This work belongs courses	Software Engineering (Fuzhou University Zhicheng College - Department of Computer Engineering)
Hong Chenglong	211706174
Hang Lin	211706186
Where the job requires	The second pair job
The target job	Data integration and prototyping
Text of the job	as follows
Other references	no

Simple self-introduction

I am from Hong Chenglong Computer Engineering Computer Science and Technology, the school number 211 706 174, like basketball, animation.
My twinning partner from Department of Computer Engineering Computer Science and Technology of Forestry Air , school number 211 706 186, is my good friend.

On this blog by the common forest and finish my flight .

Data Integration

All the students are sharing their own survey data, due to the problems of each group is different, if the common problems of blind stitching together all the questionnaires, will not be useful information. So I selected the largest amount of data of the two groups (Zhang and returned to carrian, Zhang Lu and Wu Painting) questionnaire data, combined with data from our team for analysis.

Zhang returned to carrian data and analysis team

The following analysis of data from Zhang returned and in Carrian group, a total of 583 copies.
I extracted the "gender", "Venue", "whether taking notes", "how well" these four questions, made the following analysis, and recommendations for analysis.

The number of cases whether taking notes

In the case of do not do notes, most of the students are learning in the middle and poor, and can be clearly seen, learning the boys worse than female students.
In the case of taking notes, only a very small portion of poor learning outcomes of students, male and female students study the effect is very close.

Suggest:

Students should be appropriate to make notes, timely review and consolidate, it helps to absorb knowledge of the classroom.

The number of cases of different learning locations

At the table learning learning results significantly better in bed and elsewhere.

Suggest:

Students able to do so at the table to learn as much as possible, stay awake, can improve learning outcomes.

Zhang Lu Wu Painting and data analysis team

以下分析数据来自张露和吴娉婷小组，共有203份。
我从中提取了“与传统教学相比，您觉得线上授课的阻碍是什么？”、“使用线上授课，您对哪个部分更感兴趣？”这两个问题，做出了以下统计，并针对分析结果提出建议。

线上授课的阻碍

线上授课的阻碍前三名分别为“容易走神”、“没有课本”、“没有学习氛围”

给老师的建议：

采用碎片化教学模式。

有研究表明，学生线上连续学习时间一般不超过20分钟，而且在线教学不在教室见面，降低了教师的吸引力。结合我们目前的上课情况，老师很少给同学们下课，混乱了原本的上下课时间。建议老师合理规划一节课的时间，适度减少课堂教学内容。将一节课的知识点拆成15-30分钟一段来授课，其余时间可与学生交流互动、答疑。

线上授课学生最感兴趣的部分

线上授课的学生最感兴趣的部分前三名分别为“老师讲课”、“重难点讲解”、“在线答疑”

给老师的建议：

强调重难点知识。

线上学习中，面对繁多的知识点，同学们往往难以抓住学习的重点，且容易走神，常常错过一些重难点知识。老师如果能够在讲到重难点知识时强调一下，可以很好地吸引同学们的注意力，提高同学们的学习效率。

我与林航的数据分析

先了解一下同学们的学习吸收率。

可以看出有一半的同学其实对于知识点的学习吸收并不理想。

于是我们用小提琴图来分析一下学习吸收率与其他因素的相关性。
注：小提琴图展示了任意位置的密度，通过小提琴图可以知道哪些位置的密度较高。

学习态度较差的同学，吸收率在0-33%之间的人数密度明显比其它两种态度大。
学习态度中等的同学，吸收率在0-33%的密度较小，普遍在34%-66%和67%-100%之间。
学习态度较好的同学，没有人的吸收率处在0-33%，且在67-100%之间居多。

学习状态较差的同学，吸收率主要在34%-66%之间。
学习状态中等的同学，吸收率在0-33%的密度较小，普遍在34%-66%和67%-100%之间。
学习状态较好的同学，没有人的吸收率处在0-33%，且在67-100%之间居多。

学习态度与学习状态得出的结论基本一致，说明态度和状态确实影响着我们的学习吸收率。所以希望同学们尽快摆正心态，调整状态，在这段特殊时期，克服困难，学习渐入佳境，学习效果逐渐变好。

爬虫和词云

我们爬取了知乎关于“网课效率”的有关回答。
由于知乎的页面经过js渲染而成，不能简单地进行爬取，我使用selenium模拟谷歌浏览器运行，实现所见即所得。
以下是代码片段

from selenium import webdriver
...
url = 'https://www.zhihu.com/search?type=content&q=网课效率'
browser = webdriver.Chrome()
browser.get(url)
...
text = browser.page_source
parse_one_page(text)
...
browser.close()

我们将爬取到的内容，经过简单处理做成词云。