In SN social sciences
The explosion of COVID-19 has brought new challenges to the education industry, especially higher education. Digital competency is becoming an essential competency for higher education instructors, and how to assess instructors' digital competency is attracting increasing attention in higher education. However, most studies have used self-report questionnaires or manual reviews to assess digital competencies, which are time-consuming and potentially biased, and there is a current need for valid and effective assessment methods. To address this issue, this study uses machine learning to analyze syllabi to assess the extent to which university instructors have incorporated digital competency into their courses. The results show that not only is the proposed method feasible, but the results of the assessment using machine learning are highly consistent with those of the human assessment. This approach contributes to the assessment of digital competency in higher education institutions and provides evidence that can be used as a reference for future research on the development of digital competency in higher education institutions.
Yang Tzu-Chi
2023
Digital competence, Higher education, Machine learning, Syllabus analysis
