Enhancing Motivation in a Mobile Participatory Sensing Project through Gaming Kyungsik Han, Eric A. Graham, Dylan Vassallo, Deborah Estrin Center for Embedded Networked Sensing University of California, Los Angeles Los Angeles, California {kshan, destrin}@cs.ucla.edu,
[email protected],
[email protected] Abstract— BudBurst Mobile is a smartphone application for an environmental Participatory Sensing project that focuses on observing plants and collecting plant life stage data. The app was initially designed for record-keeping and motivation to participate in this project has been based on improving scientific knowledge. To test other methods for motivating data collection and increasing user retention, we added an outdoor game activity, similar to geocaching, called floracaching. Players gain points and levels within the game by finding and making qualitative observations on plants. Location-based information is included in the game with the display of local lists of plant species occurring in a user’s area derived from governmental data sources. Additionally, user-collected data and the occurrence of species on the local lists obtained from the photo-sharing website Flickr are displayed on an interactive map. Administrator targeting of individual plants facilitates expert control over crowd-sourced data collection for species of interest. We evaluated these additional features with the help of 50 volunteers playing on the UCLA campus as a case study. Results indicated that participants were highly motivated by the floracaching game, and next-most by the knowledge that environmental scientists will use the data collected for studying the effects of global climate change. Other motivating features included sharing plant observations with other users and the information contained in the local lists of plants. Keywords—participatory sensing, mobile phones, sourcing, location based services, sustainability, games
I.
crowd-
INTRODUCTION
Participatory Sensing (PS), Crowd-sourced Science, Citizen Science, and Public Participation in Scientific Research are terms used to refer to research collaborations that enable nonscientist members of the public to assist with scientific investigations that can also be policy-oriented [1,8]. Public engagement in scientific and technological issues has dramatically grown in popularity in recent years, being described as having “gone viral” [18]. Participatory scientific data gathering can provide substantial and unique datasets that have the potential for greatly increasing the volume of observational data for research. PS projects includes (for example) asking participants to classify photographs of galaxies [14], reporting bird sighting data for ornithological research [15], planting sunflowers and observing bee pollination [16], keeping a diet diary [3] and reporting potholes on the road [2]. Innovations such as social networking and data visualizations have been shown to generate public interest in a Work supported by NSF grant #CNS-0627084.
PS project and thus significantly increase the size of the dataset collected [17]. An important shift in mobile phone usage, from communication tool to “networked mobile personal measurement instrument” [29], is expanding the number of individuals that are participating in PS activities. Indeed, increasing participation by a sufficient number of people (the “chasm of critical mass”) is considered key to the success of PS projects [22] and leveraging mobile phones shows great promise to enable non-traditional PS participants to become active data collectors [29]. Because it often takes a long time for the output of a PS project to be made public, it is suggested that motivation for participation be created from intrinsic and collective motives [19] rather than from final results. Games have been used in PS projects to increase participation and the amount of data collected or processed [e.g., 14]. Indeed, games seem to be a natural extension for some PS projects with research indicating that the top motivation for participation is enjoyment with secondary motivations involving identification with the goals of the project or other extrinsic factors [19]. Notably, games created for sustainability efforts, designed to influence everyday activities, have been explored with an emphasis on the importance of relevant and authentic, real-world tasks in improving learning and changing behavior [20, 29]. Project BudBurst1 is a national PS program that involves non-scientists in the collection of data on the timing of life cycles of plants and BudBurst Mobile is the mobile phonebased extension to this project. Historically, motivation to participate in Project BudBurst has been based on contributing to scientific knowledge about plants and climate change. Although BudBurst Mobile was developed primarily as a mobile phone record-keeping tool for Project BudBurst, it is also an example of a technology that can support civic engagement in issues that are of social concern [25, 29], and is thus an ideal platform for testing methods of secondary engagement. Our goal with this study was to test how new features added to the BudBurst Mobile record-keeping application would engage participants beyond the extrinsic motivations of helping scientists. These features support an outdoor game activity that involves accumulating points and gaining levels by making outdoor observations on plants and include:
1
http://budburst.org
•
Local Plant Lists. Location-based information on local plants obtained from the USDA PLANTS database is displayed as a set of lists, both to satisfy general interest by participants and to simplify the task of species identification prior to data collection by players to gain points. Additionally, expert-generated local plant lists can be established for targeted (e.g., classroom) data collection by players.
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Local Plant Maps. Player observations on plants are displayed on an interactive map that also allows points to be gained by other players. Additionally, “opportunistic observations” (geo-located plant images) are displayed from the social photo sharing website Flickr. Both sets of data are displayed within about a 40 km (25 mile) radius of the player’s location.
•
Floracaching. This targeted search-and-discover aspect of the game was included to motivate non-traditional citizen scientists to collect plant observations, to encourage outdoor urban greenspace use and appreciation, and to aid greenspace managers with sustainability efforts.
To better understand how the information about local plants, the display of the locations of player observations, and the floracaching activity encouraged participation, we tested the updated BudBurst Mobile application with the help of 10 volunteers playing on the UCLA campus for two weeks in May and June 2011 and of 40 volunteers playing for one hour in July. The rest of this case study is organized as follows: First, we explain the background of Project BudBurst and then the methodology for adding the motivational new features to the BudBurst Mobile app. The overall architecture of Budburst Mobile is then explained and we cover the pilot study dealing with users’ data collection and experiences with the new features. Finally, we evaluate the results of study. II.
BACKGROUND
Project BudBurst is an online PS network of more than 10,000 registered participants, many of whom are school children, who monitor plants as the seasons change and is the premier PS project of the National Environmental Observatory Network (NEON2). It is designed mostly for climate change education and engages participants in the collection of climate change data based on the timing of leafing, flowering, and fruiting of plants (plant phenology). Data from phenological studies such as Project BudBurst can be used to evaluate the effects of climate change at both the individual species and aggregate levels and are increasingly relevant for addressing applied environmental and sustainability issues [21]. As an online community, Project BudBurst is well positioned to utilize mobile technology to enhance the user experience. The project is co-managed by the National Ecological Observatory Network and the Chicago Botanic Garden3 and data collected by participants is made freely available to the public. The BudBurst Mobile app for Android was initially designed to make data collection by PS participants easier and more convenient. It allowed participants to record and keep a “diary” of their phenological observations as well as easily upload 2
http://neoninc.org; 3http://chicagobotanic.org;
photos. Automatic time-stamps and drop-down lists of phenological stages standardized data collection and observations were uploaded immediately from the phone, rather than relying on a secondary step of visiting the website. Because Project BudBurst is primarily an educational program, participation for reasons other than contributing to science are welcome and encouraged, although this has been shown to be discouraging to some PS community members [30]. III.
RATIONALE AND DESIGN
The goal of adding gaming components to BudBurst Mobile was to motivate individuals to collect more plant information and be engaged in learning activities. The game has two general ways to gain points: (1) through observations made on plants by user-initiated contributions using local plant lists and through the interactive map, and (2) through the targeted floracaching activity where players accumulate points in a search-and-discover game. Both lead to accumulating points that allow players to reach different levels in the game and allow eventual establishment of their own floracaches for other players to find. A. Location-Based Plant Information Managers of urban greenspaces are already incorporating digital information provisioning (multimedia DVDs, websites) to their sites, examining methods to encourage greater use by the public [12]. For example, an experiment with locationbased information services enabled users to find content about natural areas on their mobile phones, encouraging outdoor activities [13]. We added lists of species in the BudBurst Mobile app to increase interest in plants. However, because of the potentially large number of species in any greenspace area, overcoming “information overload” that might hinder inquiry was key [24]. The USDA PLANTS4 database can be queried to provide a list of plant species names that occur at a countylevel granularity within the U.S. The CalPhotos5 database of plant images provides over 20,000 copyright-free images of plants. We use these data sources to display lists of the local invasive, local poisonous, and local endangered plants for participants to browse. Additionally, we display the local subset of the 189 recommended Project Budburst plants that occurs for a participant’s location, providing convenient and user-relevant updated information [23]. User-defined lists displayed on the smartphone can also be created by BudBurst “expert” participants through a web-based interface. We anticipated that the user-defined plant lists feature would be useful for school groups or other organizations where a tailored list of local plants is desired. Indeed, a group of UCLA students has been mapping the trees on campus for the Sustainability6 program using the user-defined local list feature populated with information derived from historical tree-planting records. B. Location-Based Local Maps Parks and urban greenspaces can be highly valued by residents and visitors, but often only if they fulfill the specific urban residents’ needs for multiple leisure, recreation, and social activities [5]. Public participation in environmental monitoring of urban areas has been indicated to contribute to 4 6
http://plants.usda.gov; 5http://calphotos.berkeley.edu; http://www.sustain.ucla.edu
increasing the knowledge of the conditions in the local environment and at the same time promote participants’ involvement in environmental protection [11], improving sustainability. Indeed, an understanding of why people choose to visit particular urban greenspaces is crucial to access management and sustainability [9]. Offering new recreation opportunities to compliment more traditional patterns of greenspace use increases their perceived value leading to a higher degree of sustainability [7]. The local maps feature (Fig. 1) was designed to engage participants in plant discovery in urban and greenspace locations. The map displays the participant’s own plant observations, other participants’ observations, and the locations of plant images captured by community members. Initiating an observation may be made using the map interface.
(Fig. 2). Badges are assigned with each level with anthropomorphized images of plants. Detailed rules about who may establish a floracache, where, and what points are awarded can be found on the floracaching website7. An administrator-level user can establish a set of floracaches for an area, allowing urban greenspace managers to target plants of interest for public monitoring. Targeting plants will facilitate crowd-sourced observations, useful for gathering maintenance information and data used for sustainability efforts. The administrator function also allows seasonally changing targeted data collection on plants with known phenological cycles.
Figure 2. (Left) Medium level floracaches, (Middle) the detailed view of one floracache with live compass and distance, and (Right) floracache ranks. Figure 1. (Left) The main page of Budburst mobile. (Right) Local plant map.
Because an empty map might discourage an otherwise eager participant, “seeding” observations [26] with data from Flickr was designed to overcome this initial scarcity. Thus, “opportunistic observations” are also added to the map from geo-located plant images obtained from the social photo sharing website Flickr. C. Floracaching Combining social networking with entertainment in “Social Games With A Purpose” [19, 20, 24] has been suggested to increase engagement and participation in PS projects. Thus, we established “floracaching” as a game similar to geocaching, where a location has been identified using GPS coordinates for a player to try to find. In floracaching, a plant occurs at the published location that participants then search for. Finding a floracache consists of being within range of the plant, capturing a photo, and making a phenophase observation. It has been indicated that the need to allow volunteers to start contributing at lower-level tasks and progress to more demanding tasks is important for participation [19]. Thus, floracaching has three levels of difficulty: easy (with a live map interface for navigation) that offers the smallest number of points per capture, medium (Fig. 2; with a compass direction and distance indication), and hard (with only a description of the location of the plant) with the most points. Ranks or levels are obtained after accumulating sufficient points in the game, ranging from “Sprout”, with the fewest points, through “Seedling”, ”Thriving”, and “Deep-Rooted” as the highest rank 7
http://floracaching.org
Social interactions and social networking have been indicated as motivational for PS projects [28]. Thus, the detail information page of all difficulty levels of floracaches was included to display the names of the participants, photos, and comments. The name of the player who originally created the floracache is always associated with the floracache as a motivational aspect and his or her profile is available on the floracaching website. IV.
SYSTEM ARCHITECTURE AND FUNCTIONALITY
The features of BudBurst Mobile (Fig. 1) discussed in this paper reflect new work to increase engagement and retention of participants. The two new general features are location-based services and gaming, both of which are tied to participant data collection and are displayed to participants in a variety of formats on the mobile device. A. Location-Based Plant Information A simplified description of the request/response cycle (from the phone to the server and back) for generating a local list is as follows (Fig. 3): When the application is launched, it sends its latitude and longitude to the backend server (a). The server then makes a request to the SimpleGeo Context API (b) in order to resolve the (latitude, longitude) pair into a (state, county) pair. The server sends the state, county, and codes indicating either invasive, poisonous, endangered, or all local plants as search parameters to the USDA PLANTS database (c), which then returns results in CSV format. Each species returned in the
USDA results is matched with a URL to a thumbnail photo of that species from the CalPhotos project (d), if it exists. If necessary, the server starts a geolocated photo search in Flickr, based on the names of local species (e). The server then prepares and caches the results (f). When the user initiates a manual request to download the lists, the server returns the data to the mobile phone in JSON format (g). The Budburst Mobile application receives the results, downloads any images referenced by URLs in the results, caches the data locally, and displays the list to the user.
Figure 3. The flow of a local list generation
User-defined lists are created by BudBurst “expert” participants through a web-based interface and are associated with a latitude and longitude, allowing the system to retrieve these local plants in a similar manner as a query to the USDA PLANTS database B. Location-Based Local Maps 1) User Data: A simplified description of the request/response cycle is as follows: (1) The application sends the latitude and longitude to the server, (2) the server searches for previously cached results and returns them if possible, and (3) if no cached data exists, the server initiates one or more Flickr searches and returns up to 50 of the results to the phone in JSON format. When a participant clicks the plant icon within the mobile app, a pop-up box is displayed containing the thumbnail image, species name, and date of the last observation made by a participant of the plant. After clicking the box, more information with species name, the username who made the observation, date, the thumbnail image, and notes made by the last participant who observed the plant are shown. 2) Flickr Data: Because multiple Flickr searches for geotagged images may require a significant amount of time to complete, we limited the number of searches to only the species names obtained from the USDA local lists (described above). In addition to the latitude and longitude, 12 other refining search parameters are used to limit results, including a filter for selecting images that have free-use attribution licenses, a filter for removing explicit photos, and an ordering of results to return geographically nearby images first.
C. Floracaching The floracaching activity is supported on both the mobile application and the back-end server and leverages the data collection and record-keeping functions of the original BudBurst Mobile app. Floracaches may be established by any user, given that the user has accumulated sufficient points within the game, by converting a public plant observation made through the BudBurst Mobile interface into a floracache using the floracaching website. For the easy level, participants are presented with the map on the mobile phone and their current location is displayed along with all the easy floracaches that are within 1 km (0.6 miles). The participant’s position is updated continuously through GPS. If the participant is within 10 meters (30 feet) of a floracache then he or she will be presented with the detail information of the observation after touching the nearby floracache icon on the map. Otherwise, a notice is displayed indicating that the user is not within range. From this page, the participant can then make a new observation on the floracache by recording phenophase information. The floracache is thus “captured”, removed from the participant’s view of available floracaches, and points are awarded on the server. For the medium difficulty level, the application displays the floracaches in a list and each item in the list includes a thumbnail image, species name, and the distance and direction to the floracache relative to the participant’s current location. The direction is calculated by using the accelerometer sensor on the phone. Distance and direction to the floracaches are updated as the participant moves, and if a floracache is close enough, then the participant is then allowed to make an observation, similar to the method for the easy level. For the hard level, the application displays the floracaches in a list and each item in the list includes only a thumbnail image, species name, and a text description of the location of the floracache. The description can be anything, but ideally indicates the location of the floracache. For example, “A pair of trees at the west entrance of Boyer Hall, the right one when facing the entrance”. Since this level provides less information than other two levels, and has the potential to be difficult (e.g., if the description is a riddle), this level is rated as “hard”, although some text clues may actually be relatively easy to decipher. V.
EVALUATION
To test the new features for motivating participants, we recruited a total 50 volunteers who had never previously participated in Project BudBurst to collect plant observation data on campus. Ten volunteers for a two-week trial were from the UCLA Computer Science department and either used their own or a loaned Android smartphone. Forty volunteers for a one-hour trial were from a group of East Los Angeles Community College students visiting UCLA. The volunteers tested the local plant maps feature and the floracaching game involving plants previously established on the campus.
A. Plant Observations by Volunteers Volunteers for the two-week trial were asked to access the local plant maps to view six other participant’s BudBurst observations and the results from Flickr searches. In addition, volunteers were told that it was possible to examine other locations within the continental U.S. for local plant lists. Subsequently, volunteers examined 223 locations across the U.S. by clicking on the map within the application (Fig. 4). Feedback from the volunteers indicated that this feature was interesting and viewing local plants that had been observed by others was particularly appealing.
required 1.7 and hard 2.2 times as long to find as the easy floracaches. Volunteers indicated that they easily understood how to find the plants from the easy and medium levels. Several volunteers indicated that while trying to find a floracache they received weak or incorrect GPS signals. Such erroneous GPS data was common enough to delay or prevent the capturing of a floracache, even though the participants had physically found the plant. We determined that the GPS chipset on the Samsung Galaxy S was more prone to this kind of error than the others we tested. Problems with the accuracy of GPS in urban environments are not uncommon [27]. The information provided within the mobile application to find floracaches was ranked from the most helpful to the least helpful (Fig. 5). The map function was the most useful for participants for finding floracaches and the plant name (species and common names) were regarded as the least helpful. This is not surprising, considering the group of volunteers were mostly computer scientists and not biologists.
Figure 4. The requested points by the participants.
The amount of time required to access other participants’ observations and to get results from the Flickr search was quantified for seven previously uncached locations. The first time a request was made to an uncached location required on average 32.7 ± 5.1 s (mean ± S.D.) to complete. The second time that the location was accessed, after it had been cached on the server, required 2.2 ± 5.1 s. Caching the location data significantly reduced subsequent requests for information and provided users quick access to popular search locations.
Figure 5. Volunteers’ ranking of the helpfulness of information provided for finding floracaches in the range of least helpful (0) to most helpful (10). Data are means ± S.D.
For the floracaching game, the combined 50 participants found floracaches 133 times on the UCLA campus, with an average of 5.8 ± 3.8 floracaches per person for the two-week study and 3.7 ± 2.2 floracaches per person for the one-hour study. A relative ranking of how fast a floracache was discovered on campus indicated that the medium floracaches
B. User Motivation Survey After collecting plant observations, volunteers were asked to answer an online survey about the motivating potential of the features for increasing participation. Answers were ranked from the least motivating (1) to the most motivating (10). The floracaching game was highly ranked (7.9 ± 1.5) for encouraging participation in collecting plant-related observations. Knowing that the data collected was environmentally important was indicated as the next-most motivating (6.3 ± 3.4), followed by sharing personal observations on a public map (5.8 ± 2.8) and attaining a rank from accumulated points within the floracaching game (5.6 ± 2.4). The least motivating aspect of the application was the record keeping of the number of plants observed (5.1 ± 1.7). These results are not surprising, considering the research that suggests participation from intrinsic motives, such as the enjoyment of game activities, is very important [19]. However, aspects within the floracaching game, such as gaining points and attaining ranks, was less valued than simply playing the game, although competition among the players was evident. These results may reflect the relatively short test period where time was limiting to gain enough points to be motivating. The survey included hypothetical new features that volunteers thought would also be motivating if included in the application. The most motivating was having environmental news items be available from within the application (7.0 ± 2.9), being able to publicly comment on other participants’ plant observations (6.9 ± 2.8), and being notified of other participants making observations on plants that you had first observed (6.7 ± 3.2), indicating that additional social networking aspects were highly desired. Surprisingly, receiving information from scientists about how they are using the collected data ranked relatively low (6.3 ± 3.7), even though this is indicated as a key motivator in participatory sensing projects [11]. Additional comments written-in by the volunteers indicated that the floracaching game was particularly appealing because
it allowed the volunteers to see other volunteers’ photos and comment on their captured floracaches. Such social interactions have been indicated as motivational [28]. Also indicated as helpful was the ability to identify plants that the volunteers were previously not familiar with. VI.
CONCLUSION AND FUTURE WORK
We designed and tested a system that leverages locationbased plant information from the USDA and Flickr and also features a game as further motivation to participate in data collection. Volunteers were generally highly motivated by the floracaching game and secondarily by motivations associated with contributing to scientific understanding and sharing plant observations with other users. Because local plants were discoverable using the application, users also indicated the learning about the plants around them enhanced their experience. Adding games and social interactions to participatory sensing projects should improve recruitment and retention and ultimately lead to more data collected by more engaged participants within this PS project. Ideally, testing engagement by players will be possible in the future using a control group of volunteers that do not have access to the gaming aspects. Follow-up research to examine whether the perceived motivators are real motivators and how GPS errors, which might be frequent in specific areas, are handled by players. Improving locational algorithms using both GPS and WiFi on the mobile phones to get more accurate location information is continuing. REFERENCES J. Burke, et al. “Participatory sensing”. 2006. In ACM Sensys World Sensor Web Workshop. [2] J. Eriksson, et al. “The Pothole Patrol : Using a Mobile Sensor Network for Road Surface Monitoring”. 2008. ACM MobiSys. [3] S. Reddy, et al. “Image Browsing, Processing and Clustering for Participatory Sensing: Lessons From a DietSense Prototype”. 2007. In Proceedings of the 4th Workshop on Embedded Networked Sensors. [4] J. Froehlich, et al. “UbiGreen : Investigating a mobile tool for tracking and supporting green transportation habits”. 2009. Proc. CHI’09. [5] M. Bonnes, P. Passafaro, and G. Carrus. 2011. The Ambivalence of Attitudes Toward Urban Green Areas: Between Proenvironmental Worldviews and Daily Residential Experience. Environment and Behavior 43:207–232. [6] A.A. Mabelis and G. Maksymiuk. 2009. Public participation in green urban policy: two strategies compared. International Journal of Biodiversity Science, Ecosystem Services & Management 5:63 – 75. [7] A. Loukaitou-Sideris. 1995. Urban Form and Social Context: Cultural Differentiation in the Uses of Urban Parks. Journal of Planning Education, and Research 14: 89-102. [8] M.C. Powell and M. Colin. 2008. Meaningful Citizen Engagement in Science and Technology: What Would it Really Take? Science Communication 30: 126-136. [9] D. Liley, J. Mallord and M. Lobley. 2005. The “Quality” of Green Space: features that attract people to open spaces in the Thames Basin Heaths area. English Nature Research Report XX. English Nature, Peterborough. [10] J. Burgess, C.M . Harrison and M. Limb. 1988. People, Parks and the Urban Green: A Study of Popular Meanings and Values for Open Spaces in the City. Urban Studies 25:455-473. [11] C. Gouveia, A. Fonseca, A. Câmara, F. Ferreira. 2004. Promoting the use of environmental data collected by concerned citizens through
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