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Open Access Article

Advances in International Applied Mathematics. 2026; 8: (2) ; 21-30 ; DOI: 10.12208/j.aam.20260013.

Research on the connotation reconstruction, evaluation model construction and application of college students’ data analysis literacy in the era of generative AI
生成式AI时代大学生数据分析素养的内涵重构、测评模型构建与应用研究

作者: 熊海金, 刘阁森 *

广西民族大学相思湖学院 广西南宁

*通讯作者: 刘阁森,单位:广西民族大学相思湖学院 广西南宁 ;

发布时间: 2026-08-31 总浏览量: 16 下载量: 加载中...

摘要

构建科学、合理的测评模型是大学生数据分析素养水平测评的前提,而批判性思维、数据伦理风险防控与人机协同能力则是生成式AI环境下数据分析素养的核心要素。基于三轮问卷调查,采用项目分析、探索性和验证性因子分析,开发包含采集与整理、洞察与分析、规范与伦理、智能与应用4个维度,共计16个题项的数据分析素养测评量表与模型。量表在内部一致性、折半信度、组合信度以及聚合和区分效度方面表现良好,模型能显著区分不同年级学生的数据分析素养水平。

关键词: 大学生;数据分析素养;测评模型;生成式AI;人机协同

Abstract

The construction of a scientific and reasonable evaluation model is the premise of the evaluation of college students’ data analysis literacy level, and critical thinking, data ethical risk prevention and control, and human-machine collaboration ability are the core elements of data analysis literacy in the generative AI environment. Based on three rounds of questionnaire surveys, using project analysis, exploratory and confirmatory factor analysis, a data analysis literacy assessment scale and model including four dimensions of collection and collation, insight and analysis, norms and ethics, intelligence and application, and a total of 16 items were developed. The scale performs well in terms of internal consistency, split-half reliability, combined reliability, and aggregation and discriminant validity. The model can significantly distinguish the data analysis literacy level of students in different grades.

Key words: College students; Data analysis literacy; Evaluation model; Generative AI; Human-machine collaboration

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引用本文

熊海金, 刘阁森, 生成式AI时代大学生数据分析素养的内涵重构、测评模型构建与应用研究[J]. 国际应用数学进展, 2026; 8: (2) : 21-30.