The relation between in-service teachers' digital competence and personal and contextual factors: What matters most?

• Published in: Computers and education 2021-01, Vol.160, p.104052, Article 104052
• Main Authors: Lucas, Margarida, Bem-Haja, Pedro, Siddiq, Fazilat, Moreira, António, Redecker, Christine
• Format: Article
• Language: English
• Subjects: DigCompEdu; Machine learning; Regression models; Self-assessment; Teachers' professional development
• ISSN: 0360-1315; 1873-782X
• DOI: 10.1016/j.compedu.2020.104052

The objective of this article is twofold: (i) to provide a valid and reliable instrument to measure teachers' digital competence on the basis of the European Framework for the Digital Competence of Educators (also referred to as DigCompEdu) and (ii) to examine the relation between in-service teachers' digital competence and personal and contextual factors. For this purpose, a study was conducted with 1071 in-service teachers. The instrument was validated with respect to its factorial structure, and the relation between teachers' digital competence and personal and contextual factors was analysed using three different analyses: (i) simple linear regression, (ii) multiple linear regression, and (iii) machine learning. The results show that all the analyses conducted confirm the prevalence of personal factors over contextual ones, as well as their stronger predictive capacity. Gender and age differences were found, but the number of tools used for teaching and learning was the strongest predictor of teachers' digital competence, followed by ease of use, confidence in using digital technology, and openness to new technology. The article shows the soundness of the DigCompEdu framework and provides knowledge that could benefit teacher training programmes and inform policy and practice. •A self-assessment instrument based on DigCompEdu was developed.•The relation between personal and contextual factors was analysed.•The analysis of relations used a novel approach: machine learning.•Personal factors are the strongest predictors of teachers' digital competence.