Open Courseware(OCW)and massive open online courses(MOOCs)are teaching and learning resources that are easily accessible to anyone with an Internet connection.OCW is digitally published learning content including full...Open Courseware(OCW)and massive open online courses(MOOCs)are teaching and learning resources that are easily accessible to anyone with an Internet connection.OCW is digitally published learning content including full and partial courses(syllabi,outlines,lectures in pdf or video,slides,reference lists,etc.),simulations,animations,tutorials,drills and practices,modules,podcasts,case studies,and quizzes.This content is free and can be adopted or adapted to the user's needs.MOOCs are online learning experiences taught by university professors using conventional educational tools including video lectures,interactive modules,assignments,study materials,discussion boards,quizzes and tests.MOOCs are offered free or at low cost for personal and professional learning,and as a supplement to classroom teaching.Many MOOCs and OCW focus on topics of interest to nursing,particularly to nurse educators.This article provides the reader with a brief history of the development of OCW and MOOCs,conceptual descriptions,and guidance about how to access and use these new online resources.展开更多
目的 探讨大规模开放式在线课程(massive open online courses,MOOCs)联合以问题为基础的教学法(problem based learning,PBL)在儿科学本科耳鼻咽喉专业教学中的应用效果。方法 选取2021年8月—2022年8月贵州医科大学2017级儿科学本科学...目的 探讨大规模开放式在线课程(massive open online courses,MOOCs)联合以问题为基础的教学法(problem based learning,PBL)在儿科学本科耳鼻咽喉专业教学中的应用效果。方法 选取2021年8月—2022年8月贵州医科大学2017级儿科学本科学生140名。按照随机数字表法分为对照组与研究组,各70名。对照组予以传统的儿科学本科教学模式,研究组予以MOOCs联合PBL教学模式。观察学期结束后,2组学生考试成绩、学生评价结果、学生满意度及教师评价结果。结果 学期结束后,研究组期末试卷成绩为(93.53±6.72)分、随堂测试平均成绩为(89.37±6.87)分、课后论文成绩为(86.35±6.32)分、讨论积极程度评分为(85.31±5.72)分及总成绩为(91.56±5.35)分,高于对照组的(83.25±6.45)分、(86.34±7.21)分、(78.53±6.17)分、(68.53±5.41)分、(81.25±6.27)分(P <0.05)。研究组对促进知识掌握、提升学习效率、激发学习兴趣、培养自主学习能力、培养问题分析和解决能力、培养临床思维方面的满意度及整体满意度均高于对照组(P <0.05)。研究组查阅文献能力、临床思维能力、自主学习能力、提出和解决问题能力的评分均高于对照组(P <0.05)。结论 MOOCs联合PBL教育模式可明显提升儿科学本科耳鼻咽喉专业学生的学习成绩,培养其临床思维、自主学习、分析和解决问题等方面的能力,并提升其教学满意度。展开更多
Massive open online courses(MOOCs)have become a way of online learning across the world in the past few years.However,the extremely high dropout rate has brought many challenges to the development of online learning.M...Massive open online courses(MOOCs)have become a way of online learning across the world in the past few years.However,the extremely high dropout rate has brought many challenges to the development of online learning.Most of the current methods have low accuracy and poor generalization ability when dealing with high-dimensional dropout features.They focus on the analysis of the learning score and check result of online course,but neglect the phased student behaviors.Besides,the status of student participation at a given moment is necessarily impacted by the prior status of learning.To address these issues,this paper has proposed an ensemble learning model for early dropout prediction(ELM-EDP)that integrates attention-based document representation as a vector(A-Doc2vec),feature learning of course difficulty,and weighted soft voting ensemble with heterogeneous classifiers(WSV-HC).First,A-Doc2vec is proposed to learn sequence features of student behaviors of watching lecture videos and completing course assignments.It also captures the relationship between courses and videos.Then,a feature learning method is proposed to reduce the interference caused by the differences of course difficulty on the dropout prediction.Finally,WSV-HC is proposed to highlight the benefits of integration strategies of boosting and bagging.Experiments on the MOOCCube2020 dataset show that the high accuracy of our ELM-EDP has better results on Accuracy,Precision,Recall,and F1.展开更多
针对MOOC中学生行为数据的长短期混合特性,为解决辍学预测中的动态类别不平衡问题,提出一种基于深度学习的辍学预测策略。首先建立以天为时间步长、周为学习周期的新型学生行为时间序列,以捕捉每一时间步长下时间序列数据的短期依赖关...针对MOOC中学生行为数据的长短期混合特性,为解决辍学预测中的动态类别不平衡问题,提出一种基于深度学习的辍学预测策略。首先建立以天为时间步长、周为学习周期的新型学生行为时间序列,以捕捉每一时间步长下时间序列数据的短期依赖关系和相邻学习周期之间的长期模式和趋势。然后结合辍学定义的两种不同表达揭示MOOC辍学预测的动态类别不平衡现象。接着引入基于代价敏感的长短期时间序列深度学习模型,以实现对高辍学风险学生的精准预测。最后在KDD Cup 2015数据集上的实验证明,所提策略能够有效帮助MOOC课程教师和教学管理者追踪课程学生在不同时间步长的学习状态,从而动态监控不同学习阶段的辍学行为。展开更多
随着互联网技术的兴起,大规模在线开放课程(massive open online courses,MOOC)作为一种新型的大规模开放在线教学模式,被广泛应用在医学领域。与传统教学模式相比,MOOC摆脱了时间与空间的限制,有利于知识的扩充和优秀教育资源的共享。...随着互联网技术的兴起,大规模在线开放课程(massive open online courses,MOOC)作为一种新型的大规模开放在线教学模式,被广泛应用在医学领域。与传统教学模式相比,MOOC摆脱了时间与空间的限制,有利于知识的扩充和优秀教育资源的共享。面对新生儿学这样一门既需要丰富的理论知识又需要缜密的临床思维的学科,以案例为基础的教学法(case-based learning,CBL)可以让学生在病例讨论过程中对所学的理论知识更好地加以运用,提高学生解决问题的能力。CBL教学法与MOOC教学二者相辅相成,突破了传统讲授式教学方法的瓶颈,改变了传统的师生观念,有利于提高学生自主学习能力。积极解决CBL教学法结合MOOC所面临的问题,推动新生儿学教学体系的改革,这为新生儿学人才的培养提供了有力的保障。展开更多
近年来,心血管疾病的患病率和死亡率不断升高,已成为重大的公共卫生问题,影像学检查在心血管疾病的诊断、疗效评估及预后判断具有重要作用,MRI检查因无辐射、软组织分辨高、兼有形态学及功能成像特点,已成为心血管疾病的诊疗不可或缺的...近年来,心血管疾病的患病率和死亡率不断升高,已成为重大的公共卫生问题,影像学检查在心血管疾病的诊断、疗效评估及预后判断具有重要作用,MRI检查因无辐射、软组织分辨高、兼有形态学及功能成像特点,已成为心血管疾病的诊疗不可或缺的方法,尤其是原发性心肌病。由于心血管磁共振(cardiac magnetic resonance,CMR)检查技术复杂、难度较大,基层医院(县级医院)影像技师操作困难,同时也影响了临床心血管疾病患者的诊疗效果,所以提升医学生心血管MRI检查技术课程的教学效果就显得愈发重要。文章结合PBL与大型开放式网络课程(massive open online courses,MOOC)方法探讨对心血管磁共振检查的教学效果,以促进该技术的普及应用,为解决基层卫生健康提供重要支撑。展开更多
文摘Open Courseware(OCW)and massive open online courses(MOOCs)are teaching and learning resources that are easily accessible to anyone with an Internet connection.OCW is digitally published learning content including full and partial courses(syllabi,outlines,lectures in pdf or video,slides,reference lists,etc.),simulations,animations,tutorials,drills and practices,modules,podcasts,case studies,and quizzes.This content is free and can be adopted or adapted to the user's needs.MOOCs are online learning experiences taught by university professors using conventional educational tools including video lectures,interactive modules,assignments,study materials,discussion boards,quizzes and tests.MOOCs are offered free or at low cost for personal and professional learning,and as a supplement to classroom teaching.Many MOOCs and OCW focus on topics of interest to nursing,particularly to nurse educators.This article provides the reader with a brief history of the development of OCW and MOOCs,conceptual descriptions,and guidance about how to access and use these new online resources.
基金supported by the National Natural Science Foundation of China(No.61772231)the Natural Science Foundation of Shandong Province(No.ZR2022LZH016&No.ZR2017MF025)+3 种基金the Project of Shandong Provincial Social Science Program(No.18CHLJ39)the Shandong Provincial Key R&D Program of China(No.2021CXGC010103)the Shandong Provincial Teaching Research Project of Graduate Education(No.SDYAL2022102&No.SDYJG21034)the Teaching Research Project of University of Jinan(No.JZ2212)。
文摘Massive open online courses(MOOCs)have become a way of online learning across the world in the past few years.However,the extremely high dropout rate has brought many challenges to the development of online learning.Most of the current methods have low accuracy and poor generalization ability when dealing with high-dimensional dropout features.They focus on the analysis of the learning score and check result of online course,but neglect the phased student behaviors.Besides,the status of student participation at a given moment is necessarily impacted by the prior status of learning.To address these issues,this paper has proposed an ensemble learning model for early dropout prediction(ELM-EDP)that integrates attention-based document representation as a vector(A-Doc2vec),feature learning of course difficulty,and weighted soft voting ensemble with heterogeneous classifiers(WSV-HC).First,A-Doc2vec is proposed to learn sequence features of student behaviors of watching lecture videos and completing course assignments.It also captures the relationship between courses and videos.Then,a feature learning method is proposed to reduce the interference caused by the differences of course difficulty on the dropout prediction.Finally,WSV-HC is proposed to highlight the benefits of integration strategies of boosting and bagging.Experiments on the MOOCCube2020 dataset show that the high accuracy of our ELM-EDP has better results on Accuracy,Precision,Recall,and F1.
文摘针对MOOC中学生行为数据的长短期混合特性,为解决辍学预测中的动态类别不平衡问题,提出一种基于深度学习的辍学预测策略。首先建立以天为时间步长、周为学习周期的新型学生行为时间序列,以捕捉每一时间步长下时间序列数据的短期依赖关系和相邻学习周期之间的长期模式和趋势。然后结合辍学定义的两种不同表达揭示MOOC辍学预测的动态类别不平衡现象。接着引入基于代价敏感的长短期时间序列深度学习模型,以实现对高辍学风险学生的精准预测。最后在KDD Cup 2015数据集上的实验证明,所提策略能够有效帮助MOOC课程教师和教学管理者追踪课程学生在不同时间步长的学习状态,从而动态监控不同学习阶段的辍学行为。
文摘随着互联网技术的兴起,大规模在线开放课程(massive open online courses,MOOC)作为一种新型的大规模开放在线教学模式,被广泛应用在医学领域。与传统教学模式相比,MOOC摆脱了时间与空间的限制,有利于知识的扩充和优秀教育资源的共享。面对新生儿学这样一门既需要丰富的理论知识又需要缜密的临床思维的学科,以案例为基础的教学法(case-based learning,CBL)可以让学生在病例讨论过程中对所学的理论知识更好地加以运用,提高学生解决问题的能力。CBL教学法与MOOC教学二者相辅相成,突破了传统讲授式教学方法的瓶颈,改变了传统的师生观念,有利于提高学生自主学习能力。积极解决CBL教学法结合MOOC所面临的问题,推动新生儿学教学体系的改革,这为新生儿学人才的培养提供了有力的保障。
文摘近年来,心血管疾病的患病率和死亡率不断升高,已成为重大的公共卫生问题,影像学检查在心血管疾病的诊断、疗效评估及预后判断具有重要作用,MRI检查因无辐射、软组织分辨高、兼有形态学及功能成像特点,已成为心血管疾病的诊疗不可或缺的方法,尤其是原发性心肌病。由于心血管磁共振(cardiac magnetic resonance,CMR)检查技术复杂、难度较大,基层医院(县级医院)影像技师操作困难,同时也影响了临床心血管疾病患者的诊疗效果,所以提升医学生心血管MRI检查技术课程的教学效果就显得愈发重要。文章结合PBL与大型开放式网络课程(massive open online courses,MOOC)方法探讨对心血管磁共振检查的教学效果,以促进该技术的普及应用,为解决基层卫生健康提供重要支撑。
