Numerical analogies (or “perspectives”) that translate unfamiliar measurements into comparisons with familiar reference objects (e.g., “275,000 square miles is roughly as large as Texas”) have been shown to aid readers’ recall, estimation, and error detection for numbers. However, because familiar reference objects are culture-specific, analogies do not always generalize across audiences. Crowdsourcing perspectives has proven effective but is limited by scalability issues and a lack of crowdworking markets in many regions. In this research, we develop an automated technique for generating localized perspectives. We utilize several open data sources for relevance signals and develop a surprisingly simple model capable of localizing analogies to new audiences without any retraining from human judges. We validate the model by testing it in both a new domain and with a different linguistic audience residing in another country. We release the compiled dataset of 400,000 reference objects to the research community.

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.

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.