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Reply & Supply: Efficient crowdsourcing when workers do more than answer questions.

ABSTRACT: Crowdsourcing works by distributing many small tasks to large numbers of workers, yet the true potential of crowdsourcing lies in workers doing more than performing simple tasks-they can apply their experience and creativity to provide new and unexpected information to the crowdsourcer. One such case is when workers not only answer a crowdsourcer's questions but also contribute new questions for subsequent crowd analysis, leading to a growing set of questions. This growth creates an inherent bias for early questions since a question introduced earlier by a worker can be answered by more subsequent workers than a question introduced later. Here we study how to perform efficient crowdsourcing with such growing question sets. By modeling question sets as networks of interrelated questions, we introduce algorithms to help curtail the growth bias by efficiently distributing workers between exploring new questions and addressing current questions. Experiments and simulations demonstrate that these algorithms can efficiently explore an unbounded set of questions without losing confidence in crowd answers.

SUBMITTER: McAndrew TC

PROVIDER: S-EPMC5555646 | biostudies-other | 2017

REPOSITORIES: biostudies-other

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Project description:BACKGROUND: The huge amount of biomedical-molecular data increasingly produced is providing scientists with potentially valuable information. Yet, such data quantity makes difficult to find and extract those data that are most reliable and most related to the biomedical questions to be answered, which are increasingly complex and often involve many different biomedical-molecular aspects. Such questions can be addressed only by comprehensively searching and exploring different types of data, which frequently are ordered and provided by different data sources. Search Computing has been proposed for the management and integration of ranked results from heterogeneous search services. Here, we present its novel application to the explorative search of distributed biomedical-molecular data and the integration of the search results to answer complex biomedical questions. RESULTS: A set of available bioinformatics search services has been modelled and registered in the Search Computing framework, and a Bioinformatics Search Computing application (Bio-SeCo) using such services has been created and made publicly available at http://www.bioinformatics.deib.polimi.it/bio-seco/seco/. It offers an integrated environment which eases search, exploration and ranking-aware combination of heterogeneous data provided by the available registered services, and supplies global results that can support answering complex multi-topic biomedical questions. CONCLUSIONS: By using Bio-SeCo, scientists can explore the very large and very heterogeneous biomedical-molecular data available. They can easily make different explorative search attempts, inspect obtained results, select the most appropriate, expand or refine them and move forward and backward in the construction of a global complex biomedical query on multiple distributed sources that could eventually find the most relevant results. Thus, it provides an extremely useful automated support for exploratory integrated bio search, which is fundamental for Life Science data driven knowledge discovery.

Project description:OBJECTIVES:The study aimed to compare candidate performance between traditional best-of-five single-best-answer (SBA) questions and very-short-answer (VSA) questions, in which candidates must generate their own answers of between one and five words. The primary objective was to determine if the mean positive cue rate for SBAs exceeded the null hypothesis guessing rate of 20%. DESIGN:This was a cross-sectional study undertaken in 2018. SETTING:20 medical schools in the UK. PARTICIPANTS:1417 volunteer medical students preparing for their final undergraduate medicine examinations (total eligible population across all UK medical schools approximately 7500). INTERVENTIONS:Students completed a 50-question VSA test, followed immediately by the same test in SBA format, using a novel digital exam delivery platform which also facilitated rapid marking of VSAs. MAIN OUTCOME MEASURES:The main outcome measure was the mean positive cue rate across SBAs: the percentage of students getting the SBA format of the question correct after getting the VSA format incorrect. Internal consistency, item discrimination and the pass rate using Cohen standard setting for VSAs and SBAs were also evaluated, and a cost analysis in terms of marking the VSA was performed. RESULTS:The study was completed by 1417 students. Mean student scores were 21 percentage points higher for SBAs. The mean positive cue rate was 42.7% (95% CI 36.8% to 48.6%), one-sample t-test against ≤20%: t=7.53, p<0.001. Internal consistency was higher for VSAs than SBAs and the median item discrimination equivalent. The estimated marking cost was £2655 ($3500), with 24.5 hours of clinician time required (1.25 s per student per question). CONCLUSIONS:SBA questions can give a false impression of students' competence. VSAs appear to have greater authenticity and can provide useful information regarding students' cognitive errors, helping to improve learning as well as assessment. Electronic delivery and marking of VSAs is feasible and cost-effective.

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Reply & Supply: Efficient crowdsourcing when workers do more than answer questions.

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