The Influence of Social Capital and Intergenerational Mobility on University Students’ Sustainable Development in China

Abstract

A person’s family of origin has a profound impact on his or her life; a student’s performance at university and their interpersonal skills are also influenced by the characteristics of their family. In order to explore how social capital and intergenerational mobility impact university students’ quality of interpersonal communication and experiences, whether there is a “Matthew effect” at the higher education level, how the external social capital and the parenting style within a family affect the comprehensive quality of university students separately, and how university students’ family characteristics impact their quality of interpersonal communication, which will in turn affect their performance at university, this paper starts from the perspective of social capital and intergenerational mobility, and uses the cross-sectional data of the China Family Tracking Survey (CFPS) from 2018 to conduct Kaiser-Meyer-Olkin analysis and logistic binary regression for 1037 university students nationwide, to check whether the selected variables can be subjected to principal component analysis. The results show that, on one hand, university students’ family characteristics have a significant positive impact on their quality of interpersonal communication; on the other hand, their family backgrounds also have significant impact on their academic performance and enthusiasm for participating in student unions, as well as part-time employment and internships. In addition, according to the results of the mediation test, interpersonal communication skills play a mediating role in the way that family characteristics impact students’ performance at university, including their participation in student organizations, as well as internships and part-time jobs. Finally, this paper, by referring to both theoretical and empirical analysis, presents relevant suggestions from the perspectives of the individual, family, and government, aiming to facilitate the sustainable development of university students.

Quantitative Analysis and Prediction of Academic Performance of Students Using Machine Learning

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