Evaluating digital competency among statistical educators: a comparative analysis of input-oriented DEA models

Abstract

As the educational landscape shifts towards online learning, assessing educators' digital competencies has become crucial. This study aims to evaluate the digital competencies of university educators using data envelopment analysis (DEA), specifically comparing the banker, charnes, and cooper (BCC) input-oriented models (super efficiency and Bi-O multi-criteria data envelopment analysis (MCDEA) super efficiency BCC models). The research was conducted in three phases. Initially, the BCC model assessed educators' digital competencies. Subsequently, the Bi-O MCDEA model evaluated these competencies within an online learning context. Finally, the effectiveness of the two models was compared. Data was collected through a survey administered to 30 educators from Universiti Teknologi MARA, with a response rate of 75%. Results showed that while the BCC model identified 23 out of 30 educators as efficient, the Bi-O MCDEA model recognized only two as efficient. This discrepancy highlights the different stringencies of the models and their impact on assessing digital competencies. The super efficiency (SE) model was then used to rank the efficient educators to determine the most proficient. The study underscores the need for precise assessment tools in online education to enhance digital competencies effectively. It suggests that integrating advanced DEA models can significantly improve the identification and training of educators, thereby enriching the educational outcomes in digital environments.