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论文编号：	16172
作者编号：	2120243872
上传时间：	2026/6/16 17:00:45
中文题目：	大学生需求驱动的大学图书馆数据服务体系构建研究
英文题目：	Construction of Data Service Systems in University Libraries Driven by Student Needs
指导老师：	李樵
中文关键字：	大学图书馆；数据服务；数据素养；Kano模型
英文关键字：	University library; Data service; Data literacy; Kano model
中文摘要：	随着大数据与人工智能等数智技术的快速发展，数据已成为驱动科研创新的基础要素。大学图书馆作为高校学术支持体系的重要组成部分，承担着数据资源支持和数据能力培养的双重职能。然而，当前我国大学图书馆数据服务普遍呈现供给主导与结构分散的特征，对大学生实际需求及其数据素养差异关注不足。在此背景下，基于用户需求构建覆盖数据全生命周期的差异化数据服务体系，成为亟待解决的问题。 本文综合采用系统性综述与问卷调查方法，探索大学生需求驱动下的大学图书馆数据服务体系构建路径。首先，基于PRISMA规范对国内外相关文献进行系统梳理，构建由数据全生命周期管理、数据管理平台建设、数据素养教育、数据知识服务构成的理论框架，并形成多层级的数据服务分类体系；其次，在该框架基础上设计Kano模型问卷，嵌入数据素养自报告量表，共回收有效问卷203份，并根据数据素养量表得分将受访者划为高、低数据素养两组；进一步运用Kano模型及Better-Worse系数，对67项数据服务需求的属性类型与优先级进行分类判定，并计算其优先级指标。 研究发现，大学生数据服务需求整体呈现出基础保障、效率提升、体验改善的三层递进结构，且数据素养水平对需求结构具有调节作用。高数据素养群体的需求以提升数据处理效率与扩展应用功能为主，其期望型需求体现在数据利用的各个环节，魅力型需求包括支持复杂数据处理和科研协作的服务；低数据素养群体的需求以保障数据安全和降低使用门槛为主，其对隐私保护、数据销毁等风险防范服务有较高需求，同时自动数据采集等服务通过减少操作步骤对低数据素养群体的满意度产生积极影响。 本研究在理论与实践层面对大学图书馆数据服务体系进行了拓展。在理论层面，通过界定大学图书馆数据服务及相关核心概念，明确了其内涵与边界；在方法层面，基于系统性综述构建数据服务体系框架，并引入Kano模型开展需求量化分析，揭示了不同数据素养群体的需求差异及其优先级结构；在实践层面，提出了基于用户数据素养差异的分层数据服务体系构建路径，为我国大学图书馆推进需求导向的数据服务转型与资源优化配置提供了参考依据。图8幅，表7个，参考文献103篇。
英文摘要：	With the rapid development of digital and intelligent technologies such as big data and artificial intelligence, data has become a fundamental element driving scientific research innovation. As an important part of the university academic support system, university libraries bear the dual functions of providing data resource support and data literacy education. However, the data services of university libraries in China currently exhibit characteristics of being supply-driven and structurally fragmented, with insufficient attention paid to the actual needs of college students and their differences in data literacy. In this context, building a differentiated data service system covering the entire data lifecycle based on user needs is an urgent problem to address. This study employs a combined method of systematic review and questionnaire survey to explore the construction path of a university library data service system driven by college students’ needs. First, following the PRISMA guidelines, relevant domestic and international literature was systematically reviewed to construct a theoretical framework encompassing data lifecycle management, data management platform construction, data literacy education, and data knowledge services, thereby forming a multi-level data service classification system. Second, based on this framework, a Kano model questionnaire with an embedded data literacy self-reporting scale was designed. A total of 203 valid questionnaires were collected. Respondents were divided into high and low data literacy groups based on their scale scores. Subsequently, the Kano model and the Better-Worse coefficient were used to classify and determine the attribute types and priorities of 67 data service requirements, and their priority indices were calculated. The study reveals that university students’ demands for data services collectively exhibit a three-tiered, progressive structure comprising basic assurance, efficiency enhancement, and experience improvement. Data literacy levels serve as a moderating factor in this demand structure. For the high data literacy group, demands are primarily oriented towards improving data processing efficiency and expanding application functionalities. Their One-dimensional (O) requirements manifest throughout the data utilization process, while Attractive (A) requirements involve services supporting complex data processing and research collaboration. In contrast, the demands of the low data literacy group focus on ensuring data security and lowering the barrier to use. They show higher concern for risk prevention services such as privacy protection and data destruction. Meanwhile, services like automated data collection, by reducing operational complexity, contribute positively to their satisfaction. This study expands the university library data service system at theoretical and practical levels. Theoretically, it clarifies the connotation and boundaries of university library data services by defining its core concepts. Methodologically, it constructs a data service system framework based on a systematic review and introduces the Kano model to conduct a demand classification and prioritization analysis, revealing demand differences and priority structures among different data literacy groups. Practically, it proposes a construction path for a data-literacy-based hierarchical data service system, providing a reference for university libraries in China to promote demand-oriented data service transformation and optimize resource allocation. This study includes 8 figures and 7 tables, and draws upon 103 references.
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