Jonas Oppenlaender
Kennedy Opoku Asare
ABSTRACT
In mobile crowdsourcing, labour can be opportunistically elicited by sending notifications to workers who complete tasks on-the-go. While much work has focused on optimizing the work quality and quantity in mobile crowdsourcing, surprisingly few studies have explored the type of tasks that might be suitable for different user contexts. This paper presents results from a proof-of-concept user study that aimed to uncover where, when and what type of tasks mobile workers are willing to complete. We find that different contexts do affect the type of work users are willing to complete. Finally, we lay out a complete design, key challenges and opportunities for a longer field trial that we hope to conduct in the near-future.
- Jeff Howe. 2006. The rise of crowdsourcing. Wired magazine 14, 6: 1--4.Google Scholar
- Florian Alt, Alireza Sahami Shirazi, Albrecht Schmidt, Urs Kramer, and Zahid Nawaz. 2010. Location-based Crowdsourcing: Extending Crowdsourcing to the Real World. In Proc. NordiCHI, 13--22. Google ScholarDigital Library
- Gregory D. Abowd, Anind K. Dey, Peter J. Brown, Nigel Davies, Mark Smith, and Pete Steggles. 1999. Towards a Better Understanding of Context and Context-Awareness. Handheld and Ubiquitous Computing (HUC '99), 304--307. Google ScholarDigital Library
- Niels Van Berkel, Jorge Goncalves, Simo Hosio, Vassilis Kostakos. 2017. Gamification of Mobile Experience Sampling Improves Data Quality and Quantity. 2017. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 1, 3, Article 107. Google ScholarDigital Library
- Reed Larson and Mihaly Csikszentmihalyi. 1983. The Experience Sampling Method. New Directions for Methodology of Social and Behavioral Sciences 15: 41--56.Google Scholar
- Niels Van Berkel, Denzil Ferreira, and Vassilis Kostakos. 2018. The Experience Sampling Method on Mobile Devices. ACM Comput. Surv. 50, 6, 93. Google ScholarDigital Library
- Stephen Intille, Caitlin Haynes, Dharam Maniar, Aditya Ponnada, and Justin Manjourides. 2016. μE-MA: Microinteraction-based Ecological Momentary Assessment (EMA) Using a Smartwatch. In Proc. Int. Joint Conf. Pervasive and Ubiquitous Computing (UbiComp '16), 1124--1128. Google ScholarDigital Library
- Andrei Tamilin, Iacopo Carreras, Emmanuel Ssebaggala, Alfonse Opira, and Nicola Conci. 2012. Context-aware Mobile Crowdsourcing. In Proc. Ubiquitous Computing (UbiComp '12), 717--720. Google ScholarDigital Library
- Leyla Kazemi, Cyrus Shahabi, and Lei Chen. 2013. GeoTruCrowd: trustworthy query answering with spatial crowdsourcing. In Proc. SIGSPATIAL Int. Conf. Advances in Geographic Information Systems (SIGSPATIAL '13), 314--323. Google ScholarDigital Library
- Suman Nath, Michel Goraczko, Jie Liu, and Azalia Mirhoseini. 2018. Optimizing Task Recommendations in Context-Aware Mobile Crowdsourcing. United States Patent, US 9,911,088,B2.Google Scholar
- John C. Flanagan. 1954. The critical incident technique. Psychological Bulletin 51, 4: 327--358.Google ScholarCross Ref
Index Terms
- CampusTracker: Assessing Mobile Workers' Momentary Willingness to Work on Paid Crowdsourcing Tasks
Recommendations
Students' Experiences with Ecological Momentary Assessment Tools to Report on Emotional Well-being
Ecological Momentary Assessment (EMA) methods have emerged as an approach that enhances the ecological validity of data collected for the study of human behavior and experience. In particular, EMA methods are used to capture individuals' experiences (...
Context-Informed Scheduling and Analysis: Improving Accuracy of Mobile Self-Reports
CHI '19: Proceedings of the 2019 CHI Conference on Human Factors in Computing SystemsMobile self-reports are a popular technique to collect participant labelled data in the wild. While literature has focused on increasing participant compliance to self-report questionnaires, relatively little work has assessed response accuracy. In this ...
Wearable ESM: differences in the experience sampling method across wearable devices
MobileHCI '16: Proceedings of the 18th International Conference on Human-Computer Interaction with Mobile Devices and ServicesThe Experience Sampling Method is widely used for collecting self-report responses from people in natural settings. While most traditional approaches rely on using a phone to trigger prompts and record information, wearable devices now offer new ...
Comments