疫情背景下基于闭环控制理论的线上教学实践

针对线上教学管理体系不完善、广大教师线上教学能力不足的问题,以微机原理及应用线上教学课程为例,以教育部的要求"线上教学与线下教学同质等效"为目标,提出并阐述基于自动控制原理中闭环控制理论的"以学生为中心,持续改进"的良性线上教学方法.     

疫情期间线上教学实践效果分析及返校后教学策略研究

为积极响应教育部疫情期间的应对措施,提出针对大学计算机公共课“平台+数据”线上混合式教学模型。通过分析学生学习过程中的数据以及调查问卷,说明该模式切实可行,针对现存问题以及学生返校后线上线下课程的衔接提出有效的解决措施。     

新冠肺炎疫情大数据可视化平台设计与实现

介绍了新冠肺炎疫情大数据可视化平台的搭建:以大量的新冠肺炎疫情数据为基础,借助Echarts技术以及交互组件,通过可视化图形与地图集成的方式对疫情数据进行可视化展示.实现从多个角度展示国内疫情发展趋势,丰富了疫情数据的展示内容,为广大人民群众了解新冠肺炎疫情提供了便利.     

新冠疫情下电类基础课程线上实验教学探索

实验教学在高职教学中占有举足轻重的地位.以新冠疫情期间“电子技术及实践”和“检测与传感器技术”课程为例,介绍了线上实验教学前期的准备工作,探索了线上实验教学的教学新形式、教学设计、实施过程等内容,实现了线上理论实验教学同步进行的效果,为同类课程的线上实验教学提供一定的参考价值.     

新冠疫情背景下青少年网络越轨行为研究

新冠疫情当前,青少年需要参与线上课堂完成学业任务,接触网络的机会和使用网络的时间大大增加,这导致他们实施网络越轨行为的可能性变大。本文从社会学理论视角出发,运用社会控制理论、紧张理论和差别接触理论对青少年网络越轨行为进行分析,并对后疫情时代青少年网络越轨行为的预防和控制提供对策建议。     

基于情感特征的新冠肺炎疫情舆情演化分析

针对突发事件的舆情演变态势进行分析,发现社会舆情的演变规律,提出了一种基于情感特征的舆情演化分析方法,该方法包含舆论情感分析模块与舆情演化分析模块.舆论情感分析模块基于B E RT预训练模型和BiGRU模型,其中BERT作为词嵌入模型提取舆情文本特征向量,BiGRU则用于提取文本特征向量的上下文联系实现对舆情数据情感极...     

数据结构课程的在线教学实践

在疫情防控期间,数据结构这门大数据专业的专业核心课程采用在线的方式开展教学,本文从教学内容组织、教学视频、在线直播、案例与课程思政等几个方面进行了在线教学的实践,教学效果良好。     

The pandemic semesters: Examining public opinion regarding online learning amidst COVID-19

The current educational disruption caused by the COVID-19 pandemic has fuelled a plethora of investments and the use of educational technologies for Emergency Remote Learning (ERL). Despite the significance of online learning for ERL across most educational institutions, there are wide mixed perceptions about online learning during this pandemic. This study, therefore, aims at examining public perception about online learning for ERL during COVID-19. The study sample included 31,009 English language Tweets extracted and cleaned using Twitter API, Python libraries and NVivo, from 10 March 2020 to 25 July 2020, using keywords: COVID-19, Corona, e-learning, online learning, distance learning. Collected tweets were analysed using word frequencies of unigrams and bigrams, sentiment analysis, topic modelling, and sentiment labeling, cluster, and trend analysis. The results identified more positive and negative sentiments within the dataset and identified topics. Further, the identified topics which are learning support, COVID-19, online learning, schools, distance learning, e-learning, students, and education were clustered among each other. The number of daily COVID-19 related cases had a weak linear relationship with the number of online learning tweets due to the low number of tweets during the vacation period from April to June 2020. The number of tweets increased during the early weeks of July 2020 as a result of the increasing number of mixed reactions to the reopening of schools. The study findings and recommendations underscore the need for educational systems, government agencies, and other stakeholders to practically implement online learning measures and strategies for ERL in the quest of reopening of schools.     

疫情下，一种完全线上教学模式的实践

Currently, novel coronavirus is spreading all over the world, colleges and universities all use a variety of informatizationmethods actively responded to the call of the Ministry of Education. They all have launched online teaching practices to achievesuspend classes without stopping teaching and learning”. This article takes the Fundamentals of Analog Circuits course as an exam-ple to introduce the process of a completely online teaching activity in detail under the novel coronavirus situation.     

The impact of risk and protective factors on online teaching experience in high school Italian teachers during the COVID-19 pandemic

The lockdown due to COVID-19 in Italy resulted in the sudden closure of schools, with a shift from traditional teaching to the online one. Through an online questionnaire, this survey explores teachers' experience of online teaching, the level of risk factors (e.g., stress) and protective factors (e.g., locus of control) and their impact on satisfaction levels during the social distancing. One hundred seven high school teachers from Lombardy, an Italian region very affected by the COVID-19 outbreak, participated. Results show that depression and stress are the main predictors of satisfaction levels for online teaching. In addition, coping, locus of control and self-efficacy emerge as important protective factors. Finally, although there is great satisfaction with the online teaching experience, critical elements emerged. This study is relevant because it describes the critical elements of the online teaching experience, and identifies some protective factors and the main risk factors in teachers operating in an area strongly marked by social restrictions imposed by the pandemic. High school teachers emerge as a sub-group of the general population with specific psychological reactions. Considering the results, it is possible to suggest providing high-quality educational support and crisis-psychological oriented services to teachers, and help to maintain the psychological well-being.     

疫情下数据结构课程线上教学模式探索与实践

为了在新冠肺炎疫情的影响下充分保障正常的教学秩序,全力做到“停课不停教、停课不停学”,落实“教师真在教、学生真在学”,数据结构教学团队利用超星“一平三端”智慧教学系统和钉钉直播相结合的方式,在教学准备、教学实施、学习评价和教学改进等四个方面开展线上教学模式探索和实践,实现了将线上线下混合式教学转变为纯线上教学模式,体现了“以学生为中心”的教学理念,达到线上与线下同质同效。     

Python程序设计课程教与学的线上设计与实践

在疫情防控期间,全国高校响应教育部“停课不停教、停课不停学”的号召积极开展在线教学工作。本文充分探讨了作为公共基础课的Python程序设计课程线上教学的教学流程,并从教、学、教学相长、平台的二次开发等几个方面进行了教与学的实践。     

网上教学模式初探

本文对网上教学的模式进行了分类，提出基于局域网上（多媒体网、视频点播网）的教学模式和基于Internet上（个别学习、1时1、1对多、多时多）的教学模式。并分别讨论各种了网上教学模式的信息流程，教与学的关系，及网上教学具有时效性，共享性，交互性和个别化等特点。网上教学模式能在教师的主导作用引导下，充分发挥学生的主体作用。最后时加强网上教学的实践与应用提出自己的见解。     

COVID-19疫情环境下用电行为分析

针对COVID-19这一特殊时期,利用大数据技术,处理原始不同数据结构的电力数据.将用户用电行为的数据分成内部数据和外部数据,其次是确定用电行为分析,通过对传统的K-means聚类算法改进,提高K-means效率.最后利用改进算法聚类出的类别构建用电行为模型,实现用户用电行为分析.最终实现达到帮助国家电网公司达到电力智能分配的目的,并且给出了大致的政策倾向.提升国家各部门监管能力,助力国家应急管理.     

网上教学模式初探

本文对网上教学的模式进行了分类,提出基于局域网上(多媒体网、视频点播网)的教学模式和基于Internet上(个别学习、1对1、1对多、多对多)的教学模式。并分别讨论各种了网上教学模式的信息流程,教与学的关系,及网上教学具有时效性,共享性,交互性和个别化等特点。网上教学模式能在教师的主导作用引导下,充分发挥学生的主体作用。最后对加强网上教学的实践与应用提出自己的见解。     

Crowd management COVID-19

Crowds are a source of transmission in the COVID-19 spread. Contention and mitigation measures have focused on reducing people’s mass gathering. Such efforts have led to a drop in the economy. The application of a vaccine at a world level represents a grand challenge for humanity, and it is not likely to accomplish even within months. In the meantime, we still need tools to allow the people integration into their regular routines reducing the risk of infection. In this context, this paper presents a solution for crowd management. The aim is to monitor and manage crowd levels in interior places or point-of-interests (POI), particularly shopping centers or stores. The solution is based on a POI recommendation system that suggests the nearest safe options upon request of a particular POI to visit by the user. In this sense, it recommends places near the user location with the least estimated crowd. The recommendation algorithm uses a top-K approach and behavioral game theory to predict the user’s choice and estimate the crowd level for the requested POI. To evaluate the efficiency of this technological intervention in terms of the potential number of contacts of possible COVID-19 infections and the recommendation quality, we have developed an agent-based model (ABM). The adoption level of new technologies can be related to the end-user experience and trust in such technologies. As the end-user follows a recommendation that leads to uncrowded places, both the end-user experience and trust increased. We study and model this process using the OCEAN model of personality. The results from the studied scenarios showed that the proposed solution is widely adopted by the agents, as the trust factor increased from 0.5 (initial set value) to 0.76. In terms of crowd level, these are effectively managed and reduced on average by 40%. The mobility contacts were reduced by 40%, decreasing the risk of COVID-19 infection. An APP has been designed to support the described crowd management and contact tracing functionality. This APP is available on GitHub.