Can ECE Classes Sustainably Empower Preschool Children’s Development?-A Longitudinal Study of the Role of ECE Classes in Children’s Learning and Development

Does enrolling children aged 0-3 in early childhood education(ECE)classes continuously empower their learning and development after entering kindergarten?Based on the tracking survey data of 664 children aged 3-6 in 2017 and 367 children aged 3-6 in 2018,this study uses Propensity Score Matching to analyze this issue.The findings reveal that children’s family socioeconomic status is a key factor influencing their participation in ECE classes.The baseline data show that participation in ECE classes does not significantly improve children’s learning and development.However,the follow-up data show that participation in ECE classes plays a significant negative role in motor development for children moving to their second year of kindergarten,but subsequently plays a positive role in language development,health,and safety for those entering their third year of kindergarten.Overall,participation in ECE classes plays no continuous positive role in children’s learning and development after they enter kindergarten.Based on these findings,it is suggested that parents should respect the natural laws of child development instead of blindly following the trend of attending ECE classes,and high-quality parental companionship is crucial.The Chinese government is supposed to implement policies such as“double reduction”to regulate ECE institutions that fail to operate in good faith.

Which Type of Interpersonal Interaction Better Facilitates College Student Learning and Development in China:Face-to-Face or Online?

Purpose:This study attempts to explore how Chinese college students engage in face-to-facesynchronous and online asynchronous interactions and examine how the two different interacition types are associated with their academic learning(learning achievement and the developmentof research skills),satisfaction,and their perceptions of learning environments.DesignlApproach/Methods:A sample of 3,999 undergraduate students from a research university in Northern China participated in the survey.Aseries of cluster analysis,one-way analysisof variance,and hierarchical multiple regression were conducted.Findings:The cluster analysis results revealed that there were four types of learners amongthese students and thata large percentage of Chinese undergraduates were classified into eitherdigital communicators(36.16%)or passive interactors(32.71%).In general the face-to-facesynchronous interaction generated more desirable academic learning perceptions of the learn-ing environment,and higher satisfaction than the online asynchronous interaction in mostaspects.However,the asynchronous online interaction fostered student autonomy and contributed to students'completion of an in-depth thesis.OriginalitylValue:By distinguishing face-to-face synchronous versus online asynchronousinteractions,this study led toan enhanced knowledge of the interactive patterns of Chinesecollege students and uncovered the specific effects of the two types ofinterpersonal interactionsin Chinese research universities.

Women Entrepreneurship Index Prediction Model with Automated Statistical Analysis

Recently,gender equality and women’s entrepreneurship have gained considerable attention in global economic development.Prior to the design of any policy interventions to increase women’s entrepreneurship,it is significant to comprehend the factors motivating women to become entrepreneurs.The non-understanding of the factors can result in the endurance of low living stan-dards and the design of expensive and ineffectual policies.But female involve-ment in entrepreneurship becomes higher in developing economies compared to developed economies.Women Entrepreneurship Index(WEI)plays a vital role in determining the factors that enable theflourishment of high potential female entrepreneurs which enhances economic welfare and contributes to the economic and social fabric of society.Therefore,it is needed to design an automated and accurate WEI prediction model to improve women’s entrepreneurship.In this view,this article develops an automated statistical analysis enabled WEI predic-tive(ASA-WEIP)model.The proposed ASA-WEIP technique aims to effectually determine the WEI.The proposed ASA-WEIP technique encompasses a series of sub-processes such as pre-processing,WEI prediction,and parameter optimiza-tion.For the prediction of WEI,the ASA-WEIP technique makes use of the Deep Belief Network(DBN)model,and the parameter optimization process takes place using Squirrel Search Algorithm(SSA).The performance validation of the ASA-WEIP technique was executed using the benchmark dataset from the Kaggle repo-sitory.The experimental outcomes stated the better outcomes of the ASA-WEIP technique over the other existing techniques.