An Empirical Examination of the Factors of Data Literacy

R. nath, J. Kirby
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Abstract

To fully leverage the abundance of data and how data enhances decision-making, people must be data literate. Data literacy (DL) encompasses a set of interrelated skills in data management, data analysis, and the ability to interpret and communicate the results. Measuring an individual's DL level is an important first step toward designing and developing educational programs to improve one's DL skills. This paper considers a DL measurement scale referred to as the Global Data Literacy Benchmark survey and then explores the underlying constructs of this instrument. Data gathered from 311 university students across five universities in the United States is analyzed to identify and interpret the underlying factors of this DL scale. Also, the differences in DL scores among various subgroups of the students are investigated. The results show the existence of three DL factors. Also, the DL scores vary considerably among students depending upon the study areas and the comfort levels with data and analytics.
数据素养影响因素的实证研究
为了充分利用丰富的数据以及数据如何促进决策,人们必须具有数据素养。数据素养(DL)包括数据管理、数据分析以及解释和交流结果的能力方面的一系列相互关联的技能。测量个人的深度学习水平是设计和发展教育计划以提高个人深度学习技能的重要的第一步。本文考虑了一种被称为全球数据素养基准调查的深度学习测量量表,然后探讨了该工具的底层结构。从美国五所大学的311名大学生中收集的数据进行了分析,以确定和解释这种DL量表的潜在因素。此外,研究了不同亚组学生的DL得分差异。结果表明存在三个DL因子。此外,根据学习领域和对数据和分析的熟悉程度,学生的DL分数差异很大。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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