影响医学生在线学习参与水平的“硬件”与“软件”因素研究

doi:10.3760/cma.j.cn115259-20230109-00029

中华医学教育杂志 ›› 2024, Vol. 44 ›› Issue (1): 33-38.DOI: 10.3760/cma.j.cn115259-20230109-00029

影响医学生在线学习参与水平的“硬件”与“软件”因素研究

刘珵, 吴红斌

北京大学医学教育研究所全国医学教育发展中心,北京 100191

收稿日期:2023-01-09 出版日期:2024-01-01 发布日期:2023-12-29
通讯作者: 吴红斌, Email: wuhongbin@pku.edu.cn
基金资助:
教育部和国家卫生健康委员会委托立项课题(MEDU2019R004);中央高校基本科研业务费专项资金资助项目(BMU2021YJ011)

Study of the hardware or software factors that affect the differences in online learning of medical students

Liu Cheng, Wu Hongbin

Institute of Medical Education & National Center for Health Professions Education Development, Peking University, Beijing 100191, China

Received:2023-01-09 Online:2024-01-01 Published:2023-12-29
Contact: Wu Hongbin, Email: wuhongbin@pku.edu.cn
Supported by:
Project Commissioned by the Ministry of Education and the National Health Commission (MEDU2019R004);Special Funding Project for Basic Scientific Research Business Expenses of Central Universities(BMU2021YJ011)

摘要/Abstract

摘要： 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。

关键词: 学生,医科, 在线学习, 影响因素, 数字鸿沟

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

中图分类号:

R-05

刘珵, 吴红斌. 影响医学生在线学习参与水平的“硬件”与“软件”因素研究[J]. 中华医学教育杂志, 2024, 44(1): 33-38.

Liu Cheng, Wu Hongbin. Study of the hardware or software factors that affect the differences in online learning of medical students[J]. Chinese Journal of Medical Education, 2024, 44(1): 33-38.

参考文献

[1] 杨银付. “互联网+教育”带来的教育变迁与政策响应[J].教育研究,2016,37(6):4-8.
[2] Triola MM, Friedman E, Cimino C, et al. Health information technology and the medical school curriculum[J]. Am J Manag Care, 2010,16(12 Suppl HIT):SP54-56.
[3] 吴红斌, 郭建如, 程化琴, 等. 什么样的学生选择医科——基于全国学生调查数据的分析[J].复旦教育论坛,2018,16(6):105-112. DOI: 10.3969/j.issn.1672-0059.2018.06.016.
[4] 侯建林, 蔡景一, 董哲, 等. 6129名医学本科生生源及择业意愿研究[J].中华医学教育杂志,2012,32(6):826-830. DOI: 10.3760/cma.j.issn.1673-677X.2012.06.009.
[5] Dimaggio P, Hargittai E. From the 'digitaldivide' to 'digital inequality': studying internet use as penetration increases[EB/OL]. [2023-01-02]. https://ideas.repec.org/p/pri/cpanda/15.html.
[6] International Bank forConstruction and Development.World development report 2016: digital dividends[EB/OL].[2023-01-02].https://www.worldbank.org/en/publication/wdr2016.
[7] Laurie ECD, Kim JHD, José PWJ, et al. Predicting and resolving non-completion in higher (online) education–a literature review[J]. Educational Research Review, 2020, 29:100313.
[8] 张济洲, 黄书光. 隐蔽的再生产:教育公平的影响机制——基于城乡不同阶层学生互联网使用偏好的实证研究[J].中国电化教育,2018(11):18-23,132. DOI: 10.3969/j.issn.1006-9860.2018.11.003.
[9] The World Bank. Guidance note on education systems' response to COVID19[EB/OL].(2020-03-25)[2023-01-02]. http://pubdocs.worldbank.org/en/450881585235950757/COVID19-Education-Sector-Guidance-Note-March26.pdf.
[10] 中国教育科学研究院课题组.大规模在线教育的六点启示[N].光明日报, 2020-04-21(14).
[11] Pascarella ET, Seifert TA. How effective are the NSSE benchmarks in predicting important educational outcomes[EB/OL].[2023-01-02].http://www2.education.uiowa.edu/html/iae/iae-z-op-pasc-1-10.pdf.
[12] Hrastinski S. A theory of online learningas online participation[J]. Computers & Education, 2009, 52(1):78-82.
[13] Fredricks J, McColskey W, Meli J, et al. Measuring student engagement in upper elementary through high school: a description of 21 instruments[EB/OL]. [2023-01-02]. https://files.eric.ed.gov/fulltext/ED514996.pdf.
[14] 吴红斌, 王维民, 于晨, 等. 我国临床医学专业本科生特征分析[J].中华医学教育杂志,2021,41(1):1-5. DOI: 10.3760/cma.j.cn115259-20200328-00446.
[15] Huang RH, Liu DJ, Tlili A, et al. Handbook on facilitating flexible learning during educational disruption: the Chinese experience in maintaining undisrupted learning in COVID-19 outbreak[EB/OL]. [2023-01-02]. https://www.researchgate.net/publication/339939064_Handbook_on_Facilitating_Flexible_Learning_During_Educational_Disruption_The_Chinese_Experience_in_Maintaining_Undisrupted_Learning_in_COVID-19_Outbreak.
[16] 邬大光, 李文. 我国高校大规模线上教学的阶段性特征——基于对学生、教师、教务人员问卷调查的实证研究[J].华东师范大学学报(教育科学版),2020,38(7):1-30. DOI: 10.16382/j.cnki.1000-5560.2020.07.001.
[17] Liu S, Gomez J, Khan B, et al. Toward a learner-oriented community college online course dropout framework[J]. International Journal on E-Learning, 2007(6): 519-542.
[18] Tpscott D. Grown up digital: how the net generation is changing your world[M]. New York: McGraw-Hill, 2008:6.
[19] 杨钋, 徐颖. 数字鸿沟与家庭教育投资不平等[J].北京大学教育评论,2017,15(4):126-154. DOI: 10.19355/j.cnki.1671-9468.201704007.
[20] 胡钦太, 刘丽清, 丁娜. 教育公平视域中在线教育的困境与出路[J].中国电化教育,2020(8):14-21. DOI: 10.3969/j.issn.1006-9860.2020.08.003.

影响医学生在线学习参与水平的“硬件”与“软件”因素研究

Study of the hardware or software factors that affect the differences in online learning of medical students

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