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2007 Minnesota Staff Development Council Annual Forum. May 16, 2007. Dr. Scott McLeod, CASTLE, www.scottmcleod.net.
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USING DATA TO MAKE DECISIONS:Results from the Minnesota Statewide
DDDM Readiness Study
Dr. Scott McLeodDr. Karen Seashore
University of Minnesota
Get this presentation
See the RESOURCES
section of your handout!
Frequent formativeassessments
Professional learningcommunities rooted
in student information
Making instructionalchanges
•Data safety Data transparency
•TechnologyAlignment for results
Go
od
b
ase
line
da
taM
easurableinstructional goals
9 essential elements of data-driven PLCs
Respondents
Respondents
• Teachers (n = 3,135 / 11,120?) (28%?)
• Principals (n = 791 / 1,770) (45%)
• Superintendents (n = 202 / 351) (58%)
• District technology coordinators (n = 139 / 351) (40%)
4,267 Minnesota educators
Awesome!
Respondents by gender, race / ethnicity
96% White
Respondents by urbanicity
Respondents by level
Respondents by AYP status
Assessment Intensity
Frequent formativeassessments
Professional learningcommunities rooted
in student information
Making instructionalchanges
•Data safety Data transparency
•TechnologyAlignment for results
Go
od
b
ase
line
da
taM
easurableinstructional goals
9 essential elements of data-driven PLCs
I receive state assessment results each year [teachers]
I receive state assessment results each year [teachers]
I receive other yearly assessment results each year [teachers]
I receive other yearly assessment results each year [teachers]
Teachers collaborate to create and use common periodic assessments for
student progress monitoring [teachers]
Teachers collaborate to create and use common periodic assessments for
student progress monitoring [teachers]
Teachers use other (not teacher-created) periodic assessments for student progress monitoring [teachers]
Teachers use other (not teacher-created) periodic assessments for student progress monitoring [teachers]
Summary
• Lots of teachers are NOT intersecting with yearly data
• Some differences between secondary subject areas
• Clear, consistent downward gradient from elementary to secondary
Let’sRecap
Beliefs About Types of Assessments
Frequent formativeassessments
Professional learningcommunities rooted
in student information
Making instructionalchanges
•Data safety Data transparency
•TechnologyAlignment for results
Go
od
b
ase
line
da
taM
easurableinstructional goals
9 essential elements of data-driven PLCs
Assessments are aligned withstate curriculum standards
Assessment results are easy tounderstand and interpret
Assessment results are detailed enough to adequately inform teachers’ instruction
Assessment results are timely enough to adequately inform teachers’ instruction
Summary
• Weak agreement that assessments are aligned with standards
• Non-state assessments are– easier to understand– more detailed– much more timely
Let’s Recap
Other Components of the Core
Frequent formativeassessments
Professional learningcommunities rooted
in student information
Making instructionalchanges
•Data safety Data transparency
•TechnologyAlignment for results
Go
od
b
ase
line
da
taM
easurableinstructional goals
9 essential elements of data-driven PLCs
Measurable instructional goals
Measurable instructional goals
Measurable instructional goals
Teacher teams (PLCs) that meet regularly
Teacher teams (PLCs) that meet regularly
Teacher teams (PLCs) that meet regularly
Making instructional changes
Making instructional changes
Making instructional changes
Summary
• Administrators less positive about teacher behavior
• Teachers feel collaboration time is inadequate
• Clear, consistent downward gradient from – elementary to
secondary– AYP to No AYP
Let’s Recap
Supporting Conditions
Frequent formativeassessments
Professional learningcommunities rooted
in student information
Making instructionalchanges
•Data safety Data transparency
•TechnologyAlignment for results
Go
od
b
ase
line
da
taM
easurableinstructional goals
9 essential elements of data-driven PLCs
Data access and transparency
Data access and transparency
Data access and transparency
Data safety
Data safety
Data safety
Technology
Technology
Technology
Alignment for results
Alignment for results
Alignment for results
Summary
• Teachers less positive about supporting conditions
• Clear, consistent downward gradient from – elementary to secondary– AYP to No AYP
Other Factors
Leadership and support
Leadership and support
Leadership and support
Professional development
Professional development
Professional development
Beliefs
Beliefs
Beliefs
Summary
• Teachers less positive about– administrator support– staff development
• Teachers more likely to believe achievement is out of their control
• Clear, consistent downward gradient from– elementary to secondary– AYP to No AYP
Let’srecap
A Few Last Things
Teachers most likely to agree that…
1. They have the knowledge and skills to improve student learning
2. They can significantly affect students’ achievement levels by trying different teaching methods
3. If they constantly analyze what they do and adjust to get better, they will improve
4. District goals were focused on student learning
5. They feel some personal responsibility when school improvement goals are not met
Teachers most likely to disagree that…
1. They are given adequate time for collaborative planning
2. State assessments are timely enough to adequately inform instruction
3. They have significant input into data management and analysis practices
4. State assessments are detailed enough to adequately inform instruction
5. They have received adequate training to effectively interpret and act upon yearly state assessment results
Miscellaneous comments
Our success as educators should be determined primarily by our impact upon student learning
Our success or failure in teaching students is primarily due to factors beyond our control
rather than to our own efforts and ability
• State test data aren’t very useful
• Teachers feel less positively about school and district DDDM activity than do administrators
• Significant percentages of teachers are not intersecting with DDDM
• Clear, consistent differences between– elementary and
secondary– AYP and NO AYP
Overall summary of descriptive statistics
Let’srecap
Frequent formativeassessments
Professional learningcommunities rooted
in student information
Making instructionalchanges
•Data safety Data transparency
•TechnologyAlignment for results
Go
od
b
ase
line
da
taM
easurableinstructional goals
9 essential elements of data-driven PLCs
Next steps = more sophisticated statistics
• Factor analysis
Example
P34 (goals) +P41 (transparency) + P43 (technology) + P47 (prof devt) + P51 + P53 + P54 + P55 (alignment) = ADMIN BEHAVIOR
Next steps = more sophisticated statistics
• Regression, SEM, maybe HLM– dependent variables
• DDDM study results (including factors)• MDE attendance / mobility• MDE enrollment• MDE languages• MDE licensed staff• NCES Common Core of Data
– independent variables• DDDM study results (including factors)• MDE achievement (AYP status, MCAs)• MDE dropouts / graduation
Wrap-up
• Questions?
• Reactions?
• Implications for action?