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For ranking aggregation in crowdsourcing task, the key issue is how to select the optimal working group with a given number of workers to optimize the performance of their aggregation. Performance prediction for ranking aggregation can solve this issue effectively. However, the performance prediction effect for ranking aggregation varies greatly due to the different influencing factors selected. Although questions on why and how data fusion methods perform well have been thoroughly discussed in the past, there is a lack of insight about how to select influencing factors to predict the performance and how much can be improved of.

In this paper, performance prediction of multivariable linear regression based on the optimal influencing factors for ranking aggregation in crowdsourcing task is studied. An influencing factor optimization selection method based on stepwise regression (IFOS-SR) is proposed to screen the optimal influencing factors. A working group selection model based on the optimal influencing factors is built to select the optimal working group with a given number of workers.

The proposed approach can identify the optimal influencing factors of ranking aggregation, predict the aggregation performance more accurately than the state-of-the-art methods and select the optimal working group with a given number of workers.

To find out under which condition data fusion method may lead to performance improvement for ranking aggregation in crowdsourcing task, the optimal influencing factors are identified by the IFOS-SR method. This paper presents an analysis of the behavior of the linear combination method and the CombSUM method based on the optimal influencing factors, and optimizes the task assignment with a given number of workers by the optimal working group selection method.

This study explores the impact of parents’ and children’s early expectations on children’s later school persistence and completion of compulsory and secondary education, paying special attention to the parent-child agreement in early educational expectations. Results from analyzing longitudinal data from the Gansu Survey of Children and Families (GSCF) show that children often carry educational expectations quite different from their parents’. Consistent with previous research, children’s and their parents’ early expectations are strong predictors of children’s later educational attainment. More importantly, the analysis reveals that children benefit greatly when they share with their parents’ high expectations. Those children whose high expectations aligned with their parents fair best in later educational outcomes: They are more likely to complete compulsory education and secondary education. The combined determination of parents and children can help moderate the negative impact of poverty and facilitate children’s continued efforts in fulfilling their expectations. This positive impact holds even for children from the most impoverished families. This study points to the importance to recognize that there are non-material resources that family could provide to advance children’s education.