The committee found the planning literature and the learner-centered design literature to be valuable resources in understanding the evolution of citizen science as an enterprise that supports learning. These resources were consistent in their characterization of the history of design. Both literatures are aligned in organizing the evolution of human-centered design into two broad eras. In its early evolution, prior to 1970, human-centered design was in its infancy. Users and learners were largely seen as pliable, mold-able to fit the needs of program designers. By and large, the citizen science projects of the time were aligned with this ethos. Growing out of decision theory and decision science, the Program Planning Model (PPM) offered by Delbecq and Van de Ven (1971) was one such instantiation of this view of design. It had the goal of providing an orderly process for structuring decision-making at different phases of planning. The PPM included five phases with each phase requiring a different combination of stakeholders including (1) problem exploration, (2) knowledge exploration, (3) priority development, (4) program development, and (5) evaluation. Influenced by Taylorism, designers believed that participants could be “planned” for, with little in the way of input from participants. For example, in 1981, Boyle surveyed program development models and found that most models used three specific phases in program planning: (1) program planning, (2) program design and implementation, and (3) program evaluation and accountability. While the broad brush of linearity fails to capture all the programs of the time, it is true that this era produced many classically contributory projects that did not foreground the needs of learners. Early citizen science experiences were sharply focused on participants collecting data and pro-
viding data to scientists (Bonney et al., 2009). As long as the aspirations of designers were limited to constrained roles for participant-learners, linear and relatively noninteractive approaches to design adequately supported individual learners. The attendant learning design challenges, are what scholars like Nelson and Stolterman (2003) would call tame. Tame, here, suggests that, among other things, the problems are straightforward and the solution, while perhaps requiring many steps, is known at the start of the exercise.
When citizen science programs take up ambitious learning goals, the attendant design bar is raised. Following Nelson and Stolterman’s nomenclature, these newer aspirations place citizen science learning challenges firmly in the realm of wicked problems. Wicked problems—unlike tame problems that can be clearly and exhaustively formulated from the start—are complex, multicausal, and cannot be fully understood from the beginning. The components contributing to the wicked problem in citizen science are the balance between data quality, data quantity, and the varied learning outcomes; the complex ways the desired balance can be achieved; the diversity in the learning contexts; and the potential tension among the outcomes and the means of achieving them.
With the increased attention to the wicked aspects of citizen science, there has been a growing recognition that citizen science programs can lead to rich, educational experiences. Many in the field have recognized that there is a tremendous opportunity to support rich learning on the part of participants (Ballard, Dixon, and Harris, 2017; Bonney et al., 2009; Masters et al., 2016). Learning that extends beyond tame aspirations is more likely to be addressed in intentional structures that are designed for the programs. We know that learning can be supported in formal, nonformal, informal, incidental, and everyday structures (Heimlich and Reid, 2016) within citizen science programs. To realize complex learning opportunities in cognitive, affective, and social realms, citizen science programs need more powerful approaches to design.
In the second era of human-centered design, designers recognized that while learners are indeed malleable, they are not infinitely pliable. Context matters when trying to construct a designed artifact that is both useful and usable. This context includes the participants’ own prior experiences. Past learning experiences will shape future learning. The social setting matters as well. What supports learning for well-off people might not work for those who are economically changed. Local politics matters to belief, and beliefs matter to learning. Modern design practices have evolved to see these, and other aspects of the human experience, and to register their import to the construction of designed artifacts that allow for complex learning.
In the late 1990s and early 2000s the field of learning design made a pivot in the conception of human services and adult learning with the move-
ment toward asset-based community development (e.g., Bohach, 1997; Bradshaw, 2007; Greene and Haines, 2009; Kretzmann, McKnight, and Puntenney, 2005; Lerner, 2003; Mathie and Cunningham, 2003; Snow and DicKard, 2001). The major shift in this approach to service construction was seen in more collaboratively produced planning; this approach became known as consultant-based models. The idea of the consultant model is that the change agent works within the community to facilitate community engagement in its own planning process. Most community development models include steps such as understanding the context for planning, developing links with the public, facilitating an inventory of needs and assets (Rennekamp, 1999), fostering engagement of community actors, involving locals to resist or support a cause or issue, helping community residents understand what is happening and recognize choices (e.g., Friere, 1970), and working collectively to address common interests (e.g., Brundage and MacKeracher, 1980). In our review, it appears that most recent citizen science projects are using either a linear or consultant model in designing to achieve their scientific goals. These projects grow from what the study needs, and then build the program to ensure the output of scientific data. Community-based participatory research is also a consultant model but grows from the community-development models driven by the needs in the community with the study being designed to address the data needed to answer the community’s question(s). This shift has demanded a new approach to design that is more user centered (Norman, 2013) and learner centered (Soloway, Guzdial, and Hay 1994). The body of design knowledge puts pressure on service construction to make the needs of users a priority. This shift toward user-centered design marked the abandonment of what might be described as a waterfall model of design in favor of iterative and rapid prototyping to arrive at useful and usable services.
Ballard, H.L., Dixon, C.G.H., and Harris, E.M. (2017). Youth-focused citizen science: Examining the role of environmental science learning and agency for conservation. Biological Conservation, 208, 65-75.
Bohach, A. (1997). Fundamental principles of asset-based community development. Journal of Volunteer Administration, 15(4), 22-29.
Bonney, R., Cooper, C.B., Dickinson, J., Kelling, S., Phillips, T., Rosenberg, K.V., and Shirk, J. (2009). Citizen science: A developing tool for expanding science knowledge and scientific literacy. BioScience, 59(11), 977-984.
Boyle, P.G. (1981). Planning Better Programs. New York: McGraw-Hill.
Bradshaw, T.K. (2007). Theories of poverty and anti-poverty programs in community development. Community Development, 38(1), 7-25.
Brundage, D.H., and MacKeracher, D. (1980). Adult Learning Principles and Their Application to Program Planning. Toronto, ON: Ministry of Education.
Delbecq, A.L., and Van de Ven, A.H. (1971). A group process model for problem identification and program planning. The Journal of Applied Behavioral Science, 7(4), 466-492.
Friere, P. (1970). Pedagogy of the Oppressed. New York: Continuum.
Haines, A. (2009). Asset-based community development. In R. Phillips and R.H. Pittman, An Introduction to Community Development (pp. 38-48). New York, NY: Routledge.
Kretzmann, J.P., McKnight, J., and Puntenney, D. (2005). Discovering Community Power: A Guide to Mobilizing Local Assets and Your Organization’s Capacity. Evanston, IL: Asset-Based Community Development Institute, School of Education and Social Policy, Northwestern University.
Lerner, R.M. (2003). Development assets and asset-building communities: A view of the issues. In R.M. Lerner and P. Benson (Eds.), Developmental Assets and Asset-Building Communities (pp. 3-18). Boston, MA: Springer.
Masters, K., Oh, E.Y., Cox, J., Simmons, B., Lintott, C., Graham, G., Greenhill, A., and Holmes, K. (2016). Science learning via participation in online citizen science. Journal of Science Communication, Special Issue: Citizen Science, Part II, 15(3), A07-133. doi: https://doi.org/10.22323/2.15030207.
Mathie, A., and Cunningham, G. (2003). From clients to citizens: Asset-based community development as a strategy for community-driven development. Development in Practice, 13(5), 474-486.
Nelson, H.G., and Stolterman, E. (2003). The Design Way: Intentional Change in an Unpredictable World: Foundations and Fundamentals of Design Competence. Cambridge, MA: MIT Press.
Norman, D. (2013). The Design of Everyday Things: Revised and Expanded Edition. New York: Basic Books.
Rennekamp, R.A. (1999). Planning for Performance: Developing Programs That Produce Results. Lexington: University of Kentucky Cooperative Extension Service.
Snow, L.K., and DicKard, S. (2001). The Organization of Hope: A Workbook for Rural Asset-based Community Development: A Community Building Workbook. Evanston, IL: Asset-Based Community Development Institute, Institute for Policy Research, Northwestern University.
Soloway, E., Guzdial, M., and Hay, K.E. (1994). Learner-centered design: The challenge for HCI in the 21st century. Interactions, 1(2), 36-48.