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To prepare:

• Review the following videos, and consider how each connects data to vision and mission:
  • Data-driven Instruction and Assessment: Bethel School District—Connecting Data to the District’s Mission and Vision
  • Data-driven Instruction and Assessment: Richland II School District—Connecting Data to the School’s Mission and Vision
• Review Chapters 4, 5, and 6 in the course text. Course text: Bernhardt, V. L. (2016). Data, data everywhere: Bringing all the data together for continuous school improvement (2nd ed.). New York, NY: Routledge.
  • Chapter 4, “How We Do Business: Perceptions Data” (pp. 29–36)
  • Chapter 5, “How Our Students Are Doing: Student Learning Data” (pp. 37–42)
  • Chapter 6, “What Our Processes Are: School Process Data” (pp. 43–48)
• Review the vignettes in Chapter 4 in Bambrick-Santoyo text. Compare the data sets utilized by Lanieer and Riverview middle schools, Samuel Green Middle School, and Chicago International Charter School. Consider which data were most relevant to diagnosing strengths, needs, and gaps specific to the success of each district and school. Also, consider which data were missing. (https://www.tasb.org/Services/Leadership-Team-Serv…)
• Review the leadership clips in the Interactive Multimedia CIA Framework. (video: https://mym.cdn.laureate-media.com/2dett4d/Walden/…) and (video: Laureate Education (Producer). (2011j). Data-driven instruction and assessment: Richland II School District—Connecting data to the school’s mission and vision [Video file]. Baltimore, MD: Author.)
• Be ready to explain and defend your selection of the most relevant data sets, referencing aspects of the text(s) or other resources to support your position.
• Refer to the school district you chose in Module 1. (attached you will find discussion for Module 1 and the school district I used)

Post:

When analyzing the data utilized by Lanieer and Riverview middle schools, Samuel Green Middle School, and Chicago International Charter School, what was most relevant for diagnosing strengths, needs, and gaps? Were any relevant data missing? How was the collaborative inquiry strategy utilized? What role did the data teams play? Finally, what strategies were used to engage the entire faculty?