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CS Education Analytics

The following provide deeper analytics work on K12 Computer Science education based upon data already published by other sources:

  • Jupyter Notebook on Google Colab for analysis of State of CSEd report policies and their effect on success metrics.  Also on GitHub  (2022-06-15)
  • By State Analysis of CSA/CSP Test Taking Ratios, Pass Rates, and Relative Strengths metrics based on data available from the CS4All Computing for Everyone Blog (XLSX) (2022-06-02 update)
  • By State Analysis of Foundational Computer Science Courses Based on Location (City/Suburban/Town/Rural) and FRL Population (XLSX) (2022-06-02 update)
  • Explanation of CSEd Success, Optimization, and Relative Strength Metrics (PDF) (2022-03-12)
  • CSEd Success, Optimization, and Relative Strength Metrics by State (XLSX) (2022-03-12) and Jupyter Notebook used to make several of calculations  (2022-06-15)
  • Analysis of State of CS Education Policies and Success Metrics by ECEP Framework (PDF) (2022-02-11)

The analytics provided through this website will seek to provide statistics and insights that were not published with the original data.

The underlying data for all of these analytics come from the following sources:

I would very much welcome any feedback on how the analytics in the report were generated, the underlying data, the value of the analytics generated, or what new analytics you’d like to see in the future.  Some notes:

  • While I have reached out to several in the CSEd community, these reports have not been peer-reviewed.  However, the sources of the data are listed above and how the various analytics were calculated are either in the reports themselves or in publicly available Google Colab notebooks and on GIthub at https://github.com/lgtanimoto/CSed2021Data.
  • I am also aware of limitations in the data itself.   In particular, due to COVID and other reasons, not all states could report the most up-to-date data for their states, and the College Board hasn’t even published data for 2021 exams yet.
  • Despite these limitations, I still believe that these reports provide valuable insights on what is happening in high school computer science education nationwide and in each state.   The reports also “put a number” on phenomena we all know exists to some extent and allow states to better compare their progress to others to see how they can improve.   And I would definitely like to advance the conversation about what high school CS Ed analytics could be so when data is available in the future we can all be looking at the same numbers to find new trends.

 

Tags: #CSEdAnalytics