Difference between revisions of "WinterMilestone 4 Team-UXCrowd"
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By S.S.Niranga | Alka Mishra | By S.S.Niranga | Alka Mishra | ||
| + | Abstract | ||
| + | A global phenomenon with minimal barrier to entry, crowdsourcing has transformed human force from mere consumers of products to active participants in value co-creation. The crowdsourcing ecosystem is one in which work is being re- defined as an online meritocracy in which skilled work is rewarded in real time and job training is imparted immediately via feedback loops[1]. Under such working conditions, the diverse pool of untrained participants: workers and requester, often find themselves circling with mistrust and ambiguity with respect to result quality and task authorship. This indicates that there is a requirement for quality control mechanisms to account for a wide range behavior: bad task authorship, malicious workers, ethical workers, slow learners, etc.[2]. Although many crowdsourced platforms offer clear guidelines, discussion forums and tutorial sessions to overcome some of these issues but, there is still a large percentage of workers and requesters unaware with the use of platforms. In this paper, we assess how crowd workers can produce a quality output by introducing below three proposed methods. | ||
| + | • Platform ready certifications | ||
| + | • Sentimental analysis system | ||
| + | • Gold Test | ||
Revision as of 06:54, 7 February 2016
Ensuring quality in crowdsourced platform by introducing a Platform ready certification, Sentiment analysis and Standard gold test. By S.S.Niranga | Alka Mishra
Abstract A global phenomenon with minimal barrier to entry, crowdsourcing has transformed human force from mere consumers of products to active participants in value co-creation. The crowdsourcing ecosystem is one in which work is being re- defined as an online meritocracy in which skilled work is rewarded in real time and job training is imparted immediately via feedback loops[1]. Under such working conditions, the diverse pool of untrained participants: workers and requester, often find themselves circling with mistrust and ambiguity with respect to result quality and task authorship. This indicates that there is a requirement for quality control mechanisms to account for a wide range behavior: bad task authorship, malicious workers, ethical workers, slow learners, etc.[2]. Although many crowdsourced platforms offer clear guidelines, discussion forums and tutorial sessions to overcome some of these issues but, there is still a large percentage of workers and requesters unaware with the use of platforms. In this paper, we assess how crowd workers can produce a quality output by introducing below three proposed methods. • Platform ready certifications • Sentimental analysis system • Gold Test
Milestone Contributors
S.S.Niranga @niranga,
Alka Mishra @alkamishra
