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UofM Action Research and Data Visualization Problem of Practice Discussion

Action Research and Data Visualization

Jessie Green is beginning her fifth year as the executive director of a not-for-profit organization that, among other initiatives, facilitates community partnerships with city schools. As she reflects on the past four years, she is excited at the organization’s success in developing and sustaining partnerships with schools. Yet some of these partnerships seem to have lost their initial energy. Furthermore, she is not confident that all the partnerships are actually aligned with the goals that were originally established for the partnerships. She wonders how she could use a stronger and more systematic data collection and analysis process to learn how effective the partnerships really are and then use these data to develop updated charters and success criteria.

Jessie is especially interested in developing opportunities for partnerships with Riverside Unified, a nearby school district. Riverside’s superintendent has expressed enthusiasm for working with Jessie’s organization, and Jessie is eager to leverage this support. She knows, however, that she needs to learn a lot more about the district. So, she opens a link she has bookmarked on her desktop that contains publicly available data on the students, staff, and financial data for this district and all school districts in the state:

For this discussion, imagine yourself in Jessie’s place as you examine the data charts and tables for Riverside from the website above. Her first task is to gain an overview of the district’s student and staff demographics.

After reviewing both charts and tables and filtering the data by number, percentage, and sub-groupings (where available):

  • What can you infer about the Riverside Unified district in terms of student population? In terms of district mobility, given differences between the Census Enrollment and Cumulative Enrollment?
  • What can you conclude from a comparison of the 2017–2018 Free and Reduced-Price Meals; Unduplicated Pupil Count of Free/Reduced-Price Meals, English Learners & Foster Youth; and English Learners?
  • What other inferences can be made from the Riverside Unified District demographic data?

Click also on the Performance bar near the bottom of the page to locate the ELA, mathematics, and fitness performance results. Be sure to filter your results by different sub-groups to see similarities and differences.

  • What are 3–5 conclusions you might draw from the data tables and charts?
  • What questions would you ask to gain clarity about some of the conclusions suggested but not verified by the data?
  • What are some of the inherent limits to what you can infer from this type of quantitative data?

Jessie is aware that qualitative research could be used to help increase her knowledge base about the district and inform her decision making. Based on your answer to question 3 in which you explored inherent limits to what can be inferred from quantitative data, what suggestions would you provide to Jessie regarding possible qualitative data that might be helpful in accomplishing her goal of connecting the superintendent and community resources in a way that will promote student academic success? Comment on how you would recommend collecting these data, and recommend one or two approaches for analyzing the data.

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