Collaboration, collation, and competition: Crowdsourcing a directory of educational technology tools for teaching and learning

Abstract

This paper reports on a distinctive one-year online open crowdsourcing initiative which originated in the Republic of Ireland with a view to compiling an A–Z directory of educational technology tools for teaching and learning. Through analysis of multiple sources of data, the paper presents an intrinsic case study which outlines the design and implementation of the initiative and offers critical insights into engagement and participation in the project. The study found that participants from across the spectrum of educational sectors contributed to this project from a range of geographical locations, with significantly greater numbers engaging with the directory of educational technology tools that resulted from the crowdsourcing activity. It concludes that the creative project design, combined with a novel crowdsourcing methodology encompassing elements of collaboration, competition, and gamification, were strong motivational factors for participation. The case study provides a valuable context for considering the wider potential of this particular crowdsourcing format (and crowdsourcing applications in general) for teaching and learning purposes.

Contribution to team and community in crowdsourcing contests: a qualitative investigation

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A Data Envelopment Analysis Evaluation Study of Urban Crowd Sourcing Competitiveness Based on Evidence From 21 Chinese Cities

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“…Crowd sourcing intermediaries can assist the government in researching and formulating crowd sourcing industry policies and regulations, and can also encourage users to maintain the quality of service delivery to meet the quality requirements of enterprises. In addition, crowd sourcing intermediaries are also committed to safeguarding the intellectual property rights and information security of enterprises, and cooperating with contracting parties to formulate crowd sourcing industry strategies (Donlon et al, 2020). Based on these considerations, this study takes the ratio of the number of intermediaries in base cities to the number of intermediaries in the country as an input variable (X 5 ) of the DEA model.…”

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