上海口腔医学

上海口腔医学 ›› 2021, Vol. 30 ›› Issue (5): 551-555.doi: 10.19439/j.sjos.2021.05.021

• 论著 • 上一篇    下一篇

COVID-19疫情期间口腔本科生线上学习决策TAN贝叶斯网络模型分析

王皓珺1,2, 查光玉1,*, 曹明国1,*, 刘荣场1, 左玲珑1, 刘凯1, 李达生1, 黄慧捷1, 吴松泉1, 江银华1,3   

  1. 1.丽水学院 医学院,浙江 丽水 323000;
    2.丽水市人民医院,浙江 丽水 323000;
    3.杭州师范大学 口腔医学院,浙江 杭州 311121
  • 收稿日期:2020-06-09 修回日期:2020-07-16 出版日期:2021-10-25 发布日期:2021-11-08
  • 通讯作者: 查光玉,E-mail:zhaguangyu@hotmail.com;曹明国,E-mail:cmg@lsu.edu.cn。*共同通信作者
  • 作者简介:王皓珺（1997-）,女,在读研究生,E-mail:1033785365@qq.com

TAN Bayesian network modeling of online learning decision making of dental undergraduates during the COVID-19 pandemic

WANG Hao-jun1,2, ZHA Guang-yu1, CAO Ming-guo1, LIU Rong-chang1, ZUO Ling-long1, LIU Kai1, LI Da-sheng1, HUANG Hui-jie1, WU Song-quan1, JIANG Yin-hua1,3   

  1. 1. College of Medicine, Lishui University. Lishui 323000;
    2. Lishui People's Hospital. Lishui 323000;
    3. School of Stomatology, Hangzhou Normal University. Hangzhou 311121, Zhejiang Province, China
  • Received:2020-06-09 Revised:2020-07-16 Online:2021-10-25 Published:2021-11-08

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摘要： 目的: 探讨地方高校口腔医学本科生在COVID-19疫情期间线上学习的应对与决策规律。方法: 针对丽水学院2016—2018级口腔本科生,分别在线上课程开课之前和开课4周后进行2次问卷调查。利用SPSS Modeler 18.0对资料进行整理、审核和内部一致性分析,构建TAN贝叶斯网络模型（tree augmented naive Bayesian network）,并使用总体正确率、受试者工作特征曲线(ROC曲线)和ROC曲线下面积（AUC值）等指标评价模型的优劣。结果: 2次调查收集个案数分别为422个和382个,Cronbach α系数分别为0.91和0.82。所构建的TAN贝叶斯网络模型分析显示,是否预习线上资料的决策中,最重要的变量是期待开学,其次是网络资料的有效性;在线上课程是否达到线下课程标准的评价变量中,讲课节奏（视频直播或录播中）、线上资料任务点多少、线上资料框架条理3个变量的重要性显著高于其他变量。2个模型的总体预测正确率分别为89.42%和87.82%,AUC值分别为0.75和0.93。结论: 疫情期间,教师应充分认识到地方高校口腔医学本科生的教育期待。教学中首先要精心组织内容全面、结构条理清晰、生动且难度适中的线上资料,辅以节奏适中、语音和画面质量均上乘的线上直播或视频录播,再结合多渠道师生交互,真正做到“停课不停学”。

关键词: 线上学习, TAN贝叶斯网络模型, COVID-19 疫情

Abstract: PURPOSE: To perceive the dental undergraduate's policy of coping with online learning and their decision-making laws during the COVID-19 pandemic. METHODS: For dental undergraduate students from the 2016 grade to 2018 grade of Lishui University, two prospective questionnaire surveys were conducted before the online course starting and four weeks later. SPSS Modeler18.0 software was used to screen, review, and analyze the data. TAN (tree augmented naive) Bayesian network models were utilized to analyze and predict variables. Indicators like the overall prediction accuracy, receiver operating characteristic curve (ROC curve), and area under the ROC curve(AUC value) were applied to evaluate the model's predicting performances. RESULTS: The case score of each survey was 422 and 382, and the Cronbach's α coefficients of internal consistency were 0.91 and 0.82. Among the decision-making variables in the aspect of "whether to preview online learning materials", the top-two variables were "looking forward to the semester beginning" and "the validity of the network materials". In speaking of "whether the online courses meet the offline course standards", the top-three variables were "the rhythm of lecturing on live or in recorded videos", "how many online tasks', and" the data frame and organization". The overall prediction accuracy of each constructed TAN Bayesian network model was 89.42% and 87.82%, and their AUC values were 0.75 and 0.93, respectively. CONCLUSIONS: To truly make online courses comparable to the off-line curriculum, teachers should fully understand how the students cope with their online learning at first. Then, only by perceiving and recognizing the students' expectations for education, by efficiently preparing and organizing online materials with all-round, clearly-structured, vivid, comprehensible contents and moderate difficult tasks, by well interacting with students through different websites and social media, can we truly achieve " ongoing learning with suspended class".

Key words: Online learning, TAN Bayesian network, COVID-19 pandemic

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