This week I have been co-hosting Miranda Parker, from Georgia Tech, as part of our Colloquium on Computer Science Pedagogy. Her work is titled, Barriers to Computing: What Prevents CS for All.
The question is what are the barriers to accessing Computer Science at the high school level. Nation-wide, ~35% of high schools offer at least one CS course, although this is self-reported CS. In Indiana, the most popular courses are taken by ~5000 students, state-wide. In Austin Texas, about 6000 students take at least one CS course, out of ~110,000.
How do we know if students are successful (i.e., did they learn)?
For this, we need a validated assessment to ensure that we are measuring what we want to measure. An initial assessment, FCS1, worked fairly well and across multiple introductory programming languages; however, the assessment had limits in its use, which lead to the SCS1. This assessment correlated well with FCS1, so it can standin; however, it was an hour long. Assessments should: cover the content, vary in difficulty, and have good discrimination (so individual scores are good predictors for the overall performance). In analysis, most of the SCS1 questions were hard, and few provided good discrimination. The assessment was then adapted to focus on the medium difficulty problems that discriminate (in test scores), and expanded to a 30 minute test.
With a measurement of whether students are succeeding in Computer Science, we can walk back to investigate what things influence students to succeed in Computer Science.
Among prior studying factors, we know that students do better in CS if they have more prior experience with Computer Science, and students do better in CS (as well as STEM and general education) with higher socioeconomic status (SES). There are also known prior links between SES and access to computing (whether it is formal courses, or informally), and SES to spatial reasoning. And both of these later components are linked to CS achievement.
In exploratory study (large, public university with mainly high SES students), showed statistical correlation from spatial reasoning to CS achievement. There was not correlation from having access to achievement. In the interest of time, this point was not presented further.
Barriers to computing
There are three main sources of access to Computer Science courses: state policies, geography (such as, circumstances, industry, or neighboring schools with CS), and resources (such as, time and money or rural versus urban).
In partnership with Georgia Department of Education, CS enrollment data from 2012-2016, plus other public data (such as, characteristics of the county and of the school), for each of the 181 school districts in Georgia, where each district has at least one high school. Using the definition of CS courses, being those that count toward the graduation requirement in Georgia as computer science, versus other computing courses, such as web design or proficiency with office. In Georgia, out of 500,000 students, about 6000 took a CS course in a given year, where about 50% of schools offered computer science during at least one year in that time frame. And the 6000 students are actually student course events, where a student taking two CS courses would count twice.
For those schools, CS enrollment, total high school enrollment, and median income contribute to whether CS will be offered in the next year.
This work is yet ongoing, where the next steps are to visit schools and collect further data on these factors, such as why a school discontinued a course offering, or now offers one. Or what do students these courses go on to do? An audience question wondered whether the CS courses offered relates to the courses offered of other parts of STEM.
