Teachers' Familiarity with Machine Learning Concepts and Their Acceptance of Predictive Learning Technologies in a Technical College in Sichuan Province, China

Abstract

This study investigates the relationship between teachers' familiarity with machine learning (ML) concepts and their acceptance of predictive learning technologies (PLTs) at Sichuan Automotive Vocational and Technical College in China. A descriptive-comparative-correlational research design was employed, collecting data from 350 teachers via a structured questionnaire. Results indicated that teachers possessed only a slight familiarity with core ML concepts and a slightly accepting attitude towards PLTs. A very strong, statistically significant positive correlation was found between ML familiarity and technological acceptance. Furthermore, demographic factors such as education level and years of service significantly influenced both familiarity and acceptance. The findings highlight a critical need for targeted professional development programs that not only build foundational ML literacy but also address practical application and ethical concerns to foster greater adoption and effective use of predictive technologies in the classroom.