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
近年来大规模开放在线课程获得了较为广泛的关注。由于学习者学习方式不合理使得学习兴趣下降,学习效果不佳,MOOCs辍学率很高,针对这一问题,从学习者学习活动日志中自动抽取一段时间内连续特征,以学习者行为特征为自变量,建立MOOCs辍学...近年来大规模开放在线课程获得了较为广泛的关注。由于学习者学习方式不合理使得学习兴趣下降,学习效果不佳,MOOCs辍学率很高,针对这一问题,从学习者学习活动日志中自动抽取一段时间内连续特征,以学习者行为特征为自变量,建立MOOCs辍学预测模型。在KDD Cup 2015数据集上的实验表明,使用基于卷积神经网络的长短期记忆CNN_LSTM辍学预测模型,能够帮助MOOCs课程教师和设计者追踪课程学习者在不同时间步长的学习状态,从而动态监控不同阶段的辍学行为,模型的预测准确率高,这将为教师改进教学方法提供更合理的指导和建议。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.
文摘近年来大规模开放在线课程获得了较为广泛的关注。由于学习者学习方式不合理使得学习兴趣下降,学习效果不佳,MOOCs辍学率很高,针对这一问题,从学习者学习活动日志中自动抽取一段时间内连续特征,以学习者行为特征为自变量,建立MOOCs辍学预测模型。在KDD Cup 2015数据集上的实验表明,使用基于卷积神经网络的长短期记忆CNN_LSTM辍学预测模型,能够帮助MOOCs课程教师和设计者追踪课程学习者在不同时间步长的学习状态,从而动态监控不同阶段的辍学行为,模型的预测准确率高,这将为教师改进教学方法提供更合理的指导和建议。
