The structure of Chinese beginning online instructors’ competencies: evidence from Bayesian factor analysis

Abstract

With the popularity of online education, understanding and improving the beginning online instructors’ teaching competencies is crucial to improve online education. The structure of beginning online instructors’ perceived competencies was widely discussed, and it was also confirmed that the structure and level of online teaching competencies would be varied across countries and different cultural backgrounds. Followed U.S. theoretical framework, some studies discussed the differences between Chinese and U.S. online teaching and instructors. But how Chinese online instructors, especially beginning online instructors, perceiving the online teaching competencies, and how this framework would differ from the U.S. framework, was less discussed. To fill in this gap, this study explored the structure of Chinese beginning online instructors’ competencies using the Bayesian factor analysis method. With a limited sample size, the traditional factor analysis trail reported undetermined results with three options. The results of Bayesian factor analysis indicated the three-factor solution is the most appropriate solution with the collected data. The three factors are named “preparing and supporting online teaching,” “creating an appropriate environment for students’ learning,” and “conducting appraisals of student learning.” The contributions of this study are as follows: (1) discussing the structure of Chinese beginning online instructors’ perceived competencies, (2) discussing why and how the structure of online teaching competencies varied across countries, (3) providing practical suggestions for online instructors’ training programs, and (4) providing methodological guidelines in factor analysis with small sample sizes for applied researchers.