Citizen Science as an Ecosystem of Engagement: Implications for Learning and Broadening Participation

Social Justice, Community Engagement, and Undergraduate STEM Education: Participatory Science as a Teaching Tool

Social justice is increasingly being seen as relevant to the science curriculum. We examine the intersection of participatory science, social justice, and higher education in the United States to investigate how instructors can teach about social justice and enhance collaborations to work toward enacting social justice. Participatory science approaches, like those that collect data over large geographic areas, can be particularly useful for teaching students about social justice. Conversely, local-scale approaches that integrate students into community efforts can create powerful collaborations to help facilitate social justice. We suggest a variety of large-scale databases, platforms, and portals that could be used as starting points to address a set of learning objectives about social justice. We also describe local-scale participatory science approaches with a social justice focus, developed through academic and community partnerships. Considerations for implementing participatory science with undergraduates are discussed, including cautions about the necessary time investment, cultural competence, and institutional support. These approaches are not always appropriate but can provide compelling learning experiences in the correct circumstances.

Improving microbial phylogeny with citizen science within a mass-market video game

Citizen science video games are designed primarily for users already inclined to contribute to science, which severely limits their accessibility for an estimated community of 3 billion gamers worldwide. We created Borderlands Science (BLS), a citizen science activity that is seamlessly integrated within a popular commercial video game played by tens of millions of gamers. This integration is facilitated by a novel game-first design of citizen science games, in which the game design aspect has the highest priority, and a suitable task is then mapped to the game design. BLS crowdsources a multiple alignment task of 1 million 16S ribosomal RNA sequences obtained from human microbiome studies. Since its initial release on 7 April 2020, over 4 million players have solved more than 135 million science puzzles, a task unsolvable by a single individual. Leveraging these results, we show that our multiple sequence alignment simultaneously improves microbial phylogeny estimations and UniFrac effect sizes compared to state-of-the-art computational methods. This achievement demonstrates that hyper-gamified scientific tasks attract massive crowds of contributors and offers invaluable resources to the scientific community.

Building citizen science intelligence for outbreak preparedness and response: a mixed-method study in nine countries to assess knowledge, readiness and feasibility

INTRODUCTION

Citizen science (CS) is an emerging approach in public health to harness the collective intelligence of individuals to augment traditional scientific efforts. However, citizens' viewpoint, especially the hard-to-reach population, is lacking in current outbreak-related literature. We aim to understand the awareness, readiness and feasibility of outbreak-related CS, including digitally enabled CS, in low-income and middle-income countries.

METHODS

This mixed-method study was conducted in nine countries between October 2022 and June 2023. Recruitment through civil society targeted the general population, marginalised/indigenous groups, youth and community health workers. Participants (aged ≥18 years) completed a quantitative survey, and a subset participated in focus group discussions (FGDs).

RESULTS

2912 participants completed the survey and 4 FGDs were conducted in each country. Incorporating participants' perspectives, CS is defined as the practice of active public participation, collaboration and communication in all aspects of scientific research to increase public knowledge, create awareness, build trust and facilitate information flow between citizens, governments and scientists. In Bangladesh, Indonesia, the Philippines, Cameroon and Kenya, majority were unaware of outbreak-related CS. In India and Uganda, majority were aware but unengaged, while in Nepal and Zimbabwe, majority participated in CS before. Engagement approaches should consider different social and cultural contexts, while addressing incentivisation, attitudes and practicality factors. Overall, 76.0% expressed interest in digital CS but needed training to build skills and confidence. Digital CS was perceived as convenient, safer for outbreak-related activities and producing better quality and quantity of data. However, there were concerns over non-inclusion of certain groups, data security and unclear communication.

CONCLUSION

CS interventions need to be relatable and address context-specific factors influencing CS participation. Digital CS has the potential to facilitate collaboration, but capacity and access issues must be considered to ensure inclusive and sustainable engagement.

When online citizen science meets teaching: Storyfication of a science discovery game to teach, learn, and contribute to genomic research

In the last decade, video games became a common vehicle for citizen science initiatives in life science, allowing participants to contribute to real scientific data analysis while learning about it. Since 2010, our scientific discovery game (SDG) Phylo enlists participants in comparative genomic data analysis. It is frequently used as a learning tool, but the activities were difficult to aggregate to build a coherent teaching activity. Here, we describe a strategy and series of recipes to facilitate the integration of SDGs in courses and implement this approach in Phylo. We developed new roles and functionalities enabling instructors to create assignments and monitor the progress of students. A story mode progressively introduces comparative genomics concepts, allowing users to learn and contribute to the analysis of real genomic sequences. Preliminary results from a user study suggest this framework may help to boost user motivation and clarify pedagogical objectives.

Modeling intrinsic factors of inclusive engagement in citizen science: Insights from the participants' survey analysis of CSI-COP

Inclusive citizen science, an emerging field, has seen extensive research. Prior studies primarily concentrated on creating theoretical models and practical strategies for diversifying citizen science (CS) projects. These studies relied on ethical frameworks or post-project empirical observations. Few examined active participants' socio-demographic and behavioral data. Notably, none, to our knowledge, explored prospective citizen scientists' traits as intrinsic factors to enhance diversity and engagement in CS. This paper presents a new inclusive CS engagement model based on quantitative analysis of surveys administered to 540 participants of the dedicated free informal education MOOC (Massive Open Online Course) 'Your Right to Privacy Online' from eight countries in the EU funded project, CSI-COP (Citizen Scientists Investigating Cookies and App GDPR compliance). The surveys were filled out just after completing the training stage and before joining the project as active CSs. Out of the 540 participants who completed the surveys analyzed in this study, only 170 (32%) individuals actively participated as CSs in the project. Therefore, the study attempted to understand what characterizes these participants compared to those who decided to refrain from joining the project after the training stage. The study employed descriptive analysis and advanced statistical tests to explore the correlations among different research variables. The findings revealed several important relationships and predictors for becoming a citizen scientist based on the surveys analysis, such as age, gender, culture, education, Internet accessibility and apps usage, as well as the satisfaction with the MOOC, the mode of training and initial intentions for becoming a CS. These findings lead to the development of the empirical model for inclusive engagement in CS and enhance the understanding of the internal factors that influence individuals' intention and actual participation as CSs. The devised model offers valuable insights and key implications for future CS initiatives. It emphasizes the necessity of targeted recruitment strategies, focusing on underrepresented groups and overcoming accessibility barriers. Positive learning experiences, especially through MOOCs, are crucial; enhancing training programs and making educational materials accessible and culturally diverse can boost participant motivation. Acknowledging varying technological proficiency and providing necessary resources enhances active engagement. Addressing the intention-engagement gap is vital; understanding underlying factors and creating supportive environments can transform intentions into active involvement. Embracing cultural diversity through language-specific strategies ensures an inclusive environment for effective contributions.

The Eyewitness Community Survey: An Engaging Citizen Science Tool to Capture Reliable Data while Improving Community Participants' Environmental Health Knowledge and Attitudes

Many youths and young adults have variable environmental health knowledge, limited understanding of their local environment's impact on their health, and poor environmentally friendly behaviors. We sought to develop and test a tool to reliably capture data, increase environmental health knowledge, and engage youths as citizen scientists to examine and take action on their community's challenges. The Eyewitness Community Survey (ECS) was developed through several iterations of co-design. Herein, we tested its performance. In Phase I, seven youths audited five 360° photographs. In Phase II, 27 participants works as pairs/trios and audited five locations, typically 7 days apart. Inter-rater and intra-rater reliability were determined. Changes in participants' knowledge, attitudes, behaviors, and self-efficacy were surveyed. Feedback was obtained via focus groups. Intra-rater reliability was in the substantial/near-perfect range, with Phase II having greater consistency. Inter-rater reliability was high, with 42% and 63% of Phase I and II Kappa, respectively, in the substantial/near-perfect range. Knowledge scores improved after making observations (p ≤ 0.032). Participants (85%) reported the tool to be easy/very easy to use, with 70% willing to use it again. Thus, the ECS is a mutually beneficial citizen science tool that rigorously captures environmental data and provides engaging experiential learning opportunities.

Drivers of support for citizen science across state wildlife management agencies in the United States

Citizen science (CS) is gaining recognition as a valuable approach to meet data needs for environmental projects while fostering collaboration between scientists and members of the public. Despite increasing implementation of CS by natural resource entities, organizations' motivations for engaging in CS remain poorly understood. We examined the utility of the theory of planned behavior (TPB) and social exchange theory (SET) in identifying factors influencing support of CS by scientific organizations. To test predictions of the TPB and SET theories, we surveyed (quantitative, web based) state wildlife agency staff in the United States on their perceptions of organizational engagement in CS. We divided questions that measured TPB items into individual and organizational components to address the influence of personal- and organization-level decision-making on staff perceptions and attitudes. We used structural equation modeling to identify key constructs that influence staff support of CS in state wildlife agencies. The survey yielded 627 responses across 44 states. Both TPB and SET constructs accurately predicted staff support of CS; however, measures from SET (e.g., public engagement benefits and costs of CS to scientific credibility) were most influential (i.e., TPB constructs had less impact). Our findings indicate that organizational support for CS is primarily influenced by assessment of trade-offs among perceived costs and benefits. Indicators of support for CS were further elucidated by including measures from the TPB model. Based on our results, we suggest that natural resource entities give careful consideration to CS project design, develop thorough communication and data management plans, and practice iterative evaluation of CS project productivity.