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Supporting digitally enhanced learning through measurement in higher education: Development and validation of a university students' digital competence scale
Journal of Computer Assisted Learning ( IF 5.1 ) Pub Date : 2021-04-21 , DOI: 10.1111/jcal.12546
Xinghua Wang 1 , Zhuo Wang 1 , Qiyun Wang 2 , Wenli Chen 2 , Zhongling Pi 3
Affiliation  

Digital competence is critical for university students to adapt to and benefit from digitally enhanced learning. Prior studies on its measurement mostly focus on educators and relied on factor analyses. However, there is a lack of valid and convenient tools to measure university students' digital competence. This study aimed to develop a digital competence scale for university students (DC-US) in digitally enhanced learning with robust psychometric properties. An initial DC-US with 23 items was proposed to measure the single latent trait of digital competence. It was validated and refined continuously through a pilot study, a main study and a predictive validity study in three datasets involving 825 participants altogether, using factor analyses, Rasch analyses and the partial least squares modelling. The final DC-US turned out to comprise two subscales: technical literacy and digital skills, with 10 items retained, and manifested high internal consistency, unidimensionality and measurement invariance. The scale also demonstrated strong predictive validity, with technical literacy greatly predicting digital skills, which negatively predicted technostress. The DC-US enables instructors and school administrators to conveniently obtain preliminary information of university students' digital competence, informing their digital class preparation and development of timely interventions for addressing digital deficiencies.

中文翻译:

通过高等教育测量支持数字化学习:大学生数字化能力量表的开发和验证

数字能力对于大学生适应数字增强学习并从中受益至关重要。先前对其测量的研究主要集中在教育者身上,并依赖于因素分析。然而,缺乏有效和方便的工具来衡量大学生的数字能力。本研究旨在为大学生 (DC-US) 开发具有强大心理测量特性的数字增强学习的数字能力量表。提出了一个包含 23 个项目的初始 DC-US 来衡量数字能力的单一潜在特征。使用因子分析、Rasch 分析和偏最小二乘模型,通过试点研究、主要研究和预测有效性研究,在总共涉及 825 名参与者的三个数据集中不断验证和完善。最终的 DC-US 结果包括两个子量表:技术素养和数字技能,保留了 10 个项目,并表现出高度的内部一致性、单维性和测量不变性。该量表还表现出很强的预测效度,技术素养极大地预测了数字技能,这对技术压力产生了负面影响。DC-US 使教师和学校管理人员能够方便地获取大学生数字能力的初步信息,告知他们的数字课程准备和及时干预措施的发展,以解决数字缺陷。技术素养极大地预测了数字技能,这对技术压力产生了负面影响。DC-US 使教师和学校管理人员能够方便地获得大学生数字能力的初步信息,告知他们的数字课程准备和及时干预措施的发展,以解决数字缺陷。技术素养极大地预测了数字技能,这对技术压力产生了负面影响。DC-US 使教师和学校管理人员能够方便地获得大学生数字能力的初步信息,告知他们的数字课程准备和及时干预措施的发展,以解决数字缺陷。
更新日期:2021-04-21
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