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Analyzing Data Usage at Cal Prep: A Collaborative Approach Towards Student Success

Explore how data is utilized at Cal Prep, an Early College High school in Berkeley, CA, through a partnership with UC Berkeley. Discover the impact of data on student achievement and the school culture, as well as the role of the college partner in enhancing data analysis. Gain insights into successful strategies, staff culture, multiple measurement approaches, and areas for improvement.

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Analyzing Data Usage at Cal Prep: A Collaborative Approach Towards Student Success

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  1. A LOOK AT THE NUMBERS: How We Use Data at Cal Prep, An Aspire Public School in Partnership with UC Berkeley

  2. Where is Everyone with Data? Find a partner from a school other than your own. Ask the following, be ready to report out! • In what ways do you use data at your site? • What effect has data had on student achievement and culture at your site? • How has your college partner been enlisted to help with data? • What would you most like to get out of this session?

  3. Cal Prep Population by Numbers • Cal Prep is an Early College High school, currently grades 8-11 in Berkeley CA. • Serve students from over 5 different areas in the surrounding area • 60% African American, 37% Latino, 3% Pacific Islander • 51% Free and Reduced Lunch • 100% of students enrolled in Early College

  4. API GROWTH SUMMARY Cal Prep Achievement in Numbers

  5. Performance in ELA

  6. Performance in Math

  7. Science Performance

  8. History Performance

  9. Cal Prep Faces

  10. The Partnerships • Co-directed by Aspire and UC Berkley • Meet with both weekly • Both are vision holders, stakeholders and offer financial and intellectual capital • Both afford us a greater potential to look at and receive breadth of data and information • Both stress the use of data to drive student achievement and well being

  11. Similarities and Differences • First think, then share: • What similarities and differences are there between our sites? • How will this influence the ways in which we both look at data?

  12. What Works for Us • Having staff buy in with data • Having staff willing to work hard and smart based on the results • Use of data that assess success in all areas of the school and with all stakeholders • Looking at data in different settings, with different folks • Looking at data often and critically • Having resources that help “crunch” data • We are responsive to what the data says • Having an organizational culture that supports data use and sharing

  13. Staffing and Staff Culture • Focused on culture with students and staff • Trust, Positive Intent, Defined Vision • Focus on Student Achievement • Accountability • Above and beyond is the norm • From interview on, emphasis on data and professional learning and growth • Everyone is involved

  14. Multiple Measures and FociFrequent and Regular • On a regular and frequent basis, different people look at quantative data that shows: • Student performance on teacher-developed standards based assessments (teacer, team) • Student performance on regular benchmarks and/or interims (teacher, team) • Student performance as judged by grades (admin) • Student performance as judged by discipline (admin)

  15. Multiple Measures and FociAnnual or Semi-Annual • On a semi-annual or annual level, we look at quantative data that shows: • Student performance on the CST (State Test) • Efficacy of administration as assessed by staff (RISE, Aspire) • Efficacy of Aspire Support as assessed by staff (Aspire) • School performance and support as assessed by families (Aspire) • School performance and support as assesse by students (Aspire)

  16. Multiple MeasuresAs needed or as available • UC Berkeley provides us with information that looks at trends and needs, and helps us respond: • Functioning of advisories • Functioning of our reading intervention • Success of our culture building • Based on PhD projects at our school

  17. Resources with Crunching Data • CST and Bechmark analysis done by our Home Office/Godzilla – Gates Grant: • Schoolwide and comparisons to Aspire wide • By subject, teacher and strands • Proficiency level growth • Can look at individual student performance through each of these lenses (See teacher page and pivot tables) • Edusoft program (see benchmark analysis)

  18. Responses to Data – Individual and Group Student Support • See supports chart • Team Go College Now meets regularly • Support Period pull out groups – teachers determine groups on ongoing basis • Writing Center pull out groups – teachers, wrap around, admin determine • SPED referrals/support – admin/sped • PLP target students – teacher determine • Counseling – teacher, admin, counseling

  19. Response to Data: Whole Class • Cyle of Inquiry: • Assessment • Teach • Re-assess • Re-teach • Repeat

  20. Responses to Data: Whole School and By Subject • Admin vision points determined by staff, student and family feedback in addition to CST results • School wide response to data – culture, academic and extracurricular • PLP strand analysis and pacing guide review with teams • Dedicate resources to UC Berkeley tutors • A lot resources from UC Berkeley

  21. What We Need Work On • Continued training on our data tools • Increased time and space to look at data • Retention data and graduation data • Looking at a wider scope

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