学术社交网站的建模搜索:关注学习成果

Dan Wu, Liuxing Lu, Lei Cheng
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引用次数: 1

摘要

目的建立学术社交网站的理论搜索模型。设计/方法/方法:本文首先基于自动神经网络的特点和已有的扩展意义构建模型,提出了一个基于自动神经网络的初始搜索模型。接下来,在ResearchGate上进行了一项在线调查,通过调查问卷了解搜索过程和结果。共有来自70个国家的359名参与者参与了这项在线调查。调查结果为修正初始模型提供了依据。研究结果表明:社会科学知识网络搜索的理论模型包括社会科学知识网络搜索动机、需求识别、信息需求触发搜索、社会需求触发搜索和结果。信息需求引发的搜索与学习成果显著正相关。除了学习成果外,在asns上搜索还可以帮助用户扩大社交网络,促进研究传播。实践意义了解用户的搜索习惯和知识获取可以为网络服务提供商设计支持搜索和增强学习的界面提供见解。此外,该模型可以帮助用户识别自己的知识状态和学习效果,提高学习效率。原创性/价值本文建立了一个理论模型来理解用户在asns上的搜索过程和结果。
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Modelling searching on academic social networking sites: a focus on learning outcomes
Purpose This paper aims to establish a theoretical search model on academic social networking sites (ASNSs). Design/methodology/approach Based on the characteristics of ASNSs and a previous extended sense-making model, this paper first presented an initial model of searching on ASNSs. Next, an online survey was conducted on ResearchGate to understand the search processes and outcomes with the help of a survey questionnaire. In total, 359 participants from 70 countries participated in this online survey. The survey results provided a basis for modifying the initial model. Findings Results showed that the theoretical model of searching on ASNSs included motives for searching on ASNSs, identification of needs, search triggered by information needs, search triggered by social needs and outcomes. The search triggered by information needs was significantly positively correlated with learning outcomes. Besides learning outcomes, searching on ASNSs could help user amplify their social networks and promote research dissemination. Practical implications Understanding users’ search habits and knowledge acquisition can provide insights for ASNSs to design an interface to support searching and enhance learning. Moreover, the proposed model can help users recognize their knowledge status and learning effects and improve their learning efficiency. Originality/value This paper contributes to establishing a theoretical model to understand users’ search process and outcomes on ASNSs.
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