Improving Computer Science Education Sources/FAQ
1. Why were congressional districts used as a unit of classification?
These datasets were originally created to enable congressional legislators to learn more about the state of computing education and computing jobs in their districts so that they could implement more effective policies related to computing education.
2. Why are some states producing more degrees than needed to fill the computing jobs in that state?
There are several reasons a state might be producing more degrees than jobs. These include but are not limited to the following:
- there might be a large university in that state but a very small or nearly non-existent tech sector,
- there might be an online university (e.g., University of Phoenix) where people from all over the country earn degrees; these degrees are counted within that state even though the students do not live or work there, or
- the raw numbers of degrees and jobs are very small, so a university may only be producing 20 degrees in computing a year but there are only 10 computing job openings a year in that state.
Even if the additional 10 degree earners take jobs in other states, this would not be enough to make up for the shortages in other states. Of course, another reason could be that this state is doing well in computing education. Further research is needed to tease out these and other reasons.
3. Can these data be used to rank states performance in computing education?
No, you should NOT use these data to rank states or districts for a number of important reasons. For example, just because a state is producing more degrees than needed to fill the jobs in that state does not mean that computing education is excellent there; it may simply mean that there is a large university but very few technical jobs in that state (see General Question #2 for more information on these states). Conversely, a state with very low numbers likely needs to improve its computing education, but it may also have a booming technology sector that could never be filled by the schools or universities in that state alone. These are only a few of the reasons why it is very important to avoid ranking states; instead, consider district numbers in light of the larger state picture and state numbers in light of the larger national picture.
4. Can I add the data from multiple districts together?
No, because these data were collected by zip code and regional workforce areas, there will be overlap among the districts, so adding district data together could result in significant overages in terms of degrees or jobs (see Education Data section #5 and Workforce Data section #3 and 4 for more information).
5. In addition to legislators, who else might use these data?
Anyone interested in advocating for improved computing education can use these data, including educators, school board members, curriculum decision makers, or others concerned with education policy. These data can also be useful for those who want information on regional computing workforce projections.
6. How can I use these data to advocate for better computing education and policy?
Use this information to make educators, curriculum decision makers, or legislators aware of the local situation regarding the relative numbers of students choosing to pursue rigorous computer science education and this population's current ability to meet projected workforce demand. Typically, comparing the education data (number of students who take CS AP tests or graduate with computing degrees) to the workforce data (number of projected jobs) is effective when making the case for improving computing education. You might give formal presentations or give this information informally to educational decision makers. See Sample District-level Graphics for ideas of charts you could create yourself.
1. How were the education data determined for each state and district?
The education data are based on zip codes. The AP exam district data derive from a custom dataset obtained from the College Board (based on zip code of test takers), while the AP exam state data were taken directly from the College Board Website. The completed degree data are for CIP 11 (Computer and Information Sciences) and were taken from the 2007-08 National Center for Education Statistics-Integrated Postsecondary Education Data System (based on zip code of institution). For Guam, Puerto Rico, and Virgin Islands, data were downloaded according to the whole territory rather than by zip code, since they are not broken down further by congressional districts.
2. How were data assigned to congressional districts?
All US zip codes were assigned to one or more house districts. If the same zip code appears in more than one house district, data from that zip code were included in all districts in which that zip code appears. This imperfect matching means that numbers of students may be inflated, because they are sometimes counted in more than one district.
3. Are the AP test and college degree data measuring the same populations?
No. The AP exam data refer to individuals living in the area when they took the exam (i.e., the state or district). The completed degree data include all Computer and Information Sciences degrees granted by degree-granting two- and four-year institutions in a particular state or district. Comparisons cannot be made across these datasets because 1) some students living in the state/district will attend colleges out of state/district, and 2) in-district colleges will graduate out-of-state/district students. However, both datasets serve as indicators of the extent to which students are actively pursuing advanced computing education.
4. Do the completed degrees data refer to degrees completed by students living in a given district or degrees granted from institutions located in the district?
It is not possible to determine the home zip code of students graduating with CIS degrees; therefore, the completed degrees data refer to all degrees completed at degree-granting two- and four-year institutions located in that district. This means that districts without colleges or universities will show low numbers in this dataset even though some students living in these districts may graduate from nearby universities.
5. Why doesn't the total for all of the districts in a state equal the state total?
The education data are based on zip code. Some zip codes cross multiple districts; because of this overlap, district totals are often overestimates and cannot be summed to arrive at the statewide total.
6. What earned degrees are counted as CIS?
The CIS category used by the National Center for Education Statistics includes a number of computing degrees, not all of which are available at the baccalaureate level (NCES also reports two-year degrees): Computer and Information Sciences, Artificial Intelligence and Robotics Information Technology, Computer Programming, Data Processing, Computer Systems Analysis, Data Entry/Microcomputer Applications, Word Processing, Computer Science, Computer Software and Media Applications, Web Page, Digital/Multimedia and Information Resources Design, Data Modeling/Warehousing and Database Administration, Computer Graphics, Modeling, Virtual Environments and Simulation, Computer Software and Media Applications, Computer Systems Networking and Telecommunications, Computer/Information Technology Administration and Management, System Administration/Administrator, System, Networking, and LAN/WAN Management/Manager, Computer and Information Systems Security, Web/Multimedia Management and Webmaster, Information Technology Project Management, and Computer Support Specialist.
For a more detailed list, go to: http://nces.ed.gov/ipeds/cipcode/crosswalk.aspx?y=55. It is, of course, possible that individuals with other degrees can fill some of the projected jobs but this migration may require additional training.
1. How were the district approximations determined?
State workforce data were obtained from each State Department of Labor website. Workforce data are not available by house district or zip code. Instead, regional data from these department of labor websites (e.g., Metropolitan Statistical Area (MSA), Workforce Development Area (WDA), county, or other type of statistical region, depending on the state) were used to estimate projected jobs in the district AND the relevant surrounding areas (most areas where someone living in that district would be likely to take a job). Regional data are important when making arguments about CS education because typically jobs in the entire region – not just jobs in the district – could be filled by students from that district.
2. What jobs are considered "computing jobs"?
Computing jobs include all jobs that fall under the U.S. Department of Labor category "computer and mathematical occupations." The Department of Labor primarily includes the following jobs in this category: systems analysts, computer scientists, software engineers, computer programmers, network systems and data communications analysts, network and computer systems administrators, database administrators, and computer support specialists. Mathematical occupations comprise a small number of the jobs in this category.
3. Why doesn't the total for all of the districts in a state equal the state total?
Sometimes districts fall into more than one Metropolitan Statistical Area (MSA), Workforce Development Area (WDA), county, or other regional statistical area. Conversely, one regional workforce area can contain several districts. Due to this overlap, totaling the jobs data for the different districts WILL NOT equate to the number of jobs in a state. Likewise, district workforce totals should NOT be added together; doing so can result in counting many jobs twice. If you would like to know which regions were included in a district, please contact us at email@example.com.
4. Why do some of the districts share the exact same workforce data?
Multiple districts often lie within the same Metropolitan Statistical Area/s (or other statistical region/s). If the workforce data are the same for two different districts, then both of these districts are located in the same regional statistical area/s. District workforce totals should NOT be added together as this can result in counting many jobs twice.
5. What is the difference between projected average annual number of openings and the projected 10-year change in number of jobs?
The average annual number of openings refers to the average number of openings per year during the 10-year projection period; this number includes both growth and replacement job openings. The 10-year change in number of jobs refers to the number of new jobs that will have been added by the end of the 10-year projection period; it includes only growth jobs. Replacement jobs are not included. Since they simply replace existing jobs, replacement jobs do not contribute to the net growth of a field.
6. Why do different states/districts have different 10-year projection periods?
Individual states update their data at varying rates, so some states will have projection data for 2004-2014, others for 2006-2016, and so on. The data presented here were the most up-to-date data available at the time of data collection. In a few cases, the state workforce projection data had been updated, but the regional data lagged behind. In these cases, a state will have a different projection period than its districts.