Citizen science projects that rely on human computation can attempt to solicit volunteers or use paid microwork platforms such as Amazon Mechanical Turk. To better understand these approaches, this paper analyzes crowdsourced image label data sourced from an environmental justice project looking at wetland loss off the coast of Louisiana. This retrospective analysis identifies key differences between the two populations: while Mechanical Turk workers are accessible, cost-efficient, and rate more images than volunteers (on average), their labels are of lower quality, whereas volunteers can achieve high accuracy with comparably few votes. Volunteer organizations can also interface with the educational or outreach goals of an organization in ways that the limited context of microwork prevents.

Participant disengagement in citizen science tasks remains a significant challenge for crowdsourcing platform creators, in their efforts to generate meaningful data and connect with their membership. Reinforcement learning is increasingly used to take advantage of the plethora of available data to learn to sequence tasks for participants. To this end, we extend the reinforcement learning techniques used in Tile-o-Scope Grid, an image matching web game, by introducing an adaptive Q-learning based approach that incorporates participant performance in sequencing the difficulty of levels. We compared our adaptive version against both a previous non-adaptive algorithm, as well as a greedy approach. We found that the adaptive extension outperformed both, in terms of total reward. This work contributes to the growing literature on reinforcement learning approaches applied to citizen science.

Science is increasingly being achieved through citizen science (CitSci) — research conducted in part by non-professional scientists. In order to increase recruitment and retention, CitSci is sometimes gamified. Yet, research suggests that being a game does not alone make CitSci more enticing; rather, retention is dependent on the player’s experience. One recent trend in gaming is Augmented Reality (AR), as demonstrated by games like Pokémon Go. In this work, we examine the potential benefits of applying AR to CitSci gaming. We conducted a user study where we invited participants to play two citizen science adaptations (browser and AR) of the traditional game of Memory. We identified four major themes through thematic analysis. We conclude that AR is a leisurely toy: while the browser interface is better suited to rapid, accessible, productive play, AR is better suited to reflective, one-time, unstructured experiences. Our findings reveal potential towards future work on exploring AR in contexts such as museums and classrooms.

Over the last decade growing amounts of government data have been made available in an attempt to increase transparency and civic participation, but it is unclear if this data serves non-expert communities due to gaps in access and the technical knowledge needed to interpret this "open" data. We conducted a two-year design study focused on the creation of a community-based data display using the United States Environmental Protection Agency data on water permit violations by oil storage facilities on the Chelsea Creek in Massachusetts to explore whether situated data physicalization and Participatory Action Research could support meaningful engagement with open data. We selected this data as it is of interest to local groups and available online, yet remains largely invisible and inaccessible to the Chelsea community. The resulting installation, Chemicals in the Creek, responds to the call for community-engaged visualization processes and provides an application of situated methods of data representation. It proposes event-centered and power-aware modes of engagement using contextual and embodied data representations. The design of Chemicals in the Creek is grounded in interactive workshops and we analyze it through event observation, interviews, and community outcomes. We reflect on the role of community engaged research in the Information Visualization community relative to recent conversations on new approaches to design studies and evaluation.

Crowdsourcing games involving image labeling tasks are commonly digital, played online, and have rules set by designers. In this work we explore the potential of tabletop image labeling games, incorporating physical elements, in-person community-based gameplay, and support for customizable rules. We developed an augmented reality game toolkit called Tile-o-Scope AR and conducted two studies. The first study demonstrates how the toolkit can facilitate in-person discussions through collaborative image labeling, and the toolkit’s potential adaptability to other games and applications. The second study, using three different activities designed for the toolkit, demonstrates the toolkit’s flexibility for creating customized experiences for audiences of different backgrounds.