Crowd-sourcing deals with solving problems by assigning them to a large number of non-experts called crowd using their spare time. In these systems, the final answer to the question is determined by summing up the votes obtained from the community. The popularity of using these systems has increased by facilitation of access to community members through mobile phones and the Internet. One of the issues raised in crowd-sourcing is how to choose people and how to collect answers. Usually, the separation of users is done based on their performance in a pre-test. Designing the pre-test for performance calculation is challenging; The pre-test questions should be chosen in a way that they test the characteristics in people related to the main questions. One of the ways to increase the accuracy of crowd-sourcing systems is to pay attention to people's cognitive characteristics and decision-making model to form a crowd and improve the estimation of the accuracy of their answers to questions. People can estimate the correctness of their responses while making a decision. The accuracy of this estimate is determined by a quantity called metacognition ability. Metacoginition is referred to the case where the confidence level is considered along with the answer to increase the accuracy of the solution. In this paper, by both mathematical and experimental analysis, we would answer the following question: Is it possible to improve the performance of the crowd-sourcing system by knowing the metacognition of individuals and recording and using the users' confidence in their answers?

翻译：众包通过将问题分配给大量称为“群众”的非专家人员，利用其空闲时间解决问题。在这类系统中，问题的最终答案由汇总社区获得的投票数决定。随着通过手机和互联网接触社区成员的便利性增加，这些系统的使用普及度也随之提高。众包中提出的问题之一是如何选择人员以及如何收集答案。通常，用户的分组是基于他们在预测试中的表现进行的。设计用于评估表现的预测试具有挑战性：需要选择能够评估与主问题相关的人员特征的预测试问题。提高众包系统准确性的方法之一是关注人员的认知特征和决策模型，以形成群众并改进对其答案准确性的估计。人员在做出决策时能够估计其回答的正确性。这种估计的准确性由称为元认知能力的量决定。元认知指的是在答案中考虑信心水平以提高解决方案准确性的情况。本文通过数学分析和实验分析，回答了以下问题：是否可以通过了解个体的元认知能力，记录并利用用户对其答案的信心，来提升众包系统的性能？