Established on peripheral sub-urban area of Sylhet city,Shahjalal University of Science and Technology is a public university well known for its beautiful natural environment and diversified landscape with green hillo...Established on peripheral sub-urban area of Sylhet city,Shahjalal University of Science and Technology is a public university well known for its beautiful natural environment and diversified landscape with green hillocks,waterscape,forests and biodiversity.But,the academic buildings of the campus were planned in an introvert way that the common void courts remain disconnected from the outside natural environment.Although designed with positive intention,most of the courts remain unused maximum the time of a year.As the campus natural environment is getting richer day by day and users prefer to spend more time in outside environment,it is high time to integrate nature into the academic learning.This research aims to explore the possibilities of these void courts to be incorporated with the outside natural environment to enhance joyful learning.A combined approach was adopted as research methodology consists of intensive physical survey,literature study,microclimate analysis,questioner surveys among the users,interviewing the field experts and selective national and international case studies.Lastly,a set recommendation has been proposed considering all the perspectives and issues that the research has identified.展开更多
Prior studies have shown the importance of classroom dialogue in academic performance,through which knowledge construction and social interaction among students take place.However,most of them were based on small scal...Prior studies have shown the importance of classroom dialogue in academic performance,through which knowledge construction and social interaction among students take place.However,most of them were based on small scale or qualitative data,and few has explored the availability and potential of big data collected from online classrooms.To address this issue,this paper analyzes dialogues in live classrooms of a large online learning platform in China based on natural language processing techniques.The features of interactive types and emotional expression are extracted from classroom dialogues.We then develop neural network models based on these features to predict high-and low-academic performing students,and employ interpretable AI(artificial intelligence)techniques to determine the most important predictors in the prediction models.In both STEM(science,technology,engineering,mathematics)and non-STEM courses,it is found that high-performing students consistently exhibit more positive emotion,cognition and off-topic dialogues in all stages of the lesson than low-performing students.However,while the metacognitive dialogue illustrates its importance in non-STEM courses,this effect cannot be found in STEM courses.While high-performing students in non-STEM courses show negative emotion in the last stage of lessons,STEM students show positive emotion.展开更多
目的分析儿童先天性心脏病超声心动图检查报告中文字描述信息与临床风险评估结果的相关性,并验证文本挖掘方法在此类分析中的可行性和应用价值。方法回顾性分析1 042例先天性心脏病患儿的彩色超声心动图报告,通过自然语言处理(natural l...目的分析儿童先天性心脏病超声心动图检查报告中文字描述信息与临床风险评估结果的相关性,并验证文本挖掘方法在此类分析中的可行性和应用价值。方法回顾性分析1 042例先天性心脏病患儿的彩色超声心动图报告,通过自然语言处理(natural language processing,NLP)技术进行特征提取与筛选,以患儿的风险等级为预测目标,借助机器学习算法构建决策树,推测出临床医师解读心脏超声报告时可能的决策路径。通过50次基于分层抽样的10折交叉验证评价模型的风险等级预测能力,进而评估报告在临床决策中的作用和价值。结果使用自动生成的全部三元语法(3-gram)或基于领域知识筛选后的特征,所训练的风险等级预测模型分别达到32.82%和48.57%的分类准确率,平均绝对误差(normalized mean absolute error,NMAE)分别为0.33和0.25。结论超声心动图报告中的文字部分,尤其是描述疾病征象的常用术语,能够在约75%的水平上反映先天性心脏病患儿的严重程度,为临床医师诊疗决策提供重要依据。展开更多
文摘Established on peripheral sub-urban area of Sylhet city,Shahjalal University of Science and Technology is a public university well known for its beautiful natural environment and diversified landscape with green hillocks,waterscape,forests and biodiversity.But,the academic buildings of the campus were planned in an introvert way that the common void courts remain disconnected from the outside natural environment.Although designed with positive intention,most of the courts remain unused maximum the time of a year.As the campus natural environment is getting richer day by day and users prefer to spend more time in outside environment,it is high time to integrate nature into the academic learning.This research aims to explore the possibilities of these void courts to be incorporated with the outside natural environment to enhance joyful learning.A combined approach was adopted as research methodology consists of intensive physical survey,literature study,microclimate analysis,questioner surveys among the users,interviewing the field experts and selective national and international case studies.Lastly,a set recommendation has been proposed considering all the perspectives and issues that the research has identified.
基金This work was supported by the Center for Social Network Research of Tsinghua University,Tsinghua’s Research Project(No.2016THZWYY03)the Project of Tencent Social Research Center(No.20162001703).
文摘Prior studies have shown the importance of classroom dialogue in academic performance,through which knowledge construction and social interaction among students take place.However,most of them were based on small scale or qualitative data,and few has explored the availability and potential of big data collected from online classrooms.To address this issue,this paper analyzes dialogues in live classrooms of a large online learning platform in China based on natural language processing techniques.The features of interactive types and emotional expression are extracted from classroom dialogues.We then develop neural network models based on these features to predict high-and low-academic performing students,and employ interpretable AI(artificial intelligence)techniques to determine the most important predictors in the prediction models.In both STEM(science,technology,engineering,mathematics)and non-STEM courses,it is found that high-performing students consistently exhibit more positive emotion,cognition and off-topic dialogues in all stages of the lesson than low-performing students.However,while the metacognitive dialogue illustrates its importance in non-STEM courses,this effect cannot be found in STEM courses.While high-performing students in non-STEM courses show negative emotion in the last stage of lessons,STEM students show positive emotion.
文摘目的分析儿童先天性心脏病超声心动图检查报告中文字描述信息与临床风险评估结果的相关性,并验证文本挖掘方法在此类分析中的可行性和应用价值。方法回顾性分析1 042例先天性心脏病患儿的彩色超声心动图报告,通过自然语言处理(natural language processing,NLP)技术进行特征提取与筛选,以患儿的风险等级为预测目标,借助机器学习算法构建决策树,推测出临床医师解读心脏超声报告时可能的决策路径。通过50次基于分层抽样的10折交叉验证评价模型的风险等级预测能力,进而评估报告在临床决策中的作用和价值。结果使用自动生成的全部三元语法(3-gram)或基于领域知识筛选后的特征,所训练的风险等级预测模型分别达到32.82%和48.57%的分类准确率,平均绝对误差(normalized mean absolute error,NMAE)分别为0.33和0.25。结论超声心动图报告中的文字部分,尤其是描述疾病征象的常用术语,能够在约75%的水平上反映先天性心脏病患儿的严重程度,为临床医师诊疗决策提供重要依据。
