目的 探究网络环境“硬件”因素和信息技术使用能力“软件”因素对不同居住地医学生在线学习参与水平的影响。方法 2020年3月初,面向全国医学院校临床医学专业学生,以线上问卷调查的方式调研学生在线学习情况,使用STATA 15软件对相关数据进行线性回归分析。差异显著性检验水准α=0.01。结果 共收到来自90所院校99 559份有效问卷。医学生学习行为参与维度评分[(3.88±0.94)分]高于学习情感参与[(3.57±0.95)分]和学习认知参与[(2.87±0.72)分],差异均具有统计学意义(均P<0.001)。控制所在院校、学生个体特征及以往学业成绩等因素后,城市居住地医学生在线学习参与水平优于农村(β=0.118,P<0.001);加入所在地网络环境“硬件”因素后,不同居住地医学生在线学习参与差异的回归系数下降至-0.038,差异无统计学意义(P=0.021)。加入信息技术使用能力“软件”因素后,城市居住地医学生的在线学习参与水平回归系数β值为0.124,差异具有统计学意义(P<0.001);学习行为参与层面,城市与学生信息技术使用水平交互项回归系数β值为-0.033,差异具有统计学意义(P<0.001)。结论 医学生所在地网络环境“硬件”因素是影响不同居住地医学生在线学习参与水平差异的重要因素,而提高医学生信息技术使用能力对缩小不同居住地医学生在线学习行为参与水平差异更为有效。
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.
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