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Research On The Continuous Participation Of Users In Social Learning And Function Optimization Strategies In Online Knowledge Communitie

Posted on:2024-05-03Degree:MasterType:Thesis
Country:ChinaCandidate:Q R ShiFull Text:PDF
GTID:2569307106979669Subject:Management Science and Engineering
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Social learning is an important way for network users to acquire and share knowledge.The continuous participation of social learning can promote the development of individual lifelong learning.How to further deepen the connection between network users,promote the development of social learning,and improve users’ willingness to participate in social learning is a problem worth thinking about.The social learning behavior of network users needs to be carried out with the support of information technology tools.Technical support plays an important role in promoting users’ social learning.Optimizing and improving the functional design of social learning tools can help improve users’ satisfaction in social learning and achieve sustainable social learning.This article takes the online knowledge community,which provides favorable technology and social environment support for social learning,as the technical background for social learning.Based on the CSDN community’s social learning function module,it explores the influencing factors of users’ social learning participation behavior in the online knowledge community.Using functional design features as a research perspective,we designed semi structured interviews to clarify the impact of functional design features of online knowledge communities on users’ willingness to participate in social learning based on grounded theory.Then,based on social learning affordances,combined with literature research and analysis and induction of similar product features,18 functional demands of social learning in online knowledge communities were determined.Using Kano model for reference,a questionnaire was designed to define the demand attributes and importance of functional demands of social learning.The research results show that:(1)The four factors represented by the 17 functional indicators of the CSDN community,namely,observational learning,group identity,social exposure,and external stimulation,have a positive impact on users’ social learning participation behavior,and also preliminarily proven that the relevant functions of the community can effectively promote the user’s social learning process.(2)The social learning affordances of online knowledge community can promote users’ perception of the value of social learning experience under the opportunity of situational factors,thus having a positive impact on users’ continuous participation willingness of social learning in the community.(3)Among the 18 functional design demands related to social learning,content publishing is the essential demand,content search,content acquisition,comments and reply comments are expected demands,social tagging or entry grouping,collaborative editing of documents,notes taking,and social tagging are attractive demands,and content dissemination,personalized recommendation,instant communication,question or topic discussion,and evaluation buttons are a mixture of indifferent and attractive demands,The remaining five demands are indifferent demands.Among them,content publishing,search,and acquisition decision-making weights rank in the top three,while question or topic discussions,comments,and replies to comments,and notes taking rank at the top of their respective dimensions.Based on the above conclusions,this article proposes a way to improve the continuous participation of users in social learning by optimizing the design of social learning functions,focusing on user needs and satisfaction,and proposing optimization suggestions for functional elements under various design dimensions based on the characteristics and importance of different demand types,providing reference for the operation optimization of social learning tools.
Keywords/Search Tags:Social Learning, Continuous Participation, Online Knowledge Communities, Functional Requirement, Optimization Strategy
PDF Full Text Request
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