If you are on this page, you may have an outdated 2022 State-Specific CS Education Metrics scorecard. The latest 2022 CS Education metrics scorecard for your state is now at 2022 State-Specific Computer Science Education Scorecards Re-VISIONed The policy scorecards linked below are still the most recent for that time period.
Find Your Scorecard
- State-Specific 2022 CS Education Metrics Scorecards
- State-Specific January 2023 CS Education Policy Scorecards
Metrics Scorecard Additional Explanation
State-Specific 2022 CS Education Metrics Scorecards are based on data associated with 2022 State of Computer Science Education report from Code.org, CSTA, and the ECEP Alliance. The majority of the data was downloaded from the xlsx link on the Download Data link in Jan 2023. In addition, data regarding AP CS A and AP CS P tests and pass rates were from archived data from the 2021 exams from Barbara Ericson’s Computing for Everyone Blog. 2022 AP data has not been publicly released yet. The CS Education Metrics Scorecard consists of 5 sections.
| Section | Description |
|---|---|
| School-Based Statistics | Statistics in which the number of high schools is the denominator |
| Student-Based Statistics | Statistics in which the number of students is the denominator |
| Women/Men Relative Strength Statistics | Women-based statistic / Men based statistic |
| Black/White Relative Strength Statistics | Black-student-based statistic / White-student-based statistic |
| Hispanic-Latinx/White Relative Strength Statistics | Hispanic-Latinx-student-based statistic / White-student-based statistic |
References to “CS courses” in the scorecard refer to “foundational computer science courses” that contain at least 20 hours of programming as described in the 2022 State of Computer Science Education report. Each Statistic is listed in a table with the following header:
| Statistic | A description of the statistic |
| Nationwide | The nationwide (weighted) average for the statistic |
| State name | The state value for the statistic |
| Z-Score | The Z-score – number of standard deviations above/below the unweighted state average for the statistic. Because the Z-score is based on the unweighted state average, it can still be positive/negative even if the state value is equal to the nationwide value. The color of the row for the metric is based on the Z-score ranging from dark green (high positive) to dark red (low negative) |
As of Feb 2023, the links to the articles on this blog describing these metrics used data from the 2021 State of CS Education report and AP data from 2019 and 2020 from the Computing for Everyone Blog. Blog articles describing how these metrics have changed from 2021 to 2022 will be posted at a later date.
School-Based Statistics
| # | Statistic | Definition | Notes |
|---|---|---|---|
| A1 | % HS with CS | # HS offering CS / # Total HS | Main benchmark used in the 2022 State of CS Education report |
| A2 | % Students in HS with CS | # Students in HS offering CS / # Total Students | |
| A3 | Have Not/Have HS Size Ratio | Avg size of HS that does not offer CS / Avg size of HS that offers CS | See Revisiting the CS HS: Bigger, Faster, and More Suburban |
| A4 | Poor/Rich HS with CS Ratio | % schools with FRL > 50% offering CS / % schools with FRL < 50% offering CS | |
| A5 | Rural/Urban HS with CS Ratio | % schools in Town or Rural offering CS / % schools in Urban or Suburban offering CS |
Student-Based Statistics
| # | Statistic | Definition | Notes |
|---|---|---|---|
| B1 | % Students in HS with CS | # Students in HS offering CS / # Total Students | Same as A2 above |
| B2 | % Students Enrolled in CS | # Students enrolled in a CS course / # Total Students | Not available for all states. See Percent Students Enrolled in Computer Science: Opening the Treasure Chest |
| B3 | Students Enrolled in CS / Students in HS with CS | # Students enrolled in a CS course / # Students in HS offering CS | Aka “FCS Uptake Ratio”. Not available for all states. See also Treasure Chest blog post. |
| B4 | % Students taking AP CS Exam | # Students taking either AP CS Exam / # Total Students | See also AP CS Exam Rates: A closer look at an old standard |
| B5 | Students Taking AP CS Exam / Students Enrolled in CS | # Students taking either AP CS A or AP CS P / # Students Enrolled in CS Course | Aka “AP CS/FCS Uptake Ratio”. Not available for all states. See also Old Standard blog post |
| B6 | Students Taking AP CS Exam / Students in HS with CS | # Students taking either AP CS A or AP CS P / # Students Enrolled in CS Course | Aka “AP CS/School Uptake Ratio” See also Old Standard blog post |
| B7 | P Ratio (P / (A + P)) | # Students taking AP CS P Exam / (# Students taking either AP CS A or AP CS P exam) | See AP CS Principles: A Cultural shift in K12 CS Education |
| B8 | AP CS Pass Rate | # Students Achieving 3+ on either AP CS exam / # Students taking either AP CS exam | |
| B9 | AP CSP Pass Rate | # Students Achieving 3+ on AP CS P exam / # Students taking AP CS P exam | |
| B10 | AP CSA Pass Rate | # Students Achieving 3+ on AP CS A exam / # Students taking AP CS A exam |
Relative Strength Statistics
The last three sections in the metrics scorecard cover “relative strength” metrics that compare one of the student-based statistics above for a subgroup to the same statistic for a reference subgroup. The three sets of comparison metrics are:
- Women/Men Relative Strength Metrics that divide one of the student-based statistics for women students by the same statistic for male students (see also Women in HS CS: Just get them in the door!)
- Black/White Relative Strength Metrics that divide one of the student-based statistics for Black students by the same statistic for White students (see also Blacks in HS CS: Blame AP Envy!)
- Hispanic-Latinx/White Relative Strength Metrics that divide one of the student-based statistics for Hispanic-Latinx students by the same statistic for White students (see also Hispanic-Latinx Students in HS CS: Little Gaps Everywhere)
All three sections cover the same metrics below:
| # | Statistic | Definition | Notes |
|---|---|---|---|
| C1 | % Students in HS with CS | B1 for subgroup / B1 for reference subgroup | For women/men, this is assumed to be 100% |
| C2 | Students Enrolled in CS / Students in HS with CS | B3 for subgroup / B3 for reference subgroup | Not available for all states |
| C3 | Students taking AP CS Exam / Students Enrolled in CS | B5 for subgroup / B5 for reference subgroup | Not available for all states |
| C4 | Students Taking AP CS Exam / Students in HS with CS | B6 for subgroup / B6 for reference subgroup | |
| C5 | AP CS Pass Rate | B8 for subgroup / B8 for reference subgroup |
Policy Scorecards Additional Explanation
State-Specific Jan 2023 CS Education Policy Scorecards are based on data associated with 2022 State of Computer Science Education report from Code.org, CSTA, and the ECEP Alliance. The contents of this scorecard have been scraped in Jan 2023 from the State Tracking 9 Policies (Public) workbook available from the “State-by-State” details link under “Nationwide Momentum for CS” on the report home page and reorganized to provide information by each state rather than by each policy. Similar but not as up-to-date or detailed data can also be found at the following locations:
- K-12 Computer Science Policy and Implementation in States National Landscape State Detail report
- State handouts available from 2022 State of CS Report | CS Advocacy (code.org).
In addition, Computer Science Legislation | CS Advocacy (code.org) contains links to current legislation under consideration on both the state and federal levels that may not be reflected in the scoresheets. The nine Pro-CS education policies are as follows:
| Policy | Description |
|---|---|
| P1 | Create a state plan for K–12 computer science |
| P2 | Define computer science and establish rigorous K–12 computer science standards |
| P3 | Allocate funding for computer science teacher professional learning |
| P4 | Implement clear certification pathways for computer science teachers |
| P5 | Create preservice programs in computer science at higher education institutions |
| P6 | Establish computer science supervisor positions in education agencies |
| P7 | Require that all high schools offer computer science |
| P8 | Allow a computer science credit to satisfy a core graduation requirement |
| P9 | Allow computer science to satisfy a higher education admission requirement |
For more information on how the implementation of these policies affect the CS Education metrics in each state, please view the following blog posts: