Abstract: Objective To explore the effects of network ″hardware″ factors and the ″software″ ability of using information technology factors that might affect the differences in online learning among medical students from different living areas. Methods In early March 2020, an online questionnaire survey was conducted to investigate the online learning situation of clinical medicine students in medical colleges across the country, and the relevant data was analyzed using STATA 15 software for linear regression.The significance test level for differences was 0.01. Results A total of 99 559 valid questionnaires were received from 90 universities. The score of the dimension of learning behavior participation among medical students [(3.88±0.94) points] was higher than that of emotional participation in learning [(3.57±0.95) points] and cognitive participation in learning [(2.87±0.72) points], all P<0.001. After controlling for factors such as the institution, individual characteristics of students, and past academic performance, medical students residing in urban areas have a better level of online learning participation than those in rural areas (β=0.118, P<0.001); Regression coefficients for differences in online learning participation among medical students from different residential areas after incorporating “hardware” factors into the network environment of their location decreased to -0.038 (P=0.021). Regression coefficient of online learning participation level of medical students residing in urban areas after incorporating the factor of ability of using information technology ″software″ was 0.124 (P<0.001). Regression coefficient for the interaction term between city and student information technology usage level at the level of learning behavior participation was -0.033 (P<0.001). Conclusions The “hardware” factor in the network environment where medical students reside is an important factor affecting the differences in online learning participation levels among medical students from different places of residence, and improving their information technology usage ability is more effective in narrowing the differences of online learning behavior participation levels among medical students from different places of residence.

Key words: Students, medical, Online learning, Influence factor, Digital divide