highs, lows, etc.• Determine areas for improvement• Identify a goal– Write a SMART goal which will help teachers
understand the role they have in the success of the goal
– Determine data needed to know goal is met
Look at your TIP
• Determine if you made progress• What interventions worked?– Why?– Continue?– Implement in other area?
• What didn’t work?– Why?– What to do next?
TAIS Process
• One component of the continuous improvement process
• Occurs frequently during the year• Continue process before school starts• Begin year with updated plan
Questions?