“互联网+”背景下基础化学原理实验在线学习与考试系统的开发与实践

In recent years, the use of mobile terminals such as smart phones is increasingly popular with the rapid development of the mobile Internet. The online learning and examination system of basic chemistry laboratory is designed and developed under the background of "Internet + education". The system can achieve five functions: chemical laboratory safety education and examination, theoretical learning and examination of basic chemistry laboratory, test database management, online examination on mobile terminal, score management and statistical analysis. The development and construction of this online system conforms to the development trend of "Internet + education", which is beneficial to both students' independent learning and examination resource saving.     

UOOC平台与腾讯课堂相结合的“在线异步教学”

The COVID-19 broke out on a large scale, and online teaching became the first choice for teachers. Avoiding network congestion and ensuring learning effects are the dilemmas faced by online teaching. With the help of the UOOC platform and the Tencent Classroom, taking the "Polymer Structure and Performance" course as a sample, we suggested a new "online asynchronous teaching" model. Among them, the SPOC course is convenient for students to manage their learning time; "Chapter Quiz" and "Student-to-Student Mutual Evaluation" are helpful for students to obtain multi-angle feedback; project tasks are focused on case analysis so that students carry out inquiry-based learning. Overall, the "online asynchronous teaching" model breaks the time and space restrictions on teaching and learning, guarantees the quality of teaching during the epidemic, and lays the foundation for creating a "golden class".     

“课前六备、课中六讲、课后六辅”在线课程混合式教学模式研究与实践——以基于“雨课堂+钉钉直播”的中等无机化学课程为例

Taking the online teaching of "inorganic chemistry" course at Dezhou University as an example, the "six steps before class, six steps in class, six steps after class" online course teaching model based on "Rain Class + Dingding Live" and an online "short class" were constructed. The main content and implementation methods of teaching design, teaching process, teaching effect, teaching reflection, teaching evaluation in online course teaching were discussed. It has been proved in practice that this model can effectively improve students' learning interest and enhance teaching effects.     

巧用信息手段，打造疫情中无机化学云课堂

Aiming at the requirement of "suspending classes without stopping learning" in the epidemic situation of new coronary pneumonia, a variety of information technologies were used to construct a cloud classroom of inorganic chemistry. How to use Tencent classroom, Superstar learning and WeChat public account to construct this "inorganic chemistry" cloud class was introduced. The teaching effect of Inorganic Chemistry was discussed.     

云教学在化学师范生数字化实验课中的应用研究*

结合本校化学教学论实验的教学现状,融合云教学及翻转课堂理念对教学进行重构,设计并实施以“蓝墨云班课”云平台为师生互动桥梁的翻转课堂云教学。以中学化学数字化实验教学为例,让化学师范生以“学习者”和“未来教师”的双重角色体验翻转课堂云教学的学习过程,并利用SERVQUAL模型评价法对云教学进行数据分析、反馈完善。通过进一步问卷分析,了解到学生不仅熟练地掌握了所学知识内容,而且对翻转课堂模式与实施细节有了更为深刻的理解,最后对翻转课堂云教学及师范生教学提出了几点建议。     

物理化学教学模式的探索与实践

在当今高等教育创新人才培养要求下,结合国内外教育先进技术方法,通过物理化学教学实践,探索了讲授课堂、翻转课堂、对分课堂等3种教学模式,教学实效性结果表明,开放式的教学更利于提高教学质量,对分课堂教学模式更适合于我校学生学习物理化学。     

化学类专业课程在线教学模式的探索与实践*——以“分析化学”为例

以分析化学为例,构建和实践了以学生为中心,实现课上与课下、理论与实践、现实与虚拟多种教学方式有机融合的教学模式。本教学模式为化学类专业课程完全线上教学的开展提供了可行途径,同时为线上线下混合式教学的开展提供了新的思路。     

带机器准备时间的平行机ordinal排序及近似算法

本文研究带机器准备时间的m台平行机ordinal在线排序问题。讨论了在极小化最大机器完工时间和极小化最大工件完工时间两种目标下的不同下界和相应的在线近似算法。对第一个目标，我们得到了3/2的下界和最坏情况界为2-1/m的近似算法。对第二个目标，我们得到了最坏情况为m的最好近似算法。我们还对一些特殊情况进行了分析。     

数学生活化的思考——用一次函数解决生活中的实际问题课堂片段实录

生活离不开数学,利用数学知识可巧妙解决生活中的许多实际问题,反过来把数学练习与学生的生活经验结合起来,既能让学生对所涉及的数学知识有一个更深刻的认识,又能体现出数学的应用价值．用一次函数解决问题是学生理论联系实际,让学生学以致用的一个重要平台．前不久讲评一次函数的应用时学生分别在书中、评讲和补充习题上发现了一类问题的解答与生活经验不符,接下来展示学生如何找出问题以及修改的教学片段．     

Online multiple instance regression

The multiple instance regression problem has become a hot research topic recently. There are several approaches to the multiple instance regression problem, such as Salience, Citation KNN, and MI-ClusterRegress. All of these solutions work in batch mode during the training step. However, in practice, examples usually arrive in sequence. Therefore, the training step cannot be accomplished once. In this paper, an online multiple instance regression method ＂OnlineMIR＂ is proposed. OnlineMIR can not only predict the label of a new bag, but also update the current regression model with the latest arrived bag. The experimental results show that OnlineMIR achieves good performances on both synthetic and real data sets.