Driving Instruction through Data Analysis Selina Salgado Luis C. Panduro Barbara Worth Jr. High...
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Transcript of Driving Instruction through Data Analysis Selina Salgado Luis C. Panduro Barbara Worth Jr. High...
Driving Instruction through
Data Analysis
Selina SalgadoLuis C. Panduro
Barbara Worth Jr. High School
Transparent Data Conversations which Lead to Action!
• Open data conversations are held with respect to common assessments which are based on state standards.
• The decision was made to begin with English Language Arts and Mathematics.
• All department teachers were brought together to work on a backwards design approach to ensure that state standards were being addressed.
Backwards Design• All standards are analyzed and unpacked.• Standards are compared with state
blueprint which shows weight of each standard.
• Standards are ordered into a logical sequence (Some are prioritized while others may be removed due to time constraints) which allows progressive instruction as well as spiraling of concepts.
• Review periods with sacred data analysis time is built into calendar before and after each assessment.
Backwards Design
• State adopted course material is reviewed in depth with calibration of rigor and relevance to state standard.
• A scope and sequence is created which ensure that appropriate balance and time is given so that students may have an opportunity to learn standards before state assessment is given.
• Windows of time are created after each assessment so that true remediation and enrichment can take place.
Data Analysis
• Through the backwards design approach teachers worked together with the help of Educational Testing Services (ETS) to create common assessments that were calibrated to state standards.
• Teachers took ownership of the assessments and we were able to determine that these assessments were valid and reliable through the support of ETS which happens to be the creator of our CST.
Data Analysis
• We hold meetings with our ELA and Math departments after each assessment in which we look at three areas:1. How each teacher’s students performed as a
whole.2. How each teacher’s individual classes performed.3. How each teacher’s significant subgroups faired
on the exam.
• We use the State API to determine the strength of the incoming class for more appropriate comparisons as per NCLB.
• We share all data which includes names so that teachers can work together and learn from each other.
Data Analysis
• Together teachers identify areas of general need that require remediation as well as enrichment and a plan is crafted which is implemented during the empty window of time.
• Teachers observe each other during preps to identify the varied approaches that yield greater results.
• The results are that teachers begin working as a unit like never before. They seek assistance from those that show higher scores and they become a little competitive yet it remains friendly.
Data Analysis
•We found that the transparency reached even the teachers that had been set in their ways and they began seeking assistance from us and other teachers.
•The results were very encouraging. We will walk you through our data in a few moments.
Data Analysis Meeting
• CDE API Calculation
• Data Comparison Sample 1
• Data Comparison Sample 2
• Subgroup Comparison 1
• Subgroup Comparison 2
Being Honest About Our Progress
7th ELA All Students
46%
58%52%
72%
46%
0%
10%
20%
30%
40%
50%
60%
70%
80%
Mid-Quarter End First Quarter Mid-2nd Quarter
7th ELA ALL
AYP Goal
Academic Improvement
647
667661 658
682
716
600
620
640
660
680
700
720
2003 2004 2005 2006 2007 2008
Year of CST
Academic Performance Index
2003
2004
2005
2006
2007
2008
Our 2007-2008 Goal was 688
ELA 2006 2007 2008 Growth Met AYP Target?
School Wide 25.0 31.0 40.2 6.2% Yes
Hispanic 26.1 30.7 37.7 6.0% Yes
English Learner 13.7 16.7 19.9 3.2% No*
SED 21.4 26.3 32.1 5.8% Yes
White 51.0 54.5 69.7 15.2% Yes
Math 2006 2007 2008 Growth Met AYP Target?
School Wide 18.0 25.4 41.2 15.8% Yes
Hispanic 18.2 24.0 38.9 14.9% Yes
English Learner 11.9 17.0 29.8 12.8% Yes
SED 15.0 20.2 35.4 15.2% Yes
White 36.0 34.2 66.7 32.5% Yes
API Growth 2006 2007 2008 Growth Met API Target?
School Wide 658 682 713 31 Yes
Hispanic 649 673 704 31 Yes
English learner 581 599 630 31 Yes
SED 628 645 676 31 Yes
Academic Improvement
18%
25%
42%
25%31%
40%
27%
37%
52%
17%
24% 24%
4%
45%
66%
0%
10%
20%
30%
40%
50%
60%
70%
Math ELA Science History Writing
2006
2007
2008
Barbara Worth Jr. High School
We hope the presentation
was beneficial for you!