A seat in a computer science classroom is one of the hottest tickets on American campuses today. Undergraduate enrollment in computer science is at an all-time high, and many of the students in those classes (even beyond the introductory level) are non-CS majors. This surge is so large and unprecedented that the U.S. National Academy of Science wrote a report to document the surge and suggest strategies for managing the growth.a
Interest in learning computer science extends into primary and secondary schools as well. Several countries have national efforts to provide CS education to every student in every school. Among the 50 U.S. states, 36 have statewide policies promoting CS education. We are struggling to deal with all this interest, but it is a good problem to have. We have something that everyone wants. The problem is who is getting it.
Undergraduate education is still mostly the domain of the rich. Low-and middle-income families are much less likely to get access to higher education than the rich, as reported by the Equality of Opportunity project at Stanford.b Most high schools in the U.S. are not offering computer science, and wealth is a significant predictor of whether a school offers CS.3
One obvious solution is educational technology. We could offer online CS courses, at all levels from primary and secondary school, through undergraduate and graduation education, and beyond to life-long learning. Computer scientists invented MOOCs (Massive Open Online Courses) to provide CS education to as broad an audience as possible. The first MOOCs were invented by CS faculty at Stanford to offer CS courses online. The first start-ups offering MOOCs—Coursera and Udacity—were led by Stanford CS faculty. The authors of this column are both faculty at Georgia Institute of Technology, where our Online-MS in CS (OMS CS) was praised by President Barack Obama for its innovative accessibility and low cost.
In the last six years, we have also come to understand who is taking MOOCs. We now know that MOOC students tend to be older than traditional college students, have above-average wealth, and are well educated. MOOCs do not serve the masses. They do not serve to replace traditional education, but to augment it. They do not "democratize education" as many had hoped.
Recent innovations in online learning are proving to have a "rich get richer" effect—those already likely to succeed benefit, and those left behind are left at an increased disadvantage. We argue that our current technology is further undermining educational equity. Computer science departments have an ethical mandate to do better.
Rawlsian Justice
Is it OK to work to help the already advantaged? Certainly, the latest smart-phone and the latest luxury car are unapologetically created for the privileged. Our current education system is regrettably not so different from a luxury car—for a price, deluxe experiences are available that advantage the children of the rich and help reproduce their privilege. However, we have higher aspirations. We hope education can serve as a leveler, helping everyone to reach their full potential.
We used to think MOOCs were going to change higher education and would democratize education.
Some difference in privilege is necessary and even desirable to create a thriving culture. How much privilege is OK? What are our obligations to work toward greater equity, for a society that aspires to be just? These profound questions were most eloquently addressed by the philosopher John Rawls. Rawls was an ethicist who argued that for a just society, "social and economic inequalities are to be arranged so that they are both to the greatest benefit for the least advantaged and attached to offices and positions open to all under conditions of fair equality of opportunity."6 Rawls called this "the difference principle."
Most undergraduate computer science majors are taught about Rawlsian Justice. ABET-accredited programs must include a course in computing and society, which includes ethical frameworks. This definition of justice is ours. It is the one we teach our own students.
Using Evidence to Tell Us If We Are Reaching the Least Advantaged
We used to think MOOCs were going to change higher education and would democratize education. In 2012, a reasonable person might have seen development of MOOCs as a way to bridge social and economic inequities. By creating MOOCs, CS departments could reasonably claim they were using their privilege to provide great benefit to the least-advantaged members of society.
Today, we have evidence MOOCs do not work like that.
People who take MOOCs already have access to education and tend to be wealthy. Over 60% of MOOC participants already have undergraduate degrees.1 People who take MOOCs tend to be wealthy. A 2015 paper5 reports, "[MOOC] registrants on average live in neighborhoods with median incomes approximately .45 standard deviations higher than the U.S. population."
Analyses of Georgia Tech’s OMS CS shows the students who apply to the program are demographically different from those who take a face-to-face MS CS program.4 The average OMS CS applicant is a 34-year-old mid-career American, while the average in-person applicant is a 24-year old non-American. MOOCs reach a population that would be unlikely to get a master’s degree in another way. The program is transformative for mid-career professionals—people who are already successful, but aspire to more in their careers. As information technology is increasingly becoming critical to every aspect of our society, OMS CS is playing a global role in preparing us for the future. MOOCs are well worth offering, but the population being served tends not to be the least advantaged.
We now know that MOOCs as we have used them so far violate Rawls’ Difference Principle—we are further advantaging the already advantaged. We have an ethical mandate to do better.
We Have to Check If We Are Doing Better
How do we reach the least-advantaged students? Around the U.S., we can see CS departments trying a lot of ways to provide access to CS education. They are offering summer camps, putting their undergraduates into high school and elementary classrooms to help teach CS, or creating "road shows" to demonstrate computer science to elementary or secondary school students who may not know what computer science is. Some of these work. Many do not.
Often, providing computing educational opportunities to "everyone" operationally means only the most-advantaged students actually get access. Free and open summer camps are often filled first by the most-privileged students who tend to hear about the camps and fill them before less-privileged students get a chance.
It is challenging to figure out how to make free and open resources available to less-advantaged students. For example, our colleague Betsy DiSalvo found that many free CS learning resources are never discovered by disadvantaged families simply because the families do not know the right terms to search for.2 The for-profit companies are better at tailoring their websites so their resources are the first to appear for the terms that (for example) immigrant families use when searching for learning resources.
CS departments should offer interventions that measurably reach advantaged and less-advantaged students equally.
Researchers are still working to understand why MOOCs fail students from less-advantaged backgrounds. Access is part of the problem. An experiment offering Udacity MOOCs to San Jose State University students was ended early because the online students had disappointing performance compared to the face-to-face students. Part of the problem there was that the online students did not always have access to broadband Internet when at home. Other researchers are exploring the use of "nudges" to convince students they can succeed and MOOCs are worth the effort.
We do not believe MOOCs are fundamentally unsuited to struggling learners. We need to continue the design work to make scalable MOOC or MOOC-like solutions work more effectively for less-advantaged students. We also need to design and offer non-MOOC alternatives for students who need greater support.
Our Proposal: Match the Learning Opportunities
We propose that CS departments who offer MOOCs must balance the opportunities they are offering to advantaged students (like MOOCs) by pairing them with opportunities for less-advantaged students. CS MOOCs fill a need and should be offered and even expanded. But they do not meet the definition of Rawlsian justice. CS departments should offer interventions that measurably reach advantaged and less-advantaged students equally. Dollar for dollar, student for student, initiatives that reach more advantaged students need to be matched with those that reach less-advantaged ones.
The U.S. National Science Foundation has launched a new pilot effort to expand engagement in broadening participation in computing (BPC) activities by awardees in their Computer and Information Science and Engineering (CISE) directorate.c They aim to increase the number of computer scientists who are working to make computing education more accessible. Some CISE proposals already require BPC plans, and more proposals will be required in the future. Proposal writers will be provided a set of resources, and they will be encouraged to participate in meaningful activities that have successfully reached underrepresented populations. Example programs include the Distributed Research Experiences for Undergraduates (DREU) programd from the Computing Research Association’s Committee on the Status of Women in Computing Research (CRA-W) and the NCWIT Aspirations award.e There are things we can do that have a measurable impact on increasing equitable access to computing education, and it is the responsibility of the entire CS community to do them and assess whether they are working.
Conclusion
CS learning opportunities are highly sought after. CS departments have an ethical obligation to ensure access to these opportunities is equitable. We propose the use of empirical measures, to ensure we are reaching advantaged and less-advantaged students equally.
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