Exploring intrinsic and extrinsic motivations to participate in a crowdsourcing project to support blind and partially sighted students

There have been a number of crowdsourcing projects to support people with disabilities. However, there is little exploration of what motivates people to participate in such crowdsourcing projects. In this study we investigated how different motivational factors can affect the participation of people in a crowdsourcing project to support visually disabled students. We are developing “DescribeIT”, a crowdsourcing project to support blind and partially students by having sighted people describe images in digital learning resources. We investigated participants’ behavior of the DescribeIT project using three conditions: one intrinsic motivation condition and two extrinsic motivation conditions. The results showed that participants were significantly intrinsically motivated to participate in the DescribeIT project. In addition, participants’ intrinsic motivation dominated the effect of the two extrinsic motivational factors in the extrinsic conditions

An Inventory and Assessment of Street Amenities and Vacant Lots in Downtown Lewiston, Maine: Defining Potential to Create a Healthier Neighborhood

Street amenities are publicly available resources, physical or biological, that have an aesthetic, functional, and/or ecological value (i.e. street trees, benches, traffic calming measures and community gardens). Healthy Neighborhoods (HN) is an organization working to improve the quality of life for residents of downtown Lewiston, Maine, USA through community engagement in increasing the number of street amenities and improving the local housing stock. With their work, HN hopes to encourage more people to get outside, improving health outcomes, and to foster greater cross-cultural community building and an enhanced sense of place. These goals of HN create a value system of which they plan to create a model corridor (a street of one or two blocks in length that demonstrates the values of HN) as a stimulus for increased equitable revitalization of this area with street amenities, improvements to housing, and development of vacant lots. This project created an ArcGIS inventory of the street amenities that exist in a section of downtown Lewiston as outlined by HN. From this inventory, a scoring index was created as a tool to compare streets by their amenities and other factors. A brief assessment of vacant lots was conducted to evaluate the potential for future development by HN. A promotional brochure for HN that included maps was created. After creation of the ArcGIS maps, it was found that trees greatly outnumbered the other various amenities that were collected and assessed. A significant variation in sidewalk smoothness was discovered, with vacant lots more commonly found in places where the sidewalk was bumpy, unleveled, or had multiple flaws. Community gardens and open access green spaces have a non-uniform distribution within the neighborhood and the amount of each was limited. High amenity density by block was found where the sidewalk was smoother, although there were outliers. The highest model corridor block score was a 2.7 (with a maximum of 4 for a score) with the lowest score being a 0.4, which shows that no block is perfect in its current condition. The distribution of the composite scores was rather uniform, but it was noted that blocks with similar scores have different amenities and characteristics. The index demonstrates that there are multiple paths to obtaining a higher model corridor score. Vacant lots were assessed for potential development and the top three lots for future development, based on population density, distance from open access green space, and unit price per acre were: 111 Pine Street, 114 Bartlett Street, and 94 Howe Street. Lewiston has immense potential for development and community engagement in this field, and this study has outlined visually where resources can be most effectively used. For future projects, it is suggested that Healthy Neighborhoods continues with their plan of engaging the neighborhood in their work (possibly using the brochure created), complete a more thorough evaluation of vacant lots and analyze more innovative possibilities for redevelopment, and to consider the impact of adding small commercial space into the neighborhood

Combining crowdsourcing and google street view to identify street-level accessibility problems

ABSTRACT Poorly maintained sidewalks, missing curb ramps, and other obstacles pose considerable accessibility challenges; however, there are currently few, if any, mechanisms to determine accessible areas of a city a priori. In this paper, we investigate the feasibility of using untrained crowd workers from Amazon Mechanical Turk (turkers) to find, label, and assess sidewalk accessibility problems in Google Street View imagery. We report on two studies: Study 1 examines the feasibility of this labeling task with six dedicated labelers including three wheelchair users; Study 2 investigates the comparative performance of turkers. In all, we collected 13,379 labels and 19,189 verification labels from a total of 402 turkers. We show that turkers are capable of determining the presence of an accessibility problem with 81% accuracy. With simple quality control methods, this number increases to 93%. Our work demonstrates a promising new, highly scalable method for acquiring knowledge about sidewalk accessibility

Improving public transit accessibility for blind riders by crowdsourcing bus stop landmark locations with Google street view

Low-vision and blind bus riders often rely on known physical landmarks to help locate and verify bus stop locations (e.g., by searching for a shelter, bench, newspaper bin). However, there are currently few, if any, methods to determine this information a priori via computational tools or services. In this paper, we introduce and evaluate a new scalable method for collecting bus stop location and landmark descriptions by combining online crowdsourcing and Google Street View (GSV). We conduct and report on three studies in particular: (i) a formative interview study of 18 people with visual impairments to inform the design of our crowdsourcing tool; (ii) a comparative study examining differences between physical bus stop audit data and audits conducted virtually with GSV; and (iii) an online study of 153 crowd workers on Amazon Mechanical Turk to examine the feasibility of crowdsourcing bus stop audits using our custom tool with GSV. Our findings reemphasize the importance of landmarks in non-visual navigation, demonstrate that GSV is a viable bus stop audit dataset, and show that minimally trained crowd workers can find and identify bus stop landmarks with 82.5 % accuracy across 150 bus stop locations (87.3 % with simple quality control)

Combining crowdsourcing and Google street view to identify street-level accessibility problems

ABSTRACT Poorly maintained sidewalks, missing curb ramps, and other obstacles pose considerable accessibility challenges; however, there are currently few, if any, mechanisms to determine accessible areas of a city a priori. In this paper, we investigate the feasibility of using untrained crowd workers from Amazon Mechanical Turk (turkers) to find, label, and assess sidewalk accessibility problems in Google Street View imagery. We report on two studies: Study 1 examines the feasibility of this labeling task with six dedicated labelers including three wheelchair users; Study 2 investigates the comparative performance of turkers. In all, we collected 13,379 labels and 19,189 verification labels from a total of 402 turkers. We show that turkers are capable of determining the presence of an accessibility problem with 81% accuracy. With simple quality control methods, this number increases to 93%. Our work demonstrates a promising new, highly scalable method for acquiring knowledge about sidewalk accessibility

Scalable Methods to Collect and Visualize Sidewalk Accessibility Data for People with Mobility Impairments

Poorly maintained sidewalks pose considerable accessibility challenges for people with mobility impairments. Despite comprehensive civil rights legislation of Americans with Disabilities Act, many city streets and sidewalks in the U.S. remain inaccessible. The problem is not just that sidewalk accessibility fundamentally affects where and how people travel in cities, but also that there are few, if any, mechanisms to determine accessible areas of a city a priori. To address this problem, my Ph.D. dissertation introduces and evaluates new scalable methods for collecting data about street-level accessibility using a combination of crowdsourcing, automated methods, and Google Street View (GSV). My dissertation has four research threads. First, we conduct a formative interview study to establish a better understanding of how people with mobility impairments currently assess accessibility in the built environment and the role of emerging location-based technologies therein. The study uncovers the existing methods for assessing accessibility of physical environment and identify useful features of future assistive technologies. Second, we develop and evaluate scalable crowdsourced accessibility data collection methods. We show that paid crowd workers recruited from an online labor marketplace can find and label accessibility attributes in GSV with accuracy of 81%. This accuracy improves to 93% with quality control mechanisms such as majority vote. Third, we design a system that combines crowdsourcing and automated methods to increase data collection efficiency. Our work shows that by combining crowdsourcing and automated methods, we can increase data collection efficiency by 13% without sacrificing accuracy. Fourth, we develop and deploy a web tool that lets volunteers to help us collect the street-level accessibility data from Washington, D.C. As of writing this dissertation, we have collected the accessibility data from 20% of the streets in D.C. We conduct a preliminary evaluation on how the said web tool is used. Finally, we implement proof-of-concept accessibility-aware applications with accessibility data collected with the help of volunteers. My dissertation contributes to the accessibility, computer science, and HCI communities by: (i) extending the knowledge of how people with mobility impairments interact with technology to navigate in cities; (ii) introducing the first work that demonstrates that GSV is a viable source for learning about the accessibility of the physical world; (iii) introducing the first method that combines crowdsourcing and automated methods to remotely collect accessibility information; (iv) deploying interactive web tools that allow volunteers to help populate the largest dataset about street-level accessibility of the world; and (v) demonstrating accessibility-aware applications that empower people with mobility impairments

It’s Not What You Say, It’s What You Do: The Motivation of The Crowd to Participate in a Crowdsourcing Project to Support Blind and Partially Sighted Students

There is a growing interest in crowdsourcing projects for socially responsible issues. One area of socially responsible crowdsourcing is to support people with disabilities. However, there is little exploration of what motivates people to participate in such projects. This programme of research investigated the motivators for students to participate in a socially responsible crowdsourcing project to support blind and partially sighted students by describing images found in digital learning resources. For this purpose a crowdsourcing project, DescribeIT, was developed. The first study explored what students thought would motivate them to participate in the project to compare with students’ actual behaviour in the following studies. Altruism and monetary rewards were the leading self-reported motivational factors, other factors such as being interested in accessibility were reported. Studies 2 to 6 investigated the effects of different intrinsic and extrinsic motivational factors on students’ participation in the DescribeIT project with students from the UK and Arab countries. Despite the promising results of the self-reports of motivations, UK students’ participation rates in Studies 2 to 4 was extremely low. However, paying UK students small amounts of money (Study 6) did motivate them to participate. Arab students (Study 5) were intrinsically motivated to participate in the DescribeIT project and showed a higher participation rate than UK students. Studies 7 and 9 investigated the quality of the image descriptions produced by crowd members of established crowdsourcing platforms in comparison to those produced by students. The results showed a comparable quality across descriptions produced by students and crowd members. Studies 8 and 9 investigated the effect of simplifying the image description task by changing it to an image tagging task and showed that making the task easier increased participation rate. Lastly, Study 10 investigated the effect of a face-to-face training session on image description quality. It also investigated the effect of quality control instructions on quality. The face-to-face training increased description quality, but different quality control instructions did not. The practical implications of this research for crowdsourcers in socially responsible crowdsourcing contexts, are that they need to consider the cultural backgrounds of their potential crowd, make the task easy to do, offer small payments if possible and train crowd members in order to produce good quality work. The theoretical implications are a greater understanding of the motivations of crowd members in socially responsible projects and the importance of measuring both self-reports of motivation and actual behaviour