Communications of the ACM
New Social-Economic Trends in Computer Science Education
What do social funds, crowdfunding, and crowdsourcing have in common?
In a nutshell, social funds are performance-based contracts between social service providers, investors, governments, and other stakeholders, whereby financial returns for investors are generated by positive social outcomes (http://www.social-finance.org.il/). In other words, the government pays investors who invest in social initiatives according to their success solving social problems (e.g., reducing the dropout rate among computer science students).
Crowdfunding is "the practice of funding a project or venture by raising small amounts of money from a large number of people, typically via the Internet" (https://en.wikipedia.org/wiki/Crowdfunding). Examples of crowdfunding platforms are Indigogo and Kickstarter.
And lastly, crowdsourcing is the practice of harnessing the wisdom of a group to achieve a common goal. It is best applied when attempting to solve complex problems in an innovative way, or to streamline intricate processes. Some people involved in crowdsourcing may work as paid freelancers, while others perform small tasks on a voluntary basis (Howe 2006; White, 2019). These phenomena, as other similar ones, can take different forms when working to achieve specific targets.
In addition to being financial tools for promoting projects, either social or not, they reflect a social approach by targeting social problems (social funds) or by providing access to all, as either project entrepreneurs, contributors, or investors, to projects for which that would not be possible using traditional financial tools (crowdfunding and crowdsourcing). In other words, they reflect the wish of organizations or individuals to achieve some target by approaching individuals and organizations other than typical investors or service providers.
Specifically, in the case of social funds, an organization may invest in a social project whose aim is to solve a social problem that typically would not be solved by said organization, but rather by some other organization or authority that does not have the required budget or does not want to take the risk of investing in the project. In the case of crowdfunding, access to launch a project is provided to anyone who has an idea he or she wants to promote in any area, by raising small amounts of money from a large number of people who wish to invest in the project. This enables small investments to create a huge impact. Finally, crowdsourcing provides access to service providers who are not part of the organization that initiated the project or defined the problem that is to be solved.
Relevance for Computer Science, Software Engineering, and Data Science studies
Since many projects that implement these financial approaches use computational, technological, data-based infrastructures, I suggest such approaches are relevant and should be taught in computer science, software engineering, and data science study programs. I will explain this assertion by presenting 10 reasons, which I have grouped into three groups: social factors (1-3), educational factors (4-6), and professional factors (7-10), all of which shape the environment of students in these fields.
Social factors
1. Generation Z. "Generation Z is rapidly replacing Millennials on college campuses. Those born from 1995 through 2010 have different motivations, learning styles, characteristics, skill sets, and social concerns than previous generations …While skeptical about the cost and value of higher education, they are also entrepreneurial, innovative, and independent learners concerned with effecting social change." (Seemiller and Grace, 2016). It is, therefore, highly relevant to expose students belonging to Generation Z to financial-social trends such as those I have mentioned.
2. New economy. Generation Z students need economic knowledge on how to behave, navigate, and manage in the new economy. One important aspect of this new economy is that it changes the discourse on investment, from exits to impacts. In this context, I would like to mention positive investment. "Positive Investment strategies prioritize getting funds into sustainable, life-enhancing solutions, whilst finding smart, targeted ways to get rid of damaging economic habits. It is any investment strategy that contributes to the wellbeing of people and the planet — a positive vision of the world we want and how to get there, not just a response to the futures that we fear or the developments that we oppose" (http://positiveinvest.org/). Many initiatives that promote positive investment are implemented using the financial-social tools described above. Another important concept in the context of the new economy is that of a sharing economy, which is "a socio-economic system built around the sharing of resources. It often involves a way of purchasing goods and services that differs from the traditional business model of companies hiring employees to produce products to sell to consumers. It includes the shared creation, production, distribution, trade and consumption of goods and services by different people and organizations" (https://en.wikipedia.org/wiki/Sharing_economy). Sharing has mutually influenced the development and use of the new financial-social tools mentioned above which, in turn, support the shaping of the new economy (see, for example, the case of Amsterdam harnesses the sharing economy for social good). Among other things, the new economy has led to the establishment of new kinds of organizations and cross-sectorial collaborations (described below) to which current students should be exposed.
3. New kind of organizations. In the new economy, new kinds of organizations emerge and flourish. Two examples of such organizations are: exponential organizations (Ismail, Malone and van Geest, 2014), which, beside financial benefits, foster the involvement of the community and the crowd; and organizations whose goal is to take care of the public wealth, such as the Civic Hall and the Center for Social Development of Washington University in St. Louis, which is "a hub for implementing and testing applied social innovations that broaden well-being for individuals, families and communities. [It] incubates ideas that can be scaled to reach millions, and creates new fields of study to meet social needs." The new economy, along with the new kinds of organizations that have been emerging recently at a growing pace, reflects the understanding that social problems (a) should bother everyone, and (b) can be solved either by the crowd or by the collaboration of several sectors (e.g., social funds and collective impact). Generation Z students should be familiar with these kinds of organizations in the new economy so as to fulfill their desires for personal-professional development (see below).
Educational factors
4. Soft skills. As part of their acquisition of soft skills, whose importance has been widely discussed lately in the context of the new economy's tech industry, students need economic and social knowledge and skills. This knowledge will increase the graduates' awareness of organizational cultural aspects (such as diversity, transparency, and trust) in their future professional work environment. Social-economic tools, such as social funds, crowdfunding, and crowdsourcing, illustrate how diversity is addressed as a means rather than as a goal, and as a result, the increased diversity of the stakeholders involved in the project contributes to the ability to achieve its targets. This message is delivered by many organizations, whose inclusion and diversity missions include the message that diversity helps the organization improve its performance and the services it provides (see, e.g., Intel's diversity and inclusion statement).
5. Interdisciplinarity. The trends discussed above are interdisciplinary in nature. They involve economic, technological, and social issues. It is nowadays accepted that many problems with which scientists and engineers are expected to cope are interdisciplinary in nature. And so, computer science, software engineering, and data science graduates should be exposed to these topics from a variety of viewpoints — technological, social, and economic — that reflect their interdisciplinary nature, rather than regarding them as isolated topics in technology, sociology, and economics. These trends, therefore, provide a golden opportunity to expose computer science, software engineering, and data science students to this kind of problem and to open their eyes to the concept of interdisciplinarity, with which they will have to deal during their professional development. This claim is especially relevant with respect to data science, which is an interdisciplinary topic in itself.
6. Teaching, learning, and evaluation methods. Integrating the topics involved in the different interdisciplinary problems discussed here (economy, sociology, technology) can accelerate the adoption of a variety of teaching, active-learning, and evaluation methods. Project-based learning (PBL) is only one example of a teaching method that can take a variety of forms in different settings, both curricular (e.g., in a regular course in which students develop a project, or in a capstone course) and extracurricular (e.g., hackathon, accelerator, or a collaborative work with the industry, or with a non-profit organization from either governmental authorities or the third sector, non-governmental non-profit organizations). PBL can be integrated in a continuous fashion into different frameworks. For example, in a regular course, students can develop the basis of a project in which they implement the main topics of the course, and then on this basis, they can continue the project development in a capstone course, integrating additional aspects as required in a capstone project. When developing such projects, students can use the available sharing tools with which they are all familiar, for both communication and project development. Developing such projects also fosters students' soft skills (as mentioned above) and intrinsic motivation, since it enables them to fulfill their needs for autonomy, competence, and relatedness (see the self-determination theory by Deci and Ryan, 2000).
Professional factors
7. Student characteristics. Since computer science, software engineering, and data science undergraduate students are, in many cases, among the best students in their universities, the atmosphere in these departments may be competitive, leading students to concentrate mainly on their academic achievements. Increasing their attention to and awareness of social issues may widen their perspective of the world in which they will work after graduation.
8. Future professional work. In their future work, computer science, software engineering, and data science graduates will be the ones to develop the technological tools that support the above-mentioned trends and should therefore be aware of them as well as of their impact on the world. Specifically, each of the three professions has a unique and important role in the development and use of the technological tools with which these trends are implemented. For example, computer science graduates will verify the reliability and efficiency of the tools; software engineers will apply the agile approach in the development process of these tools, among other things, by being attentive to the customers' perceptions and requirements; and data scientists will verify that the data gathered by these platforms on user profiles is managed ethically, avoiding biases and enabling them to achieve their purpose by being open to all as they are intended to be.
9. Cross-sectorial collaboration. New forms of cross-sectorial collaboration are created, based on the recognition that a cross-sectorial collaboration benefits all sectors: governmental organizations and local authorities (1st sector), for-profit organizations (2nd sector, in which most computer science, software engineering, and data science graduates will most probably work), and non-governmental non-profit organizations (3rd sector). Such new cross-sectorial collaborations are important since the different sectors bring different interests to the joint activity, that together create a whole that is larger than its parts, thus exploiting the potential of these coalitions. Two such forms of cross-sectorial collaborations are public-private partnerships (PPP) and collective impact. It is not surprising that cross-sectorial collaborations are becoming more relevant now as companies associate their activities to social trends and needs (without foregoing profit). See for example, our January 11, 2021 column on The Cross-Sectorial Collaborative Shared Value Strategy. If such topics are integrated into undergraduate computer science, software engineering, and data science programs, graduates of these programs who join the industry will be familiar with these trends in general, and with their organization's corporate social responsibility activities, which transition from zero-sum partnerships to all-win game partnerships, in particular. Thus, Generation Z students will be able to combine and connect their desire to contribute simultaneously both to society and to their tech organizations.
10. Professional development. Awareness of these financial social tools will open up more professional development options for computer science, software engineering, and data science graduates, not only in the tech industry (where most of them work), but also in the first and third sectors, in which their representation is relatively low. Thus, by joining the first and third sectors as employees, these graduates will contribute their knowledge and skills to these sectors and increase the diversity of the human resources in these organizations in terms of professionalism.
Why now?
The above trends started to emerge at the onset of the millennium, more or less when the agile approach started to be explored and adopted by more and more organizations, and new work habits, such as transparency (and no free riders), trustful relationships with customers, and a new kind of contracts started being applied in its spirit.
The emergence of these trends has accelerated in the last decade, however, and the question is "Why now?" In addition to the regular scientific and technological explanations of what has been happening in the last decade (more computing power, more data, more sharing of technological tools, and more machine learning algorithms to process this huge amount of data), I argue that the new economy, the social change, and the new technological tools mutually influence each other in shaping the current social-economic-technological texture.
As the 10 reasons presented above aim to argue, this social-economic-technological texture should be integrated somehow into computer science, software engineering, and data science programs. Furthermore, the Corona era has increased the relevance of these topics, since (a) it has increased students' awareness to the fact that they are part of the world and cannot ignore the changes that the entire world has experienced together, and (b) students have gained extensive experience during the Corona era using a variety of sharing-based online learning tools.
Deci, E. L. and Ryan, R. M. (2000). The "What" and "Why" of Goal Pursuits: Human Needs and the Self-Determination of Behavior, Psychological Inquiry 11(4), pp. 227-268
Howe, J. (2006). The rise of crowdsourcing. Wired. Blog: http://www.wired.com/wired/archive/14.06/crowds.html.
Ismail, S., Malone, M. S. and van Geest, Y. (2014). Exponential Organizations: Why New Organizations are Ten Times Better, Faster, and Cheaper than Yours (and What to Do about It), Diversion Books.
Seemiller, C. and Grace, M. (2016). Generation Z Goes to College, Jossey-Bass.
White, J. (2019). What is crowdsourcing and how does it work? Definition and example, TheStreet, July 22, 2019.
Orit Hazzan is a professor at the Technion's Department of Education in Science and Technology. Her research focuses on computer science, software engineering, and data science education. For additional details, see https://orithazzan.net.technion.ac.il/ .
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