Assessment of digital skills of university MOOC instructors: a systematic quantitative study of multidimensional indicators

This study presents a hierarchical framework for evaluating university MOOC (Massive Open Online Course) instructors' digital skills through systematically interconnected quantitative indicators. Our three-level assessment structure employs: (1) foundational diversity measures (Shannon and Simpson Indices) to evaluate skill distribution and balance; (2) intermediate indicators (Coverage Rate and Skills Richness) to assess skill prevalence and variety; and (3) advanced metrics (Skills Combination Index and Digital Literacy Maturity Model) to measure skill integration and development stages. By analyzing data over eight semesters, we demonstrate how these hierarchically related measures build upon each other – from basic skill distribution analysis to advanced integration assessment – to provide comprehensive insights. Our findings reveal significant variations across disciplines, with high-tech departments like Computer Information Engineering showing consistently higher scores across all assessment levels compared to humanities departments such as Fine Arts. We identify trends in key skill development and integration, proposing targeted training recommendations based on departmental variations and specific skill gaps. The innovation in this study lies in introducing a systematically structured evaluation framework where each measurement level informs and enhances the insights from others, providing comprehensive empirical evidence of digital skills distribution, development, and integration among instructors.