影响中国学生数据素养的因素：基于TAM的分析

Data Literacy Influencing Factors in Chinese Students: A TAM Analysis

摘要: 数据素养已成为数字时代大学生适应智能社会和提升就业竞争力的核心能力。然而，现有研究多聚焦于数据素养的能力构成与教育成效，对其形成机制的解释仍较为不足，尤其缺乏从技术接受理论视角探讨教育情境中心理因素与环境因素协同作用的研究。基于技术接受模型(Technology Acceptance Model, TAM)，本研究构建融合数据素养教育、数据使用氛围和就业焦虑的扩展理论框架，以569名中国高校本科生和研究生为研究对象，采用偏最小二乘结构方程模型(PLS-SEM)实证检验数据素养的形成机制及作用路径。研究结果表明：(1) 数据素养教育和数据使用氛围均能显著提升学生的感知易用性与感知有用性，并进一步促进积极的数据使用态度，从而提升数据素养水平；(2) 感知易用性不仅直接影响数据使用态度，还通过提升感知有用性间接促进数据素养形成，验证了TAM在数据素养教育情境中的适用性；(3) 就业焦虑具有双重作用机制，一方面增强学生对数据技能职业价值的认知，提高感知有用性，另一方面削弱对数据工具易用性的判断，降低使用信心；(4) 本科生与研究生在感知有用性、数据使用氛围等变量上存在显著的均值差异，提示不同教育阶段学生对数据技术的认知可能存在系统性区别，为后续开展分组路径比较研究提供了初步证据。本文将就业焦虑和数据使用氛围纳入技术接受模型，拓展了TAM在数据素养教育领域的解释边界，揭示了教育情境中心理因素与环境因素协同影响大学生数据素养形成的作用机制，为高校数据素养课程建设、学习环境优化及就业导向教育提供了理论依据与实践启示。

Abstract: Data literacy has become an essential competency for university students to adapt to the digital society and enhance their employability. However, existing studies have primarily focused on the components and educational outcomes of data literacy, while providing limited explanations of its underlying formation mechanisms, particularly regarding the joint influence of psychological and environmental factors in educational settings. To address this gap, this study extends the Technology Acceptance Model (TAM) by incorporating data literacy education, data usage atmosphere, and employment anxiety into an integrated analytical framework. Based on survey data collected from 569 undergraduate and postgraduate students in Chinese universities, Partial Least Squares Structural Equation Modeling (PLS-SEM) was employed to examine the mechanisms underlying the development of data literacy. The findings indicate that: (1) data literacy education and a supportive data usage atmosphere significantly enhance perceived ease of use and perceived usefulness, which subsequently foster positive attitudes toward data use and improve students’ data literacy; (2) perceived ease of use not only directly influences attitudes toward data use but also indirectly promotes data literacy through perceived usefulness, further confirming the applicability of TAM in data literacy education; (3) employment anxiety exerts a dual effect by strengthening students’ perceptions of the career value of data skills while simultaneously weakening their perceptions of the ease of using data-related technologies; and (4) undergraduate and postgraduate students show significant mean-level differences in perceived usefulness and data usage atmosphere, suggesting that students at different educational stages may perceive data-related technologies in systematically different ways and providing preliminary evidence for future group-comparison analyses of the underlying paths. By integrating employment anxiety and data usage atmosphere into TAM, this study extends the explanatory power of the model in the context of data literacy education and demonstrates how psychological and environmental factors jointly shape the development of university students’ data literacy. The findings provide theoretical insights for technology acceptance research and practical implications for curriculum design, learning environment optimization, and employability-oriented data literacy education.