明翰全日制英国硕士学术写作V0.1（持续更新）

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传送门

1. 明翰英语教学系列之方法篇
2. 明翰英语教学系列之音标篇
3. 明翰英语教学系列之名词篇
4. 明翰英语教学系列之动词篇
5. 明翰英语教学系列之形容词与副词篇
6. 明翰英语教学系列之冠词篇
7. 明翰英语教学系列之代词篇
8. 明翰英语教学系列之介词篇
9. 明翰英语教学系列之连词篇
10. 明翰英语教学系列之数词篇
11. 明翰英语教学系列之时态与语态篇
12. 明翰英语教学系列之句法篇
13. 明翰英语教学系列之语料篇
14. 明翰英语教学系列之雅思篇
15. 明翰英语教学系列之雅思听力篇
16. 明翰英语教学系列之雅思阅读篇
17. 明翰英语教学系列之雅思写作篇
18. 明翰英语教学系列之雅思口语篇
19. 明翰英语教学系列之雅思常见词汇与固定搭配篇
20. 明翰英语教学系列之PTE与多邻国篇
21. 明翰全日制英国硕士留学攻略
22. 明翰全日制英国硕士常见词汇与固定搭配

PAP学术写作

（以下内容大部分来自英国某大学6周语言班。）

学术写作有很多个分支，什么是PAP？
Position Analysis Paper，
立场分析，找出一个有争议的问题，
批判性地分析、评估、讨论不同的立场，
之后[建立/形成]自己的立场。

作为作者，要考虑到自己的读者是哪些受众群体，
要考虑到使用的内容和词汇是否适合读者。

由于字数限制，你要做取舍，
哪些地方该深入，哪些地方该省略。

批判性思维

英国大学注重培养学生的批判性思维与自主学习能力。
一般情况下，老师充当引路人角色，使用问题导向式教学方法，
老师不会“灌输”唯一的答案，而是把自主权交给学生，
老师看中的是探索的过程。

很多人一看到"批判"二字自然会联想到批评，
但其实是一种误解，批判性思维一种非常普遍的西方教育思维模式。

批判性思维起源于希腊教育学家苏格拉底，
“批判性”（critical），
这个词源本身就来自希腊文“kritikos”，
意思是辨别力，洞察力，判断力，引申义有敏锐，精明的意思。

主旨为不直接告诉学生正确答案，
而是引导学生自己去发现问题，
苏格拉底经常做的是通过双方辩论、向对方提问，
揭露对方回答问题中的矛盾之处及推理缺陷，
或是提出反例，引入更为深入的思考。

不能只停留在对信息的盲目吸收，
而是要进行评估、比较、证明、分析，
找到逻辑性，给出[原因/理由/证据]，
全面地评估与分析后再得出自己的结论。

看待问题与事物不能只从一个方面，
要从多个方面多个维度去观察去分析，
不要只看好的方面，也要看坏的方面，反之亦然。

对概念、定义、问题进行进一步的独立思考与分析，
而不是人云亦云，循规蹈矩，
权威不应全信，可以去质疑。

不要100%相信任何人，大胆地理性地去质疑去怀疑去反驳，
不以否定为目的，不对他人批评和攻击，基于证据与逻辑，
用严谨的逻辑，客观的分析，批判的眼光去看待问题，
去做出合理的判断。

定义解释

Position

[立场/观点/态度]，
立场是你正在支持或反对的想法（赞成或反对某事），
然后你得给出原因、例子、证据，
以及引用其他文献来证明你的立场。

论点提出一个结论（想法、观点、判断或观点），
并说服读者去接受。

A position is the idea that you are supporting (or refuting). Then, you need to argue for that position by giving reasons, examples, evidence and support from other sources.

例如：
“机器人不能替代老师”
“Against skyscrapers”
“In favour of skyscrapers”

Position meaning ‘argument’.

This question might be better: Explain your views on an important argument that you have included in your paper.

It would be good if you can be critical and show how much you agree/disagree with the argument. You should also refer to a source. (e.g. One argument I included in my paper was Bladstoff’s idea that…)

Reason

支撑立场的原因（共同的信念，假设和价值观）。
论据包括原因和证据。

例如：
“Skyscrapers are easy targets for terrorist attacks.”

“Constructing high-rise buildings is an effective solution to housing shortages.”

Evidence

支撑立场的证据（事实，事例，数据和权威人士的说法）。

Murder scene:
Police need evidence to arrest the murderer.

Evidence = fingerprints, DNA, blood on an item of clothing.

Support = witness who said that they saw the murderer going into the person’s house

例如：
“In 2001, two airliners were flown into the World Trade Center in New York.”

“Skyscrapers can accommodate more people than low-rise buildings on the same land.”

Argument

These series of statements (Position+Reason+Evidence) can be called arguments.

An argument = a coherent series of reasons, statements, or facts intended to support or establish a point of view；

（How a position is supported by following arguments through a text；）

The word argument can be used in two senses:
a) for the assignment as a whole, supporting the conclusion: the overall argument; or b) for each step in the argument (see Explore EAP-1).

Thus, above we have two arguments in sense (b),
one against and one in favour of skyscrapers.

These two arguments will form steps in an overall argument in sense (a) which will support a conclusion either against or in favour of skyscrapers.

The overall argument will reach its conclusion after evaluating all the individual arguments.

argument是正面论点，counter-argument是反面论点，
如果字数太多，则要在分段的时候考虑正反分两段。

Rebuttal

反驳，先正论，再反论，之后再反驳一下，
把立场又回到正论上来。

Source

资料来源，外部资源，
在论文中需要引用外部资源来支撑论点，
可以是文献（别人发表的论文）、调查研究、网站等等。

有的论文要求引用文献数量，
引用文献不能是为了引用而引用，
需要支撑自己的观点才行！！！

Source = literature / research that has been published (written or spoken).

引用集合

quotation	原文引用	直接引用文献原文，复制粘贴出来的，带引号的
citation	引用	一般称为in-text citations，是在论文内部的引用部分，需要包含作者的姓、发布年份、页号（有时），quotation与citation都属于in-text reference
integral citation	包含引用	被引用信息作为句子的一部分进行呈现。
non-integral citation	非包含引用	被引用信息不作为句子的一部分进行呈现。
secondary citation	二次引用	你引用了A，A里引用了B，而你想引用B，但又找不到B的原文，那B就可以成为你的2级引用。可以把2级饮用当1级引用使，但前提是你真的找到了原来的文献。
reference	引用	在论文的底部会有引用列表，包含完整资料来源的详细信息，文献列表一定要用工具（Mendeley）自动生成，如果完全自己手写会很低效。

Patch Writing

补丁写作，修改原句中的部分词句，
然后成为自己文中的一部分，并没有完全进行改写。

Synthesizing

when you are building argument, you’re looking for writers’ positions, ideas, things they agree on or things they disagree on.

synthesizing is bringing all those writers together.
synthesizing is putting writers’ ideas together.

synthesizing（整合）非常重要，
synthesizing将帮你把论文的格式变得更加得体，
你需要去合并你自己的argument与从其他文献中获得的argument。

Paraphrasing

”改写”，当你在给出一个想法的细节时使用；
paraphrasing = same meaning, your own words

除了quotation以外，永远不要直接从文献材料中复制粘贴内容，
抄袭部分太多，查重率高，需要改写文献中的原文。

需要用自己的话来paraphrase，
不要因为paraphrasing很难就直接用quotation，
尽可能少用quotation，如果quotation很多会扣分，
如果你读不懂文献原文，你就没办法去paraphrase，
但如果你发现原文有一个短句非常有趣写的非常好非常容易解释，
那你可以直接引用过来。

Summarising

”总结”，当把全部argument压缩成非常非常紧凑的句子时使用；

写作步骤

0. 时间管理

学会时间管理，哪些时间用来做什么，
不然肯定会delay，跟做项目是一个道理，从deadline往前推，
一定要给自己留出大量的查缺补漏与改正的时间，
不要写完第一版就交作业，不要踩点交作业，
要不断的review、打磨、检查、润色。

在不断的review自己的论文时，
正好有机会问自己WHY，读者也会问WHY，
为什么那样解释，有例子吗？
试想自己是3岁的小孩问一堆WHY，
你需要去解释一堆WHY。

第一版先出个思维导图，之后第二版出若干个主体段，
再之后把全文写好，之后继续不断的打磨。

先多写一些内容，全部写完后，再做减法，删掉冗余内容，
不要上来就做减法，因为刚开始你的字数可能都没法达标。

最后交作业前再通读一边，能说服读者吗？有逻辑性吗？
把自己当成是读者而不是作者。

1. 学术阅读

[学术阅读/阅读文献]非常重要，是学术写作的基础，
一般我们先去思考，对哪个topic比较感兴趣。

之后便是阅读，
去搜索这个topic相关的论文（谷歌学术，大学图书馆，学术论文网站等等），
之后再去搭框架，
不断缩小topic范围，评估文献，
关于topic的批判性思维，
识别争议性，批判性的分析，反思等等。

千万不要一字不差的从头到尾把文章所有内容全部读一遍，非常浪费时间和精力，要分清主次，不用每句话都精读。

在阅读时需要写笔记，写文献的概述，
[组织/管理]自己的[想法/笔记]，
有助于整理思路，
为后面的写作以及[文献综述]打基础，
去搜索与搜集更多相关的文献，
这样更能找到适合你的文献材料。

再缩小自己的topic与title，
知道争论性在哪里，
把正面论点与反面论点分类组织，
之后才开始动手写，在写的过程中，
也可能需要的文献。

记笔记时，可以在高亮后添加一些自己的评论，
以及总结+改写文献中的原文。

此外，自己写的论文中的一些内容和想法，
必须需要引入文献来支撑，
需要来源于已经发布在互联网上的的其他学术论文&研究文章，
从外面引用到自己的论文中，
这个过程要阅读大量的学术文章，
去看看前辈们是怎么想的。

如果只是自己单纯的想法，并没有引用任何文献，
那么会被视为是不可靠的言论，
那读者（考官）为什么要相信你？

阅读资料来源，找文献，找出证据，
是最难最核心的部分。
我们需要找文献、做笔记、归纳总结文献中你要用到的想法，改写成文内引用。

随着可用文献数量的增多，我们可以将其分类，
有助于管理和提升效率。

Make sure you follow the procedure step by step:

1. take notes;
2. stay away from the original source;
3. summarized where necessary;
4. paraphrase, put as much as citations wherever necessary, keep an editing your work;

在硕士阶段的论文只需要从发布过的文献中寻找资料即可，
不用自己做研究，自己做数据。

Primary research = raw data.
Secondary research = research from others.

在我们阅读文献资料时，我们需要记笔记，判断，拥有逻辑思维，have evidence。

学术论文一般是个人或团队有一个想法，
然后他们去做实验去构建理论，之后提供实验结果。
我们可以从中获得自己想要的相关的内容。

学术论文的阅读跟阅读报纸小说不一样，
阅读学术论文可能需要多次，需要有目的性的阅读。

第一次，通过看摘要，开头段，
结尾段，副标题等得到大意并决定这篇论文是否跟自己的东西相关。

之后再看看哪些地方你想看，再深挖细节。
不用把所有的内容都仔细的看一遍去仔细的理解一遍，
只在你感兴趣的地方上进行精读即可。

在阅读的过程中，遇到不懂的知识点，可以先暂停下来，
然后去搜一下相关的知识点，之后再继续阅读。

在继续阅读之前，试着“猜”下一步，
如果你对了，那就表明这篇论文写得很好，你理解了作者的思路。
如果你错了，你可能发现了一个有趣的替代方法，可以用在自己的论文中。

思维导图（Mind Map）

先头脑风暴一下，
把任何你想到的东西都列在思维导图中，
保证它好看、明确且容易被读懂，
这个图相当于是论文的[框架/大纲]，
可以使用思维导图，把大脑中的想法可视化，
这样更符合大脑思维模式。

当初始的[大纲/diagram]准备好后，
再去相关的学术资料去了解更多相关方面的内容，
之后就会自然而然地形成你自己的Research。

使用数字版文献

在写学术论文时，可以使用Google Scholar，
学校搜索引擎可以用来访问学术资源，
大学也有一些学术资源可以共享给学生，
细节可以问自己的tutor，学校的线上图书馆，
尽量使用数字版资料与文献，
用线上版的学术论文或期刊，
可以通过全局检索关键字来快速定位，
非必要不看纸质文献或纸质书，性价比超级无敌低。

批判性阅读

Critical Reading，
批评性阅读是写论文的前置步骤，在阅读文献文章时，
需要养成良好的思考模式，之后才能写出好的论文。

对于普通人而言，文章提供的就是事实，
读者只是单纯的从文章中获取信息，
以获取信息为唯一目的。

而对于留学生而言，
除了关注文章内容外，还需要跳出文章，
不是被动且顺从地接受作者的理论，
而应该是主动寻找作者论点的立场，
尝试用不同的思维方式解读文章主旨。

专家或老师或新闻上讲的东西就是100%正确的吗？
不一定吧，需要勇于质疑他们的观点，
我们不能接受阅读的所有内容都是真的。

To read critically means to ‘ask’ questions about the information in the text.

不要只是看谁说了XXX是对的，XXX是错的，
而是对着文章问自己一堆问题：
这篇文章的出处是哪里？谁写的？什么时候发布的？
文中的内容有进行讨论吗？还是都是基于事实的描述？
讨论的强弱？
我是怎么看待这个观点？
对于文中的观点，我的主观想法是什么？
同意吗？不同意吗？
多大程度下同意？多大程度下不同意？为什么？
如果不同意的话，我能提供证据反驳吗？
我如何支撑我的观点？（从文献中找到一些证据来支撑）

Claims（主张）
文章的主要论点或论点是什么？作者的主要观点是什么？

Logic（逻辑）
作者是如何得出这些结论的？
作者的逻辑推理步骤是什么？作者的逻辑是合理的吗？

Evidence（证据）
作者提出了哪些证据来支持他的观点？
作者提供的证据充分吗？
有说服力吗？你能想出哪些反驳作者观点的证据？

Assumptions（假设）
作者的观点是否建立在某些隐藏的假设之上？
如果是的话，这些假设是对的吗？

Alternative Arguments（替代性论点）
你能想到哪些作者没有考虑到的替代性论点吗？

在写作之前, 你需要首先成为一名批判性的读者，
什么都不读，是没有办法写论文的，
需要花至少50%以上的时间去阅读。

作为读者，在批判性阅读时，
需要识别与分析academic argument，
然后判断它们的强弱。

作为作者，通过使用和分析在文献中找到的argument，
来建立你自己的argument。

学术阅读很枯燥很难很无聊，但每天都要读学术文章，
熟能生巧，没有捷径可言。

我们要知道要读什么，什么是好的文献，
什么是浪费时间，如何做笔记。

可以把原文中的句子用不同的颜色高亮，
这样会更醒目，更容易帮助你深入理解文章。

阅读要分不同的目的，需要读若干次，需要花时间，
很难第一次读什么都搞定，
在阅读之前就清楚自己的目的是什么：
第1次读，general idea = gist；
第2次读，即使第1次没读明白也OK，第2次关注language(grammar and vocabulary)；
第3次读，structure；
第4次读，知道了idea以后，
需要考虑批判性，深入思考，
同意？不同意？有哪些idea不确定？

Microsoft’s Immersive Reader
用这个工具可以帮助阅读比较难理解的文章，
需要登录https://www.office.com

在阅读时，需要考虑为什么我在阅读这篇文章？
我需要从这篇文章中使用哪些内容？
什么内容是重要的？

预读

正式阅读前先对论文有一个大致的了解，
预读能够让读者在精读之前了解论文的大致内容和布局。

Reading for a general idea.
先看概要、开头段、结尾段，先了解文章大意，
略读文章以获取内容和结构的概要。

Scanning and Skimming:
Skimming is to read quickly.
为了快速阅读，了解文章大意。

Scanning is to find pieces of information.
在文中快速找到关键字。

语境化

将文本置于特定的历史、创作背景和文化语境中，
在阅读文献时，读者往往会加入个人的理解。

读者对文献的理解受到个人所处的特定时间和环境的影响。

但随着时间的推移，
这与文本的创作时间与写作环境可能已经截然不同。
批判性阅读要求读者置于源语语境之中思考问题。

带着问题理解与记忆

对文本的内容提出问题，这些问题有助于帮助你理解阅读内容。
在阅读复杂的学术文献时，你可以随时记下不懂的问题。
如果你能对每一个段落或章节提出一个问题，
那么，你对于文本的理解将会更加透彻深入。

但值得注意的是，每个问题都应该针对文章的主要观点提出，
不应过于关注插图或细节，并尽量用自己的话来表达，
而不是简单地从段落的截取一部分。

注重反面观点

反思与分析那些与你的信仰和价值观不一致的观点，
标注文献中的与你意见不一样的地方，并做一个简短的标注，
说明你对文本的想法或理念冲突是什么。

比较和对比相关文献

分析多个同质化文献之间的相似性与差异性，
才能更好地理解文本，
有助于读者理解作者采取不同角度理解某个特定问题的原因。

2. 找到争议问题

找到[controversial issue/topic]

争议性是什么？
Controversy means opposition，
What is the fight in it?
What is the opposition in it?
Why am I thinking in it?

争议性具有许多立场、意见、看法等等，
没有一个严格统一的结论，
有很多的but、but、what if等等。

我们不要只是找优缺点，尝试找更多的groups，更多的争议性，
如果论文没有争议性，没有反对的声音，那么分数会直线下降，
论文中必须要体现出争论，不同的论点分别是什么。

如果你的论文仅仅只是叙述性，而没有批判性，
如果没有试图去衡量不同的角度，多大程度上应该去[做/使用]，
多大程度上不应该去[做/使用]某事，就意味着没有批判性，
这样分数就会很低，
不能仅仅只是简单的[是/否]或[好/坏]的那种论文。

在1个topic中，必须有平衡、有判断、对于不同角度的逻辑展示，
对于争议性问题，每个[个人/组织]的观点都可能会不一样，
因为大家的立场不一样，看问题的角度不一样。

论文中的group越多越好，把更多的相关的group拉进来，
因为这样就会有更多的positions，会更有争议性，更有逻辑性。

搜索topic关键字

要选择一个具有争议性的topic，
怎么界定我选的topic是不是好的？

先去学术网站上搜索与阅读，
如果能找到跟这个topic相关联的学术文献或内容比较多，
那就证明对了，否则就得换topic，
或者你用错了关键字搜索。

但如果找到了特别特别多，那就得去缩小你的范围，
更聚焦在某个更小的topic上，
你不可能覆盖到1个非常大的topic，
要讨论的东西太多太多了，因此要聚焦与缩小。

如果搜不到相关的文章，可以尝试去修改搜索关键字，
或许你需要稍微修改一下你的topic。

假如如果你只是一直寻找关于中国的文献资料，
你可能会把你的topic弄向错误的方向。

因为你的question只聚焦在了一个特定的国家，
或一个特定的环境上，但并不代表你只研究这些。

假如我的聚焦问题是关于小学生学习语言的特性，
也许网上没有相关的研究，
那我就要去寻找下一个更合适的目标，
也许是关注稍微年长一点的学生。

在topic的选择上，一定要非常的聚焦，不能太广泛，
把范围缩小，这样会更有争议性，
这样就不会在讨论中迷失方向且更容易管理，
这样也不用需要大量的文献材料作为支撑，节省时间，
只找一些重要的文献就好。

确保选择那些已经被调查研究的或是值得调查研究的topic，
尽量找一些大众化、具有普遍性的topic，
如果太另类太偏门，就会增加写作难度，
有时甚至可以是一些愚蠢的topic，
愚蠢问题也是可以研究拓展的，可以深耕的。

可能你自己选的topic的题材是好的，
但搜不到或很少搜到相关的学术资料，那也是白搭。

定好topic后，继续批判性阅读相关文献，
去更深入的了解这个topic，
去找不同的position，然后去批判性地分析positions。

the topic is your initial idea…then the positions develop from your research and reading of different sources
the paragraphs are the structural and functional components of your writing- your essay…

when we start the research we shift from the initial subjective opinion to objective reasoning depends on the logical justification of the researcher…

3. 分析立场

positions/groups

写论文时，给定题目后，
需要自己去网上找相关的学术论文，在看完这些学术论文后，
需要做笔记，归纳主旨，记录生词，然后找立场。

1个topic会有许多不同的立场，作为作者来说，
分析某个立场并不代表你在支持这个立场，
所以你客观分析，哪些是正面观点，哪些是反面观点。

之后你自己的立场基于这些立场而建立，
作者的结论不仅仅是赞同或反对，而是考虑到所有的立场。

在写论文时，除了考虑自己的立场外，也要考虑其他的立场，
尤其是考虑到反对自己的立场，
这样会显得你的论文很有深度，辩论性更强一些。

作者的立场会比只是赞同与反对更加的复杂，
因为有不同的角度、扩展、衡量用来使其变得更加清楚。

你的立场：总体来说你自己是怎么想的？
你是怎么看待这个topic的？
你的立场并不是你要分析的立场，
你的立场本质上是在回答你自己的问题，
但你要分析的立场是更general的立场，是文献中的立场。

不要提前下结论，这样可能会限制你的想法，
再多阅读一些文献，可能会有反转。

没有绝对的对错，只是立场不一样而已，
多去看其他人的论点，然后在最后下定论之前，
我们去修改、肯定、否定其他人的观点。

例如：
学生上网课，学校服务供应商不喜欢，因为损失钱，
国际学生喜欢，因为省钱。

分析、评估、权衡每一个立场，
之后再基于证据，再决定哪个是最有价值的，
再变成自己的观点。

紧紧贴着不同立场之间的争议性去走，
一旦你已经分析了不同的立场，
就要开始深入分析一个立场，
为了发展出你自己的argument，
你需要展示所有的立场，
为了展示为什么你依赖那个单独的立场。

把是否问题拆成it depends

注意不要简单当成一个[是/否]的问题来回答，
“it depends on…”，
似乎是对于一个[是/否]问题来说最大众化的回答，
答案并不非得是"是"或"否"，对于不同的情况，
可能会有不同的答案，例如：小公司很在意这些成本，
但大公司很多金，就不会特别在意这些成本。

所以要根据不同的情况来分析，
可以depends on很多不同的东西，
即使答案看起来只有"是"与"否"，
但也很可能会有更多的答案。

如果你觉得有更多多样化的范围，
你可以用"To what extent…"，
你可以说从多大程度上，
沉没成本谬论是一个谬论，答案可能会有很多。

[详细讨论/深入分析]1个立场，
尝试在不同的[观点/理解/逻辑]下去评估利弊，
讨论评估不同的立场，例如你的topic是管理相关，
那你可以先从managers下手，之后再去聚焦。

4. 支撑论点

supporting arguments(reasons, evidences, examples, etc.)

每个position，都要有不止1个的[原因/论点]去支撑。

例如，先找到自己的topic：
Should children learn second language in school?
小孩应该在学校中学第二语言吗？

从一堆文献中找到相关的立场：

Position A
小孩不应该在学校中学第二语言

Position B
小孩应该在学校中学第二语言，越早越好

Position C
小孩应该在13岁之后再在学校中学第二语言

下面我要去分析Position A了，这个A是我的position吗？
不是的，对于刚开始的每一种立场都要保持中立保持怀疑，
当我去分析A时，我可能还没有自己具体的想法。

从文献上看到的即将要分析的position A的相关论点：

问自己一些问题，之后逐渐形成了我自己的立场与想法：
What are the arguments for this position?
Why do people think this?
What’s the weakness of the argument?

A) children cannot decide what is the right language for them to learn. In time, they may become demotivated and give up if it is the wrong choice.

B) Adults learn second languages faster that children in certain learning environments.

我可能[同意/不同意]这2个论点，
我的任务就是找到evidences和reasons，
去找相关的研究和文献，决定这些论点是不是真的？
或者多大程度上它们是真的？多大程度上同意或不同意？
如果不同意，为什么？
能给出一些evidences和reasons来支撑你的观点吗？

就像B中说的，我不太同意，
因为it depends on the age, country, language, method of teaching，这些就是我将讨论的东西。

例子，分析关于Java和Python的不同立场：

立场	论点	原因、证据、结论
Java更适合初学者	（学习性）Java比C++简单，在很多大学都有Java课程	反面：1. 举例子，某大学把课程从其他语言换成了Java，但并没有什么效果，在大学中的Java课程多并不意味着Java最合适。2. Java比较难，要学很多复杂内容，举例子，main函数，复杂语法，值传递引用传递出bug。Java并不适合没有编程经验的人。（反驳）但学完Java后，更容易学其他的语言
	（流行性与应用场景）Java是目前最流行的语言	（正面）有大量的公司都在使用Java，Java的应用场景多，研发安卓，研发网站，后端从业人员等等，Java比Python流行，Java更适合后端
	（使用性）Java的运行速度更快，Java生态好	（正面）Java使用各种库，开发效率高，运行速度比Python快
Python更适合初学者	（学习性）Python的学习难度低，容易上手	正面：关键字少，语法简短简单像英文，更像人类思考方式，举例子，对比Java与Python的语法，简单基础概念，Python更适合没有编程经验的人，以及K12学生，可以锻炼小孩的动脑动手能力。研究表明，小范围学生都想要Python，大范围学校降低80%失学率，（反驳）虽然简单但可能不明白底层原理，不合适过渡到其他语言。
	（流行性与应用场景）Python的应用场景多，尤其是AI方向	（正面）AI是趋势，Python是AI的主语言，因为脚本语言容易写，AI是未来的主流，Python是未来的趋势，更适合AI从业人员，2个语言分别是各自领域的霸主，在选择语言时需要考虑应用场景
	（使用性）Python的开发效率高	（正面）Python的开发效率比Java更高，不仅库多，而且开发量少。Python的执行速度慢，因为是动态类型语言，但有库也挺快，Python是胶水语言，这一点Java比不了

1个论点不一定非得只有1段，可以有2-3个段，
看看自己有简短介绍argument吗？
有寻找evidence的段落去支撑argument吗？

再用一些反例evidence去反驳，如果把这些都放在1个段落中，
就会很臃肿，可以把它们拆成2个（1个支持，1个反对），
具体情况具体分析，如果每个段落很短，
你可以把它们整合成1个段落。

关于段落的长度，你可以把你写的论文给其他同学读，
如果他们迷失在段落中，那么你就需要把大段拆成2段，
以便给读者明确的导向。

润色与检查

英文论文全部写完之后，还需要不断地润色+检查，
至少把自己的论文读十遍，在不断的重复过程中，
把自己假装成读者，不断的问自己为什么。

• 检查字数，多退少补；
• 检查是否符合老师的要求；
• 检查主题段的主旨句是否恰当；
• 检查主题段中的解释与例子是否恰当；
• 检查文献引用是否恰当，不要为了引用而引用；
• 用翻译软件翻一下，看看是不是有的地方可能存在表达有歧义的情况；
• 语法检查；
• 查重，重复率小于30%；

论文结构

开头段大约是10-15%字，结尾段大约是10-15%，
主体段70-80%。

先写正文主体段，
再写开头段与结尾段，最后写摘要。

Not all papers have this structure.
Some might not have an experimental section:
a purely theoretical paper
a paper based on interviews etc. not practical work

Some papers will be more philosophical, idea-centric
But the idea that you are reading for a purpose remains

Title

Research Question比Title更重要，
用一个question去引导你，
去尝试回答1个问题会给你写作上的方向。

Research Question像是心脏，Title就像是衣服，
只是为了穿着好看，只是为了让读者想继续往下读。

标题一般不是完整的句子，一般也不是疑问句，
可以用一些名词短语。

RQ:
"Which programming language, Java or Python, is more suitable for students to learn?”

Title:
“Choosing programming languages for learners: Java or Python?”

疑问句标题：
用疑问句来做标题很棒，当你在写论文时，
你可以随时把标题换一种说法，
把疑问句变成陈述句，让含义更清晰，

刚开始的标题并不一定是最后的标题，
可以在写论文的时候随时改变论文标题，
刚开始把疑问句作为标题有助于帮你整理想法，
有助于找到更多的研究思路，以及聚焦你的想法。

修改后的标题应该是比较短小精悍的，
更聚焦，范围更小，使用陈述句，
一般论文的标题都不是疑问句，
在修改前的标题可以是比较长的疑问句。

一般来说，一般疑问句更容易有争议性。

A. Should more skyscrapers be constructed in the modern Chinese metropolis?
B. Could both companies and workers benefit from work time reduction?

A与B都有争议，但A更有争议，
因为B只有companies与workers这两个group，
只能从level层面上去考虑与讨论，
但A会有更多的group，从而会有更多的position。

C. Should big data be used in healthcare research?
D. To what extent is big data useful?

C比D要更适合做标题，因为1更聚焦，
更容易发现不同的group与position，
但是2太宽泛，有太多的不确定性，不太好写。

一般疑问句中的问题可能会有一些欺骗性，
在去查找一些文献资料之后，
可能会发现问题中的内容与自己起初简单想象的感觉完全不一样。

例如：减少工人的工作时间就对工人完全有利吗？
不一定，工作时间减少后，可能会导致工人的工作压力增大，
在更少的时间内完成更多的内容等等。

当你在思考你的topic与research area时，
不要给你的读者a wrong impression of your focus.

读者在读你的有争议的标题时，
读者需要有一个什么东西是要即将讨论的线索，不要欺骗读者。

假设你的标题是:
“more skyscrapers must be built in China”，
但在你做调查研究与分析立场时，发现了一些负面的立场，
例如：环境问题、安全问题等等，
在你的论文中也许你考虑到了这些问题，
但在这个标题中，并没有体现你考虑到了这些问题。

标题必须能反应出你研究的内容是什么，可以把标题改成：
“Critical consideration into building more skyscrapers.”

critical consideration可以让读者知道你已经考虑到了很多方面。

如果找不到太多的立场怎么办？
可以把这个问题问问身边的朋友同学等，
看看从他们的角度出发，他们的看法是怎样的。

未完待续

Acknowledgements

Thank anyone who has helped you in any way.

Abstract

摘要里不要写引用，摘要是最后写的，
用不用keywords需要看是否有用到学术词汇。

By the time you have read the abstract, you should have an idea of the overall contributions of the paper. But sometimes it is better just to dive into the introduction of the paper if the abstract is too dense.

An abstract (200-500 words) that summarises what the project is about, what it has achieved, and how it contributes to the development of research and/or technology in the area. This should also include 5 to 10 keywords you think would help someone trying to find your dissertation (e.g., in a web search). Please be careful to enter specific keywords relevant to your dissertation, don’t be too general. We recommend that you include full names and acronyms where appropriate, and separate key word with semi-colons e.g. “Keywords: Human Computer Interaction (HCI); Internet of Things (IoT); autonomous vehicles; user study; qualitative study”.

未完待续

Contents

目录

A table of contents (perhaps also a table of diagrams or tables). Provide page numbers for all major section headings; include page numbers throughout the dissertation. You should be able to automatically generate this using appropriate features of Word or LaTeX.

Introduction

开头段

PAP：
当你知道如何总结争议性问题，你就可以develop你的分析的想法。
在你的第一个简短争议性问题的总结中，
你打算分析的立场必须明确，
你需要在开头段中给出一个线索，你已经逻辑上和依赖证据，
你已经知道，基本上你将分析的立场是什么。

A description of the problem being studied.

Not just “what is this paper about? but what bigger question is it contributing to?

An introduction in which you set out your objectives and briefly introduce the [contents/structure] of the following chapters to give the reader an overview of what to expect.

By the end of the introduction,
you should have an understanding of two things:
What the contributions of the paper are?
What the structure of the paper is?

在Introduction中也需要简单描述一下需要在文献综述中提到的内容。

不然直接在文献综述中提到一些新概念会显得有些突兀。

1. introducing the reader to the topic

Introducing the reader to the topic of the paper and its importance.

Providing relevant background information on the topic of the paper (e.g. definitions)

基本上在开头段，你需要解释要讨论的是什么topic，
争议的问题是什么，
为什么这个那么重要，点出主要的position。

然后再去解释你在关注的是哪个position，
你将关注更多有深度的细节，
你也许会找到更多的arguments，evidence等。
不要在开头段中展开supporting argument，
在主体段中去分析position，
position是必要的，
这意味着去看看某个人说了什么，
看看他的关于问题的态度，看看他的想法。

在开头段总结你的controversial issue与你想分析的立场。

开头段更像是给出context（上下文&环境），
你要解释要争论的问题是什么。

在开头段，你需要提及各种各样的立场。

2. stating the purpose of the paper

purpose statements

The purpose statement should make it clear what your research question is.

I would recommend that you convert your research question into an indirect question.

Keep it as close to the original RQ as possible. See some examples below:

Some points to consider when writing your purpose statement:

1. Signposting;
2. Choice of verb;
3. Changing the word order;
4. Using “whether” when turning a yes/no question into an indirect question;

Research question (direct)	Purpose statement (indirect)
To what extent are teachers responsible for their students’ learning outcomes?	[1.The purpose of this essay is to] [2.discuss] to what extent [3.teachers are] responsible for their students’ learning outcomes.
Are teachers responsible for their students’ learning outcomes? (yes/no question)	[1.The purpose of this essay is to] [2.discuss] [4.whether] [3.teachers are] responsible for their students’ learning outcomes.
How effective are personalised search engines in terms of privacy and performance?	[1.The aim of this paper is to] [2.evaluate] how effective [3.personalised search engines are] in terms of privacy and performance.
To what extent is manual therapy more effective than steroid injections for treating adhesive capsulitis?	[1.This paper] [2.will investigate] to what extent [3.manual therapy is] more effective than steroid injections for treating adhesive capsulitis.
Is Ali an idiot? (yes/no question)	[1.This paper aims to] [2.determine] [4.whether] [3.Ali is] an idiot.

3. stating the writer’s position

position statement在开头段，是关于你的position，
并不是去说其他人的positions，
不要用some [researchers/people]，一定要明确，
到底是谁，不然你可能只是瞎编的，
或者你可以不说some researchers，
只说one position is that …，
也不要用some，some太模糊了。。。
在学术写作中，需要比较精准。

如果先写some researchers，在后面才有名字的话，
那需要citation，
你可以用一个带研究者与出版物名字的非完整引用。

例如：“在这一点上有很多的证据”，
之后用1个非完整引用，列出相关证据。
You could use a non-integral citation with the names of the researchers and the publications)

A. This paper suggests even more farmland diversification should occur.
B. Our paper provides evidence from three different contexts in support of a decrease in university fees.
C. The paper proposes an immediate implementation of the dual navigational system.
D. This paper demonstrates a high incidence in the occurrence of truancy amongst children from lower income families.
E. The evidence provided in this study refutes Fisher’s well established argument on the way that new vocabulary is stored in the brain.
F. This discussion is concerned with the newly introduced law on pensions (45/2011) which, I would argue, is inconsistent with the government’s stated aims.
G. This paper advocates the elimination of legal grey holes in UK immigration law and cautions against increased use of the European Court of Justice.
H. This analysis will show that, far from making rational choices, customers purchase decisions are often based on emotional feeling.
I. It will be argued that nuclear power may well be economically advantageous, but that its huge environmental problems make it a very questionable energy source for the future.
J. I will argue strongly that ecotourism, despite its apparent success, still fails to deliver solid economic and social benefits to local people.

4. outlining the structure of the paper

• 概述论文背景，简短地概述在全篇中你要讨论的且具有争议性的问题是什么，并表明你[将要/想要]分析的立场是什么（不只是1个立场，不用在开头段直接给出自己的立场，而是把核心的要讨论的立场摊开来说一下，在主体段中将要为这些立场去给出详细内容、去解释、去分析、去评估）；

• 用一些短小精悍的句子来概括一下你将用到的arguments，
  这将会为主体段中的key supporting arguments（为支撑position而做的详细解释）提供线索；

• 在开头段中当你想介绍你的topic时，你需要去解释你的topic，
  在某种程度上将反映出你自己的想法，但不要完全依赖你自己的想法，一定要基于从不同文献中得到的不同立场。
  不可能所有的论点都是你自己的想法，在开头段中也许有1-2个你自己的想法，之后再逐渐发展成1个被不同文献支持的argument；

Literature Review

文献综述，并不是所有论文都需要这个章节，
对于毕业论文，和正式的报告来说，文献综述比较重要。

文献综述是论文的背景，前辈们的文献为我们毕设提供了背景，
我们，作为学术垃圾制造者，
是要站在前辈们的肩膀上的，
前辈们的肩膀就是我们自己要写的论文的基础与背景，
文献综述应该是写论文过程中最先开始写的部分，
在你真正开始动笔写论文前，
你肯定要去搜一些跟自己要写的内容所相关的学术论文或资料，
看看在自己想写的这个[研究方向/课题]里，
前辈们已经做了哪些研究？
看看前辈们的研究成果是什么？结论是什么？
相关的研究目前处于什么进度？什么状态？有什么趋势？
前辈们有哪些不足是我们有能力去[弥补/创新/改进]的？
看看哪些文献可以被引用过来为我自己的论文所用，
文献综述就是聊那堆文献的（文献综述中的内容也需要引用文献），
为什么你要引用这些文献？它们给你的论文贡献了什么？

文献综述的作用：

• learn about theories and established understanding of a specific topic （了解相关话题研究）；
• act as a basis for hypotheses development（假设提出的基础）适用于定量研究；
• act as source of interview design；

you should be trying to get from the paper material that you need for your project

Good idea to write a summary after you have read the paper

Containing:
The reference details: authors, where/when published
What the key ideas/algorithms/experiments/results are
Perhaps in a structured form
The relevance of the paper to your work
Which other papers this connects to

It is useful to have a systematic way of referring to other papers; for example, I give each one a code based on the author’s name(s) and the year of the paper
Then you can just write e.g. “Akk18 – uses the same ideas but with deep learning; Ber12 – applies this algorithm to text data”

Paper as Structural Exemplar
Another reason to read a paper is to use it as a “structural exemplar”
By this, I mean that you use the structure of the paper as the model for how you structure your work
Think about a paper that has been particularly easy to read.
perhaps despite it being on a complex topic
How has the paper been structured? How does it introduce its ideas? How are the experiments structured and analysed?
…then take the “skeleton” of that as inspiration for the structure of your own work.

A literature and technology review, leading up to the problem that is tackled—you should show a knowledge and awareness of the relevant literature and/or technologies. You shouldn’t just give a descriptive list of other work: you should organise the work that you review in an appropriate scheme, show connections and contrasts between the work, point out the strengths and weaknesses of existing work, and where appropriate clearly identify what the current state-of-the-art approaches are to the problem.

A discussion of possible approaches to the problem and the reasons for the approach chosen.

A description of the work undertaken in investigating the problem.

怎么写

先对相关文献进行分析与整理，使其具有逻辑层次感，
再对这堆文献进行系统的比较和评论，
我们可以由4个原因（不止这4个）进行展开，
Think about why papers have been mentioned in the literature review:

• Do they introduce the problem tackled in the paper?
• Describe previous attempts to tackle it?
• Describe [techniques/methods] used in the paper?
• Provide datasets that are used in the paper?

They might introduce the problem tackled in the paper, so the literature review might say: “The problem studied in this paper was first introduced by Lauren, citation to a paper, and they’ll give someone’s name and the paper introduced and in this paper we're going to extend it by doing something else. So the reason for making that citation is to say what the problem is and where you can go to read more details about the problem and where it comes from.”

他们可能会介绍论文中解决的问题，
先介绍文献，作者是谁？之后我们要通过做一些事情来扩展这个文献。
引用文献的原因是，说明研究问题是什么，
关于问题更多的细节从哪里可以读到？问题从哪里来。

Some citations might say "here’s s previous attempt to tackle this [paper/problem] used decision trees or use neural network or whatever, so there might be a number of things which say here’s the problem we’re trying to tackle, here are four or five approaches that have been tried in the past and now we're going to do something differently, learn from these... I'm going to do it in [this/different] way."
例如：“以前曾经使用决策树或神经网络来解决问题，很能有很多个：我们正在尝试解决这个问题，已经有4-5种方法在过去已经尝试，从之前的经验中学习，现在我们要做一些不同的尝试。”

In my describe, techniques or methods that are used in the paper, so you might have a section that the literature review says: “we’re reviewing this paper, we described this paper because ... our technique generalizes from this one, or technique used this one's component or the overall method of this paper is similar to this other paper. So it links the methods and techniques used in the paper to that previously published piece of work. ”

literature review: “我们正在评论这片论文，我们描述这片论文，因为我们的方法从这片论文中归纳或者这片论文中的一个或全部方法，跟其他论文很相似。所以它将论文中使用的方法和技术与之前发表的工作联系起来。”

Or it might provide a dataset that’s used in the paper, it might say that we consider 10 possible datasets for analysis in this paper. Here are the references to them, here’s what they contain and we choose these three dataets for these reasons, so you say, it's about saying where the data that you're analyzing in your paper comes from.

或者它可能提供一个在论文中使用过的数据集，它可能说我们考虑了10个用于在这篇论文中分析的数据集。这是对它们的引用，这是它们包含的内容，我们选择了这3个数据集因为什么什么，所以你可以说这是关于说明你在论文中分析的数据来自哪里。。。

A good literature review will have a structure (which might be as useful to you as the details) and will analyse the topic through looking at the papers, not just summarise them one-by-one. not just a summary, not just here’s this paper is about that, here’s this paper is about that, here’s this paper is about that. You will say there’s a good paper that do this, this does it in this way, this does it in this way, this paper trying to combine those two methods but failed, this paper successfully combined them and then this one extended it. So there’s some structure it [imposes/inputs] some structure on this collection of papers that it’s talking about.
通过阅读一些论文来分析主题，不只是概述，不只是这篇论文关于什么，而是：这是一篇好的论文，因为它做了什么什么用了什么什么方法，A论文尝试结合2种方法，但最后失败了。B篇论文成功的结合了它们并进行了拓展。可以是在机械论文上的结构的加强。

In particular, good structure if you’re writing a literature review is to think about you talk about the [general/broad] topics first, and then you go closer and closer to the thing that you’re writting about and that’s common thing you’ll find in.
先广泛再深入

But you’re reading these literature review, they’ll say that’s a little about the whole context and then say a lot about the papers (the papers that reference in the end of paper) that are very related to the work in the current paper.
少说背景，多说与当前文章相关的引用的文献。

Description of [techniques/methods] used
Where does the description of existing methods end and the description of original methods start? This is not always obvious!

Often, the description in a paper is fairly terse—a paper is not a textbook, but a reminder of a technique for an experienced reader, and a confirmation of notation and terminology used.

Core original work: Ideas/concepts/algorithms/proofs

Think about alternatives: Why this approach? Why is the data represented in this way? How else could this statement be proven? How widely can the idea be applied?

Link this to your own work: Can you [use/adapt/improve] this?

“virtual re-implementation”

Ideally, a paper will have enough detail about the methods used so that you can run the experiments, implement the algorithms, and extend the work. But, sometimes you have to look hard to find details, particularly for parameter settings etc. And sometimes, details are missing!

Literature review isn’t just choosing 15 papers and writting a paragraph about each conclusion. You should both have some thematic structure to that.

例如：
Here’s the question I’m trying to answer so far, people have taken 4 approaches to it. They’ve taken a measurement approach in machine learning approach, a catastrophic approach, and conventional approach or something else. There are given names to all these different approaches, and then write about each of those 4 things and say within a measurement approach they’ve done this … and within a learning approach, people have done this …, and then summarize at the end and particularly summarize with regards to your particular project, your particular question.

PHD students size to do of course about introducing how to work on your PhD is at the point of the literature review is to leave a thesis shaped hole in the lierature.

You link in various different reasons, you might cite something or put in literature review, you might want to compare your work to it, you might want to build your work on it, you might want to point out something they didn’t do that you’re doing, so there’s various different reasons why you want to look at those and when you take the words different perspectives on it. You gotta a whole gap that needs to be filled he made the argument as to why this thing is important.

概述：
文献作者，发表时间，文献出处等。

主体：
1.Background 背景
2.Problem/hypothesis 问题/假设
3.Solution/Argumentation 解决方案/论证
4.Experimental test/Conclusion 实验/结论
每个点可以只用一句话来概括，只需要把Literature Review的主线理清楚就行，
不必叙述每个细节。

分析评论：

1. 意义层面，需要说明某篇文献对你的研究有什么意义。研究方法是否新颖，解决的问题是否有意义，所用的实验步骤、实验对象是否合适，结论是否正确。
2. 文法层面，如论述的组织是否合理，论证的过程有没有漏洞，文学作品的段落结构有没有问题等等。
3. 语法层面，句子是否通顺，词语是否恰当，有没有更好的表达方式等等。

三层的重要性是递减的，最上面的那一层是最重要的。

Not just saying “Has the work in this paper been done before?” —instead, setting the broadly context for the work.

注意事项：

• 千万不能只是像记流水账那样机械式地简单地罗列出一个个文献的相关内容介绍，例如：“A研究表明。。。，A认为。。。”，一定要有自己的东西，自己的评论、总结等等，用来突出你的研究的意义；
• 文献综述要做到critical，可以考虑这样几个问题，前人研究的角度是什么？得出哪些结论？有没有就某一方面跟你达成共识？为什么没有？前人研究意义何在？对我们自己的论文有什么指导意义或借鉴价值？他们的不足在哪里？

如果Literature Review是单独的一篇文章，
那么需要有结构：

• Introduction，研究的原因、意义、范围等等；
• Body，该研究的现状，所采用的研究方法，已经解决的问题和未解决的，研究重点，研究方向等；
• Conclusion，结论，总结，自己的意见和需要解决的问题；

常见表达方式

注意查重，因为可能其他人也是用的例句，造成抄袭风险。

General description:

• A large and growing body of literature has investigated …
• More recent attention has focused on the provision of …
• Much of the current literature on X pays particular attention to …
• Over the past decade, most research in X has emphasized the use of …
• There is a large volume of published studies describing the role of …
• In recent years, there has been an increasing amount of literature on …
• During the past 30 years, much more information has become available on …
• A considerable amount of literature has been published on X. These studies …
• The first serious discussions and analyses of X emerged during the 1970s with …

Reference to single investigation in the past:
(refer to 过去的单个研究)

• XX found that as levels of literacy and education of the population rise …
• XX showed that reducing X to … (see Figure 2) .
• XX demonstrated that when the maximum temperature is exceeded …
• XX investigated the differential impact of formal and non-formal education on …
• XX analysed the data from 72 countries and concluded that …
• XX reviewed the literature from the period and found little evidence for this …
• XX interviewed 250 undergraduate students using semi-structured …
• XX studied the effects of X on unprotected nerve cells.

一些例子

• Literature surrounding analysis of drug reviews can be divided into two main studies. One concerning ADR and another concerning sentiment [2]. We look at the latter in this paper, but both studies have been approached by lexicon and ML methods. ML is becoming more common due to its advantages over lexicon based methods. The most important being that it is unaffected by mis-spelling of words / phrases.
  The paper from which we are using the same data, [3], built a logistic regression model to classify overall sentiment and sentiment on side-effects. Focusing mainly on the performance of the model on cross-data (train on one site test on another) and cross-domain (train on one side effect test on another). We focus our efforts on comparing different ML algorithms perfor- mance; [5] does a comparison of two different ML algorithms: neural network and support vector machine (SVM). Two types of neural networks are investigated, probabilistic neural network and radial basis function (RBF) neural network. These two are compared against a SVM baseline. Their findings concluded that the RBF neural network performed best. One paper that did use a big data approach like us was [6]. This paper built an SVM and collected 2 billion tweets to analyse for ADR. MapReduce and compute instances (EC2) offered by Amazon were used to process the large amount of data. Performance was limited due to noise from the social media platform but demonstrates the scalability of our solution.
  There is a clear literature gap in an approach that combines both big data and a comprehensive analysis of different ML methods to perform sentiment analysis on drug reviews. We set out to achieve this in this paper.

Main Body

主体段可以包含：

• Experiments, results and discussion, have the experiments been described in sufficient detail for you to re-implement or extend them? Are datasets, hyper-parameters, sampling methods, populations of participants, given in detail? Is code, data publicly available? What (if any!) statistical techniques have been used to ensure the robustness of the conclusions? Sample sizes, error bars, significance tests;

• Results, testing, verification— if the work involved the construction of a piece of software, then the examiners will want to be convinced the program actually works and has been built according to good software engineering principles. If the work involved something other than this, the evaluation and verification will take a different form, but still need to be presented and your work needs to be discussed in the context of relevant principles. Make sure that you present your results effectively and discuss their significance;

• For several improvements, not just compare the current one with the new version. Do each small improvement as a separate line on the graph. e.g., one line with improvement, one another line with improvement two, another line with improvement 3, and then one with all of the improvements together, which means to show more thoroughly.

1. More literature review with clearer comparison using table to show different related works, and then compared by some categories, which means more systematic. To show not just talk about what other people have done but how you fit into the area as well (it is important to highlight how your work fits into the current gaps in knowledge), which would be important when you compare some other people’s approaches in your experiments when you compare some other technologies.
2. Justify any values and parameters that were picked. How realistic are the parameters? Are there any papers you can cite which support this?;
3. Add something into your graphs, if you’re showing average value, use error bars, statistical methods, or something that you can show the range of values, so if you run the experiments 5 times, and then plot the maximum, the minimum, and average result.
4. There is a lot of literature which could be built upon in the context of the current work. It might be a good idea to think about the specific contributions of your research in order to help with this.

The plots are very useful but would be good to include the ‘error bars’ with either max/min or +/- 1 standard deviation. Max/min is sufficient! This also allows you to talk about how good your approach is compared to others in more detail. E.g. the average might be better but if the max is worse then maybe there are cases where their approach was better for extreme cases.

It is a good idea to explore the data in these ways without necessarily generating new data from experiments.

1. Topic Sentence

主旨句是[主体/议论]段落第一句，快速指出本段的主题是什么，
主要想法是什么，主旨句不要是小短句，尽量是长句，
尽量在句子的前半部分描述出主题。

一般来说（不是100%，不是always，具体情况具体分析），
段落中必须有很强的topic sentence，段落中的第一句，
需要清晰地告诉读者，这一段在讲什么，
如果没有的话，会让读者蒙圈。

一般来说，主旨句是个长句子，尝试写长句，
在句子中的前半段表明topic，这样更容易识别。

the topic sentence is formed according to the main idea in a specific paragraph.

2. Explanation/Additional info

Reason/Explanation/Further Information
之后是去支持与评估主旨句中的想法，
加以讨论，引入文献，讨论反向观点，再反驳再回到主观点上等等。
批判性思维不是让你去批评某个东西：“哇，这个写的真垃圾，像狗屎一样”。
而是去评估，去思考我是怎么想的？
我如何去支撑我自己的想法？
有argument，counter-argument，反驳，用文献来支撑论点，
整个这个过程是批判性的体现。

确保引用的文献是与争议性有关联的，
不要去添加一些把你带跑偏的文献。

3. Examples, evidence, support

表达Claim观点，followed by evidence, examples, and support，在有必要的地方加入critical comment。

刚开始解释某个专业概念时，可以用1个简单的类比，
但是，需要用更多适合的例子去支撑你的argument。

How to describe the examples from sources, just like a result of an experiment, should I use “the most of xxx” or “50% of xxx”.

examples可以是任何形式的数据，数据不只是数字，
也可能是照片、插图、访谈、对话。

你可以用不同形式来展示数据，如果在文献中写的是”50%”，
你可以写成”half of xxx”，如果是”18%”，
则可以写成”less than 20%”，用自己的话来写。

此外，自己写的evidence要与reasons离得近一些，
You can use pronouns to indicate that you are still referring to the aforementioned citation/study。

Explain in detail the supporting argument(s) for the position you have chosen.

Organise your text in paragraphs to help identify reasons/arguments for the position you are analysing.

主体段主要是详细描述你在开头段提及的supporting arguments，
一步步地把读者带进你的arguments细节中，
这些supporting arguments用来支撑position，
你需要在段落中组织好你的文字；

你的argument，类似于你的想法的延伸，需要是可以逻辑呈现的，
可以说服读者，你可以随时调整你的想法，
可以借助自己之前的思维导图，
然后用一种非常有逻辑性说服性的方式表达出来；
你可以用不止1个的文献资料，
去尝试和帮助你的读者去get到你所选的position是强还是弱。

只要有开头段与结尾段，一般主体段需要分几段，
主要依赖于你有多少东西打算描述，有多少个arguments，
分多少段需要你自己去拿捏，无论怎么选择，都要有自信。

组织你的文字，把它们放进小的topic中，
你可能会有1个大的section，
我们要把内容打碎揉进段落中，这样可以更容易的follow，
“to help identify reasons/arguments”，
是帮助读者识别出原因/论点，如果是1个清晰的段落，
这里是argument，这是里counter-argument，
他们都分散在段落中，更容易让读者follow。

Appropriate Sources

Select appropriate sources, use appropriate in-text references and list these at the end using XXX Guide.

我们需要改写与总结文献中的想法，选择合适的文献，
选择那些可以让你的观点更聚焦的文献，
需要按照XXX指南中的格式列在论文中引用以及在论文的最底部形成reference list。

我们可以做一个表格，然后把一些东西放进去，
当你在思考不同文献里的内容时，
这个表格中可以展示出对于相同的1个topic所延展出的不同的想法，假如文献A中的讨论的想法与文献B很像，
尝试给每个文献做笔记，这样你就可以把他们俩的想法综合一下，
这样你可以更清晰的管理文献中的内容，
从而更好的支持你的arguments与counter arguments；

如何找文献

我们只能使用英文版的文献资料，因为是写英语论文，
可以去找一些中国作者发布的英文文献，
不一定非得是西方作者。

有一个快捷找文献的好方法，
就是在看完一篇文献时，看他引用的文献，可以顺藤摸瓜。

除了Google Scholar以及学校自己的学术论文搜索引擎外，
我们还可以用下面的资源：

Academic Blogs	http://patthomson.wordpress.com/ (Education and Social Science Blog)，http://teachingeap.wordpress.com/ (EAP blog)
Online Lectures	iTunes U，YouTube，University websites
Open Access	http://www.academia.edu/ (where you can find published and unpublished work by students and academics in your field)
Newspaper Commentaries	http://www.theguardian.com/commentisfree/page/allsubjectsaf，http://www.bbc.co.uk/science/0/，http://www.thetimes.co.uk/tto/opinion/，http://www.independent.co.uk/voices/debate/

• https://doaj.org/ [multidisciplinary]
• https://www.mdpi.com/ [multidisciplinary]
• https://core.ac.uk/ [multidisciplinary]
• https://www.scienceopen.com/ [science]
• https://eric.ed.gov/ [multidisciplinary]
• https://www.econbiz.de/ [business and economics]

文献是否可用

在阅读不同的文献时，
需要评估与判断我找的文献是否能用？
如何判断呢？有哪些不同的方法可以考虑？

先看title和abstract，决定是否往下继续读，
是？否？也许？
如果是也许的话就先标记一下，等有时间再来读。

上面都没问题后，再看introduction与conclusion，
就可以快速掌握文章大意，如果不太符合就直接pass掉，
不要浪费时间，还可以用一些标准来判断。

• 【权威性/可靠性】
  看作者是不是学术界、大学中的专家？
  作者是博士学位吗？是否发表了许多学术文章？
  是这个领域的专业人士吗？
  作者有这个topic相关的足够的学术背景吗？
  深入了解这个topic吗？（如果是物理教授写金融论文就有问题了）他说的话有信服度吗？可靠吗？不是作者瞎编的吧
  可以选择任何来源，但你要决定是否可靠，
  如果是WHO的网站，可以。不要是中文网站，
  但可以是英文版的中文网站。

• 【准确性】
  文献内容是否准确，是否有引用，引用的文献恰当吗？准确吗？
  语法准确吗？词汇准确吗？有很多错误吗？

• 【覆盖度】
  作者只关注很小的一部分？还是可以覆盖到很多的的点？
  研究范围大吗？例如只有47人，那这个覆盖度是不够的。

• 【客观性】
  作者是为学校工作？还是为公司工作？
  是要为了卖公司产品吗？如果是的话就不可能客观，
  文章是否发布在知名可靠的期刊上？如果是的话，那就足够客观。
  但也分情况，如果是写一片反思性的论文，
  想想哪里做的好哪里做的不好
  那么就是主观的，因为都是关于你个人的内容。
  论文中的内容，不能只是个人主观想法，
  还需要伴随着引用文献中客观的数据与分析来支撑这个想法。

• 【流行性】
  文章写了多久，是近代的啊？文内的引用是近代的吗？
  如果是1965年写的，就是currency很差，
  如果是2018年就是currency很好，
  一般情况下，离当前时间越近的学术文章越好。
  但也分情况，如果20年前写的规则，现在还在使用，
  那就可以把这个文献引用过来。

Paraphrase and Summarise

Paraphrase/summarise the ideas of others appropriately in your own words.
把文献中的内容，适当改写成自己的话，引用到论文中。

当你试图在讨论中论证观点时，你需要引用不同的文献，
可用使用"改写"与"总结"这两项技能。

当从文献中拿到其他作者的结论时，必须要解释原因，
读者需要知道为什么他们有这个想法。

critically comment and evaluate

Critically comment and evaluate on the reasons/arguments given for the position you are analysing by comparing/contrasting ideas from different sources (synthesis) and/or introducing counter-arguments.

在主体段中，我们尝试去展示不同人们的想法，
他们同意吗？他们反对吗？
先寻找文献，之后进行compare and contrast，
但是你也要考虑这些信息是否清晰，文献来源是否可靠？
举例子，文献中的作者只关注了中国的1个年龄组，
并没有聚焦到其他国家，这就是你的分析，
你需要批判性阅读你找的文献材料然后去思考，
这个文献材料好吗？文献中展示的内容是你真正需要的吗？
论点强势吗？如果答案是yes，
那么你就可以用这些比较强的evidence来支持你要表达的观点。
如果不强，你依然可以用，但你可以去批判它，
你可以说他这个argument是弱的，因为什么什么什么。
需要去批判argument的弱项，我们需要阅读不同的文献，
用文献中的信息去蜕变出属于我们自己的立场，
与此同时，我们还要去展示我们已经分析了argument，
在这个过程中，我们顺带着就搞定了argument的弱项部分，
但并不是所有的argument都有弱项，当你找到弱项时，
确保你将这个弱项与你的argument关联在一起，
在批判写作中，弱项一定要进入到你的argument的流程中去。
我们的论文需要最少使用n篇文献，当在使用这些文献时，
尝试将文献中的立场关联到你自己的立场与你自己的argument，
之后尝试进行评估，你不仅仅要分析弱项，也要分析强项。
你首先需要展示1个从文献中获得的argument的结论，
之后尝试去批判性地评估和评论，
你不仅仅只是展示这个argument的强与弱，你也要给出原因，
之后对这些原因和arguments进行批判性地评论和评估，
你要通过compare and contrast去分析，这非常重要。
假如你正在分析1个position，你需要看一下evidence，
你是在看一个某个人的立场？还是在看一个general的立场？
你需要去看可以支撑这些立场的所有调查研究。
你不能只是叙事性的告诉读者，这些研究是什么，结果是什么，
叙述性并不会把你带进入1个立场分析，
在topic内部，一定要有一些东西是冲突的，
一定要有更多的争议，更多反对的声音。

你还必须要有批判性，为什么这个研究是对的，
为什么我需要相信这个研究，所以无论什么时候，
你在读一篇研究调查时，你都需要寻找潜在的弱项，
在查阅学习&研究资料时，你都要去观察与讨论强弱关系。
先去分析，评估，找argument中的弱项，再去做对比。
假如：在一项关于教育的研究中，每个班里只有20人，
作者找到了1个教学方法很棒的证据，试验结果非常好，
OK，等一下，班里有多少人来着？20个，人数太少了吧这样，
在中国一个班可能会有60人左右，
那在这里我们就可以做一个critical comment，
在分析完原因和argument后，你可以说：“（这个argument不是很强，）实际上，这个研究在英国非常成功，但是，在中国的教育环境下就未必会成功，因为人数差异”，之后你再说明相关情况。
critical comment需要有你自己的主观想法，
不能只是文献中的观点和结论，
加入你自己的思想，结合arguments与position中，
关键是讨论，你需要讨论你的想法。
再举个栗子，如果我在教学生，我会倾向于把学生分到不同的组，
每天我会把大家打乱再分到不同的组去，
那我能在60人的班里这样做吗？
可能不会，但如果班里只有12个人呢？也许就可以了。
我们思考这个研究适用于所有地方吗？是具有普遍性的吗？
所以需要寻找弱项，如果你有关于中国的topic，
你可能会去看全世界各个地方的evidence，
然后再问问自己，这堆evidence都适用于中国吗，
如果不行，给出理由，为什么？
comparing/contrasting：
有了自己比较认可的reasons或arguments后，
还要去看看它们是否可靠，是否论点不够充分，
是否太强硬太明确，是否能找到一些相反的对立的想法。

point by point comparison

Topic：哪种编程语言更适合新手学习？
文献中是咋说Java和Python的？
需要具体的答案而不是more suitable。
矛盾点：如果Java那么好，那为什么还有矛盾？
Python肯定有很多比Java更好的地方，需要用"横向对比"。

先把要对比的东西列出来，不要1段是Java的优缺点，
1段是Python的优缺点，这样并不会有帮助。

不要Java写一段Python写一段，你需要对比它们之间特定的元素，

用2个手机举例子，我要对比电池寿命，它们俩都很惊艳，
这个新手机的电量更持久充电更快，
但是，研究表明，1年后电池的续航能力下降的非常快。
你不只是描述一些东西，你不能只是说”A比B好，B比A差“，
你需要引入一些研究，可以引入2个研究，
例如1个研究是“这个人的研究是他去看了2个手机的电池，
然后他发现了xxx”，
另一个人的研究是另个一个手机的讨论电池平均寿命是xxx，
之后去做对比，基于这些证据，看起某个手机的电池更好，however，我们还需要考虑到充电类型。

Let’s look at the battery life of these two, They are both very impressive, and new phone as a battery life of xxx, it takes this long to charge, however, studies have shown that the battery life of success dropped dramatically after one year, whereas the iPhone SE the battery life stays consistent for part 2 years before the battery needs to replace, and you might go into detail looking at that one specific aspect of these two phones.

同样可以用于Java，可以关注一下易用性等等，需要更多的详情，需要借鉴文献，
不要是自己想当然，之后再critical comment。

知道自己需要比较什么？安全性？兼容性？
Identify what points you want to compare between the languages you gave identified and structure it that way.

例如：学Java需要多久学Python需要多久？学习时间只是其中的1个维度，
再比如，在未来，学成之后能获得什么？为学习其他更复杂的语言打基础？还是直接工作？
Java程序员能赚多少钱，Python能赚多少钱？

尝试去找一些使用编程语言的研究，并不只是在介绍编程语言的文献，例如，用起来有多简单，用户的想法是什么？不仅仅只是数量上的例子，也可以是跟学生的访谈，有谈到学生吗？有谈到小孩吗？我们的目标群体是谁？
你不只是描述一些东西，你不能只是说”A比B好，B比A差“，你需要引入一些研究，可以引入2个研究，例如1个研究是“这个人的研究是他去看了2个手机的电池，然后他发现了xxx”，另一个人的研究是另个一个手机的讨论电池平均寿命是xxx，之后去做对比，基于这些证据，看起某个手机的电池更好，however，我们还需要考虑到充电类型。
先把要对比的东西列出来，不要1段是Java的优缺点，1段是Python的优缺点，这样的话，那2段都会出现电池，这样被分割的信息就会很奇怪，这个就是point by point横向对比。
这些要对比的点都需要找证据来支撑，网上有很多这种研究，你可能会发现一些评估用户意见的研究，也许是老师的想法 。
有些人的编程语言不是被教的，而是自学的，添加更多关于学习Java和Python的文献。
如何深入？

你不能只是说A比B的电池好，而是要讲述更多关于电池的内容，
讲述你是怎么知道的，有什么文献研究支撑吗？有人反对吗？
并不是所有的研究都会有相同的结果，
可能研究A说行，研究B说一般，研究C说不行，
那你需要把这3个研究synthesize一下，放到你的论文中。
文献引用的多没用，需要有深入的去讨论一下文献，
不断的问自己why？

assumption

当想找evidence，找不到相关的文献来举例子时怎么办，
我能只说我自己的理解吗？

你不能用你自己的经历去代替真实的evidence，这样会丢分，
但你可以去做假设：
It could be argued that…
One could argue that…
This could possibly be explained by…

你可以用这类短语去支撑1个假设，
即使到目前为止还没有论证还没有结论还没有evidence，
但也可以去做假设，
你可以指出某个论点中的不足之处（如果有的话），
然后去做假设，形成你自己的counter-argument。

strengths and weaknesses

Analyse and criticise weaknesses in arguments.

在描述事实时，没有强弱之分，
只有在讨论的场景下，才有强弱。

1. 可能是参与研究的人数太少了，规模太小了，没有很强的说服性；
2. 假设我在学教育学，我在看一篇英国的研究，但我的topic是关于中国和日本，中西方在教育系统上是有差异的，这是没有可比性的；

strength of argument：
有说服力的argument等等

strong evidence：
有一些比较好的研究所支撑

Mention the scope of research covered in the source used, mention specific useful evidence, mention the arguments that you used from the source, mention how argument from source A was supported by (what evidence) from source B

weak evidence：
没有比较好的研究所支撑，也许是覆盖率不够，
可靠吗？是专家写的吗？还是只是自己脑子里的主观想法？

the issues not discussed, not considered, specify, ethical, environmental, financial, realistic aspects

general academic audience

Show awareness of writing for a general academic audience.

如果你的读者只是一名普通的学术读者，
他们很可能对你的专业不是很了解，
有一些专业术语你需要解释清楚，
之后再举几个例子或给出一些适当的定义。

举例子会更直观一些，让读者更容易懂，让你的argument更强，
例子中可以包含一些名称例如：Mendeley。

简化复杂的概念，
写完作业后让一些跟你不是一个专业的同学读一下，
看看他们能不能读懂，如果他们都读不懂，那老师也读不懂，
确保老师能读的懂，不要用一些专业词汇，
例如”膝盖”不要用patella（很少有人能看懂），
用knee cap大家都知道，
金融术语sunk cost沉没成本也有很多人不知道，
这种术语是需要进行解释或举例子的。

Conclusion

结尾段，既然都到了总结了，前面肯定很多信息都提及了，
因此不用再加引用了。

introduction与conclusion有相似之处，
那就是设置目标，指明方向。
introduction是用来向前看的，给读者方向，
例如：“这篇论文讲的是什么什么，这是它为什么重要的原因，
这是我们要往哪里走，我们将要讨论什么。”

conclusion是用来向后看的（有点像三明治），
就像个镜子一样，
需要总结全文，主要是为了提醒读者，这篇论文讨论了什么东西，
之后要写你真实的评论，你是怎么想的。

Conclusions and future work, again, try to “guess” the conclusions — do you agree with the authors about what is important? Do the suggestions for future work give you inspiration for your project? (Check that no-one has done them subsequently, though!)

结论的时候说自己做出来了什么，优缺点是什么，还有哪些待解决的问题，结尾段要说what impact that has on the initial question。

Summary, conclusions, evaluation—it is very important that you demonstrate an ability to reflect self-critically upon your individual work. In this chapter section you should demonstrate self-critical evaluation of the extent to which you have achieved your objectives, and explain how you have advanced the state-of-the-art in research and/or technology in your area. Failure to achieve all the objectives you set yourself (especially the more challenging ones) will not in itself result in a loss of marks - but glossing over failed achievements or ignoring them altogether certainly would.

你的贡献对当前的研究领域有什么意义？
我们通过这次经历学到了什么，Reflections/learning—what have you learned as a result?

1. briefly summarise

Summarise the main ideas and the position.

Very briefly summarise the main findings from your analysis of the position.

Indicate the extent to which you agree with the position you have analysed.

短小精悍地再次去总结一下主要的发现成果，
之后你同不同意某个position，需要考虑进一步的研究与建议。

这些发现结果依赖于分析，当你在评估reason与evidence时，
尝试去判断去权衡，之后你就能发现一些东西，
之后去展示在多大程度上你同意或不同意，
你是否找到了更多的去支撑论点的想法或更多的争议性问题或更多的相反的argument，这很重要，因为你的立场并不是最终的，
你的论文还在继续进行，所以我们要评估一下你能支撑多少东西，
这很重要，请反复阅读，这表明了程度，这很重要。

2. counterarguments

3. Suggestions/measures to take forward

Suggest areas that may need further exploration/critical focus

在结尾段的最后，要体现出这个topic有进一步探索的可能，
类似于一个开放式的结尾，一切皆有可能，
还会有更多的探索出现，还没有结束，
这只是这个topic的一小部分。

An overview of where the project ideas could be taken in the future, e.g. by a student in future years doing a project on the same topic.

Reference

Bibliography - please make sure that all references are properly cited (any commonly-used citation method, such as the Harvard system, is fine). Note: your bibliography should include full references and not just URLs, where possible. You should therefore also track down and reference archival copies of material (either full journal/proceedings references or technical reports) wherever relevant and possible

Presentation

未完待续

Genre: this focuses on whether or not you understand what the purpose of the viva is, that you know what the summary is for (and that you have prepared it accurately), and that you know how to conduct yourself in a one-to-one interview situation.

Criticality: can you explain the main position(s), can you offer suggestions based on your evaluations, can you answer your question with more than one-word answers (and without being asked ‘why’), can you provide reflections on what you have experienced this term (e.g. what happened, how it made you feel, what have you learnt from it etc.) - things like this

Language: this focuses on choice and accuracy of vocabulary (including linking words) and grammatical structures, whether your answers are connected and understandable (cohesion and coherence), and your pronunciation and intonation.

• 论文中不要有特别复杂的概念，否则答辩时会死的很惨，确保论文里的内容都是自己可以用英语口述解释清楚的，自己多看几遍自己的论文，做到滚瓜烂熟，在答辩的时候有优势，一定能比考官更熟悉，考官看你的论文，有不懂的地方他就会问你问题，考官就会找一些你论文中不好的地方问你，你要先在答辩前自己问自己问题；

• 把自己的summary录下来，自己反复看，能找出来问题；

• 不要说"some people think that, other people think that"，需要更准确，例如：“A group of 20th century researchers have found out that.”；

• 无论说什么，都不要重复再重复；

• 多用tentative language；

• 考虑相同立场的不同方面；
  不要说My controversial issue is xxx.

老师可能会就1个问题而问一些更深入的问题。
保证你的回答里包含why? how?

例如：为什么你选择这个文献？你是怎么找到数据的？你为什么要用这些数据？这些数据的说服力怎么样？强弱分别是什么？你的建议是什么？为什么你选择这个建议？（把你的paper关联到1个建议上，可以贡献到学术界的一些东东，把Java变得更简单一些）

为什么你选择使用这个特定的单词？
为什么你选择了这个特定的想法？
为什么你要先讲这个想法，再讲别的想法？
他们也会问你的逻辑，去看你是否有批判性思维。

老师也可能会去读你的文献，哪怕只是看一眼。
所以自己要熟悉自己的source。

考试时，需要有自己的论文，打印出来。

要有自信，是我读的文献，
我知道作者的名字，发布年份，知道它在讲什么，

identify and analyse the strengths & weaknesses of chosen sources

explain, argue & defend own position (and to analyse others, as appropriate)

explore potential conclusions and make recommendations

不要去找general answers，而是找属于你自己的answers。
找你自己的经历，你是怎么实现的，
你学到了什么，你发现了什么？
你是怎么做的？

考官会发现你是在讲你自己的亲身经历，
还是在读课件里的理论知识去糊弄老师，
考官希望理论+自己的亲身实践，
要有你自己的理解，自己的想法，自己发现的东西。

你的论文可能会有很多复杂的地方，可能需要你去解释。
去cover住你论文的不足部分。

课件中的重点概念，自己列个表。
用自己的话能说出来，
有时间的话去看一下课件中的reflection内容。

make a list of academic words that are key in your PAP.

then check the pronunciations using online dictionary

stress the words that have the key meaning in a sentence avoid incomplete sentence, give reason for the statements made

make a list of things you don’t know, you are curious about, you need to find out and what you found out through your course and research work

what are your strategies to develop your research: how do you evaluate the sources?

• not just read, read critically, then take notes, compare and contrast the different findings, make a table, think about the purpose of your research, what was the contradiction, the controversy, what arguments did you find, what can your own findings add to it…

make these in a table.
write the name of the source
what is the argument?counter-argument?
how it suits my topic
judge how strong and how weak?

this talbe will lead me to the article.

做笔记，把相关的论点放到自己的table中，
什么是evidence，是正面论点还是反面论点？
找到strength和weakness

在阅读时，close reading（精读），尝试找到结论，
对内容提问，批判性阅读，评判评判它，跟其他的文献作对比。

考官会期望你识别并分析你为论文所选材料的优缺点，
并反思你在寻找或使用这些材料时遇到的问题

在整个研究过程中，积极地思考你的信息来源有多“好”(或“可靠”)，并准备好解释你为什么选择它们

• You will also need to explain, argue & defend your own views (and possibly to compare, contrast & evaluate views which may be different to your own)

Take advantage of every available opportunity to explain ideas from your paper in your lessons and tutorials (i.e. answering questions, not just asking them).
Consider significant arguments around the areas you choose to focus on in your topic, and think about to what extent you agree or disagree with them. If you disagree, why? And what evidence do you have to support your views?

• You will also need to explore your conclusions with the assessors and may be asked to link these to practical implications

Make conclusions about the ideas you read. Think about what the results of your views might be on both theoretical and practical levels. Can you make any recommendations, either for further research or how the ideas might work “in the real world”?

• Part of the assessment involves demonstrating that you can reflect on the research and writing process and how this has affected your views, ideas, habits, and so on. You also need to reflect on the key lessons learned from your experience of studying on this course

Think about how the things you do whilst researching and writing your paper affect you in both positive and negative ways. For the positives, think about why they were of benefit – what was successful? What made you feel good about your progress? For the negatives, consider what, if anything, you learned. How did you react to negative feedback? How did you deal with the challenges? Did you need to do things in a different way to solve the problem? If so, how did changing your habits help you?

• You also have to try to link your experiences on the PEAP 4 course to your future studies at university in the UK, and to do this in a meaningful way

During the process of studying this course and researching your paper, what significant lessons have you learned which will help you to succeed at Bachelor’s, Master’s or PhD level. Be explicit and be ready to explain and exemplify this; don’t just say “I have learned lots of good things”. This is also a reflective exercise, so consider similar questions to the ones in the previous section: What exactly have you learned? How easy or difficult was it to learn this? How exactly will it be of benefit when you join your department? Think deeply about this, and maybe make notes on possible ideas throughout the PEAP course.

1. Did you include a clear description of the topic?
2. Did you explain clearly why you chose that topic?
3. Did you cover some aspects, positions or views you would like to include in your research?

Summary

you will be expected to give a brief summary of your written essay, based on a selection of what you feel are the most important ideas.

在summary中，
转换成自己的语言，因为写作的语言跟口语是不太一样的。
可以给例子、data、evidence，但不要给太多。

背景信息介绍缩短一些，更多的foucs在argument上。

如果删掉detail，还能看懂，那就删，否则就留着，
如果必须要读者听到，就留着。
留不留着可以问tutor。

[外链图片转存失败,源站可能有防盗链机制,建议将图片保存下来直接上传(img-HBr8Eg5S-1678239720735)(evernotecid://85A94EB2-9BD2-45DE-A16D-D9C9CD7ECF0B/appyinxiangcom/12192613/ENResource/p538)]

Discussion

想想考官会问什么，自己先preview几遍。
不懂就问，千万不要不懂装懂

a question-and-answer session with two assessors in which you will discuss various aspects of your paper in order to evidence your knowledge of your chosen topic. You will also be expected to show critical and reflective thinking, and to demonstrate your ability to use appropriate oral communication skills (approximately 15 minutes).

During the discussion stage of the viva, you will be asked a number of questions which will allow you to demonstrate your ability to:

• identify and analyse the strengths & weaknesses of the sources you used in your essay
  ~ explain, argue & defend your own views
  ~ compare, contrast & evaluate views which may be different to your own
  ~ explore your conclusions and link these to practical implications
  ~ reflect on the research and writing process and how this has affected your views, ideas, habits, and so on
  ~ link your experiences on the PEAP 4 course to your future studies at university in the UK

Source相关

回到你的文献中，阅读摘要、标题、开头段、结尾段，
选择并记住至少3篇最贴近你paper的最重要的文献来回答口试问题，不是所有的文献都同等重要。

把作者名字年份都写出来，不要只是记在脑子里。
在口试summary中也要说sourecs吗？需要check

熟悉并且可以背诵短的名字+作者+年份，
去看在自己的paper中，哪些与之关联，
去到你的paper中高亮用到source的地方。

并问自己，用的对吗？有逻辑性吗？strength或weakness？
Why? How? What else? Why not?
Can I change it?
Should I change it?
Is it OK?
How can I make it more befitting to my paper?
口试需要提及sources，文献是如何关联到你的paper的，
你是咋用的？
每个文献要有1个strength和1个weakness。

strength of a [research/source]
1方面：
strength并不是你是否可以使用这个文献，
strength是你[检查/调查]文献，例如：
文献的作者是谁，他是可靠的作者吗？
在哪发布的？关联性怎么样、发布日期？？
多大范围他能覆盖？调查2个公司？10个公司？
考虑2件事？考虑3件事？这些都是研究的范围。

能给到你什么详细的考虑？
作者是否带有偏见？例如：他只调查了1个国家，
并没有考虑到国际化问题，
并没有考虑到近期的数据更新问题，这些是weakness。

1方面：
考虑了A但没有考虑B，
考虑了A是strength，没有考虑B是weakness。

如何找？

想想你从文献里用到了什么，
通过对比不同文献，看看其他[维度/立场]，
再想想文献里缺少什么？
缺少的东西就是这篇文献中的weakness。

引用的所有资料来源都是相同的topic，
找到你要用的文献中的内容，1，2，3列出来，
不要写many things，
不要写very suitable，
你要说它合适是因为它帮助了很多人更快速的学习编程，
例如时间的概念，Python也许非常有效，但它会花更长的时间，
这个点没有被考虑进文献中，这就是weakness。

例如：
文献A谈论了BCD，文献E谈论了FGH，
试图去对比，A和F的不同之处是什么？
之后你会发现gap。

你在谈论使用Python时，
你从source里考虑的是什么factor？source是咋说的？
需要具体的答案而不是more suitable，
Python在哪方面比Java更好？更适合？
cost? availability? development? expertise? studying the field?

Java在哪方面比Python更好？更适合？
Publicity（知名度？）? popular？[cost effective / cheap]? available? Universal?
对比这2个aspects，小心地思考，文献中批评Python的地方是什么，如果Java这么流行，那为什么还会有矛盾？

Python在多大程度上占据了Java的市场份额？
你为什么这样认为这个更重要，
当文献没有覆盖到Java的这些factors，这就是文献的weakness。

你在考虑，为什么使用Java以及为什么应该使用Python？
之后你在分析不同的立场关于Java和Python，
现在你关注在文献上，
这些文献有没有写Python能提供的不可思议的机会，
没有考虑cost，没有考虑availability，没有考虑最新的研究，
As Java已经发展到1个高等级，这就是文献中的weakness。

需要看自己的录像，去找自己的问题，多问why，
看看自己讲的是否明确？
信息是从哪里来的？
为什么我要用这个信息？

学术写作小妙招

• 不要写雅思那种加分的长难句，1句话不要超过20-25个单词，教授讨厌看到马拉松式的句子；
• 第一版论文写完后，要进行一些检查和反复阅读，打磨自己的文章，其中包括：检查每段的主旨句是否恰当，语法错误，逻辑结构，是否跑题，重复率等；
• 不要在学术论文中提出反问句；
• 使用省略+代词，写Java1次，用it代替19次，除非怕弄混了可以继续用Java。需要使用”省略”技巧，用写go to the cinema to watch a comedy moive，因为这是常识大家都知道，你是去看电影，直接写成go to the cinema to watch a comedy即可，你不可能去电影院看书；
• 用一些非限定性定语从句；
• 英语学术论文的主体段的写作逻辑为：提出观点->解释观点->证明观点，给出例子或引用文献；

Google Scholar使用技巧

双引号词段限定符：谷歌学术支持用双引号（英文半角格式）””作为词段限定符。这个限定符非常有用，因为谷歌学术搜索多个关键词，有时候它们不在同一个句子，相隔比较远，参考意义不大。而使用词段限定符可以使关键词按顺序排列在一起。

通配符：谷歌学术支持 * （星号）作为通配符，结合双引号词段限定符使用，可以得到很好的效果。例如，检索"in this paper we* the"，可以得到根据搜索结果，对于这样一个句子：In this paper we_____ the，我们可知we后可以接generalize/ verify/ reevaluate/ investigate/ propose/ compare/ present/ discuss/ explore/ describe/ demonstrate等动词，我们可以根据需要，填入相应的单词，是不是很简单？

谷歌学术支持多个通配符联合使用，每个 * 代表一个单词。搜索"in this paper we* the effect of **** on the"，可以得到根据搜索结果，可知in this paper we后面常接的词有report/ investigate/ examine/ estimate/ show/ propose/ compare/ investigate/ present/ discuss/ explore/ describe/ demonstrate等等。

我们还可以根据谷歌学术搜索结果来判断用词和搭配是否合理。例如，我们想写一个词组“XX随着XX的减少而增加”，我们可以搜索“增加”和“减少”的关键词并根据返回结果的数量来判断：

"increased with *decrease"约4万条

"increase with *decrease"约3万条

"increase with *decreasing"约7千条

"increased with *decreasing"约7千条

"increasing with *decrease"约3千条

"increasing with *decreasing"约2千条

"increased with *decreased"约2千条

"increase with *decreased"约5百条

"increasing with *decreased"约84条

由此可知，"increased with *decrease"和"increase with *decrease"这两种用法更常见一些。

优点：谷歌学术数据库最大最全，支持词段限定和通配符检索。强烈建议联合使用词段限定+通配符进行搜索。

缺点：包含大量英语为非母语人士所写论文，句子质量良莠不齐。国内上谷歌学术不方便。现在国内有很多网站提供了谷歌学术的镜像网站，需要的话可以自行搜索。

有用网站

https://translate.google.com
谷歌翻译

https://scholar.google.com
谷歌学术

https://www.grammarly.com
语法检查

https://www.phrasebank.manchester.ac.uk
例句

https://www.thesaurus.com
同义词

https://www.turnitin.com
论文查重

https://quillbot.com
句子改写

https://writefull.com
学术写作帮助
（可能需要在线版的Word，https://www.office.com/）

https://www.collinsdictionary.com
柯林斯词典

先阅读再写作

一般情况下，
论文中的论点必须要有文献做支撑，
不能只是单纯的自己的想法，
需要用别人的论点来帮助自己，
不能自己瞎编乱造。

千万不要先写论文再搜文献，
因为很有可能你自己的想法没有任何英文文献支撑，
这样就傻了，必须要推翻重写，浪费很多时间。

你可以谈论你自己的想法，
但不要聚焦到你自己，在写作时，
不能用让读者识别到”你”，你可以用被动语态，
你可以解释内容，可以给出自己的想法，
但不要说“我”。

不要写我认为怎么样，不要用I think这样的词，
也不要写没有任何文献支持的观点作为你的claim，
这样是没有意义的。

因为你不知道你的opinion是对是错，
所以那你需要找文献论证，
这过程可能找到支持你的正面论点，也有反面论点，
如果正反都有，那你是更支持哪一个，
若你赞同正面论点，
那你就需要argue一下counter-argument。

研究过程不应该被”我先相信这个论点，然后我再想去证明它”所驱动。

你的position应该基于你所发现的东西，
你不应该选择evidence只是因为它支持了你的想法，
你不是在选择去支撑你想法的东西，
而是你在基于你读过的东西做决定。

自己的观点不重要，重要的是文献中的观点。
文献太多，可以搜关键字，并用略读等方式。
有很多网上的数字图书馆，
需要用学校账号密码进行登录，非常方便，
例如：谷歌学术搜索和自己学校的数字图书馆。

学校论文查重

每个学校基本都会有自己的查重软件，
以英国诺丁汉大学为例，
使用Turnitin（查重软件），
报告可能在24小时后出来，
在段落中有大量的标注，那就得重写，
查重报告无法体现出补丁写作，
但老师能看出来，不要尝试走捷径。

不算抄袭的部分：

• 学术词汇短语；
• 带引号的引用；
• 段落标题；
• 缩写字母+每个字母的非缩写单词（UTIFEN）；
• 引用列表；

此外，还要注意不能自我抄袭，
例如之前自己原创写过一篇论文，
然后把那篇论文的某个段落复制到自己的新论文中。

查重越早做越好，
不要等还有1个小时就是deadline了才去查重。

管理文献工具

Mendeley，引用管理大杀器之一，
参考文献引用管理软件，非常非常有用。
用于存储、管理学术文献资料，
就像个人图书馆一样。

假如你有10个文献资料，
你把它们的PDF保存到Mendeley中，
你可以输入信息，记笔记，分享屏幕给tutor，
让tutor看到你都看过什么，tutor还可以评论。

在写论文时，
Mendeley还可以帮你往Word文档里加引用，
以及自动生成底部的引用列表。

这个软件可以组织管理你的学术阅读，
你可以把每个文献放到软件中的文件夹里，
这样可以有效管理。

我的建议是，无论你阅读什么东西，
你都需要把它们放到1个Reading List中（Mendeley），
以便有组织地阅读，把你读过的内容都放进去，
就像是一个你自己的图书馆，
当你写完论文时，
你只要用你论文中要用到的source去复制粘贴到论文中即可，
你在reading list中可能会记录20-30篇文章，
但你真正用到的可能只有10个。

如果你现在做这件事，以后就不必做了。
例如，
我选了1本书，我在书中找到了一些有用的东西，然后我做了笔记，
之后我会把这本书放到我的reading list，
所以我要做的是，我也许会找到这本书的类型是什么，
假如是一本教科书中的其中一章，我就会去找到指南，
去看看如何符合相关的恰当的书写格式。

当我在写完论文时，
我就可以直接复制粘贴进我最终的reference list中，
任务完成，不要等到最后一天才去做reference list，
因为你会做的很糟糕并且花费非常多的时间。

引用文献是一件很无趣的事情，但你还必须得做，
如果你delay的话，那会给你带来更多的工作量。

有的时候pdf文件属性中的作者和文章日期可能是错的，
在Mendeley中可以非常容易的进行修改。

引用格式

一般论文会被要求使用一种引用格式，
例如: Harvard Coventry，APA，IEEE等。

每个格式都会有相关的格式指南，
这本指南告诉你什么样的引用格式是正确的，
这本是拿来用的，不是像小说那样拿来看的。

在未来你将习惯，无论你学的是什么，
你总是需要引用参考文献，
你都需要遵守一本特定的指南，
并不一定非得是这本哈弗考文垂指南，
可能是任何指南，哈佛只是一种风格，
哈佛可能有不同的版本，
也会有一堆不同类型的参考文献，
如果未来的老师让你用一本指南，
你必须要准确无误的去用。

1个论文只能用1种风格，不能混合，
这里一般使用软件就可以自动化生成你想要的格式，
不用再费劲去看指南手册。

学术写作风格

你需要使用适当的文字，
不能用[俚语/口语/成语/土话/习语]来表述，
你需要稍微正式一些，这
样有利于在学术环境中理解你的内容，
这些格式在我们上课都会或多或少的学到，
当你在阅读文献时，
也可以仿照他们的风格，
如果你的写风格与文献中的风格大相径庭，
那么你最好修改你的写作风格，
这些风格也跟你自己的专业有关，
不同的专业风格会有偏差，
例如你很少在学术写作中看到经常使用”I、we”，
一些学术写作的例子，
例如：不能用don’t，只能是do not，避免动词短语，避免俚语，
避免发表过于强势的言论（不要像政客那样去表达观点，那样太有进攻性，太有激情太情绪化，太主观，例如不要用：”这个是最好的解决方案”，”这是唯一的办法去做某件事”，“这件事情will发生”，去健身房了就一定会减肥吗？

有的人去健身房可能会增重，
所以这件事情不一定是对的，
所以要调整自己的强弱表达，
或者换一个动词，
不要用will（看具体的情况，并不强制），
相反地应该适当使用试探性、
假设的表达(例如，这”也许是”最好的解决方案)，
需要保持客观性，应该基于事实，
而不是基于自己脑子里想当然的东西。

感受一下强弱变化：

1. Ali is stupid.
2. Ali seems stupid.
3. There are signs of stupidity in Ali’s way of teaching.

并不是绝对不能用will，具体情况具体分析。
“Smoking will kill you” 太强硬了，
并且这句话不太成立，因为很多人抽烟，
但很多抽烟的人并不是因为抽烟死的，
而是因为其他原因，
但如果说出演提升了死亡的几率，
例如肺癌，这样就更准确一些。
“Smoking is highly likely to kill you”
这样说会更合适一些。

很多学生会认为用了那种很强硬的话术，
就会显得自己的argument更强更有说服力，
但实际上会显得有些夸张，有点像在说谎，
或者像政客在发表演说，
所以在写论文时要尽可能的准确。

例文

Title
Choosing programming languages for learners: Java or Python?

Abstract
There are a variety of high-level programming languages today, each with its particular purpose. Because of the usability of different programming languages, it is difficult to choose an ideal programming language that is appropriate to learn for beginners. There are still a large number of debates on which programming language, Java or Python, is more appropriate for learners. This paper analyses the position that, while there are advantages and drawbacks of both Java and Python, Python could be more suitable for learners in some situations. The aim of this paper shows the characters of two famous programming languages in three different aspects: learnability, popularity, and efficiency. Learnability is a significant requirement for learners of a first programming language. Although Java is simpler than C++ and many universities offer Java courses, Java is still difficult to learn and Python is easier. The results of this analysis indicate that despite Java is more popular and successful for many reasons, Python is more suitable for learners without programming experience. On the other hand, Python is more suitable for AI direction and Java is more suitable for back-end direction. It also suggests that Java developers should be mindful of the challenge of Python to change Java simpler for beginners in the future.

Introduction
Recently, there has been growing interest in programming languages. A computer programming language can be recognized by computers and it is a bridge between human beings and computers, which means programmers can use it to ask computers to do some actions, such as storing data, running some programs and accessing resources. There are numerous high-level languages today, each with its specific purpose. For students, choosing the first programming language is important, because it could affect their future career development plans if they want to join the programming industry, different programming languages go in different directions and do different things. Nevertheless, it is difficult to choose an ideal programming language that is appropriate to learn for beginners because of the features of different types of programming languages.

There is still a large number of debates about Java and Python. They are both quite popular languages at present and both emerged in the 1990s. Java is an important part of high-performance Internet architecture. It is the most popular and suitable programming language for learners (Farooq et al. 2014). Whereas with the rise of the artificial intelligence (AI) industry, Python is becoming more popular and attractive recently. Additionally, it is more suitable for beginners to use Python because of its simplicity. It is a modern high-level scripting language that can increase the productivity of coders, the readability of code, and the usability of language (You et al. 2018).

This paper aims to analyze the characters of two famous programming languages in three different aspects: learnability, popularity, and efficiency. Also, It evaluates which programming language is more suitable for beginners to learn. Despite Java is successful and popular, Python is more simple and more suitable for beginners without programming experience. On the other hand, Python is more suitable for AI direction and Java is more suitable for back-end direction (Deploy programs to run on a server and provide services to the public). The first section analyses which language is easier to learn for beginners, then the second section focuses on which language is more popular in the world and what are their application scenarios, the final section will explain which language is developed more efficiently.

Learnability and Simplicity of Languages
Java is easier to learn because Java is simpler than C++ (C++ is another popular programming language). Java not only absorbs C++’s advantages but also avoids several complex concepts in C++, such as multiple inheritance and pointers (Gupta 2004). Learnability is an important criterion of a first programming language to students, which means how fast learners could learn and how easily they could start. Moreover, Ivanovića and Budimac (2013) inappropriately state in their paper that there are still many Java courses in different universities and colleges in the past decade because Java is easier and cleaner than C++. Unfortunately, there is no general agreement with it, and the quantity of Java courses in the academic environment does not mean the suitability of the first programming language. According to Ivanović et al. (2015), they changed their first programming language from another language to Java, but there were no significant effects and differences in students’ performance in different generations. Although the scope of the study is small and it does not take into account other countries and regions, it could show that Java is not all-purpose to be the introductory programming course for students.

Additionally, learning Java is not easy for novices, which means they have to learn a large number of basic complex structures and grammar to write programs (Farooq et al. 2014). For instance, Java’s “main method” (see Figure 1) could not allow users understand why is it designed that way, and it is complicated to explain several basic concepts, such as ”void”, ”static”, “class”, ”public”. In addition, It is too difficult to understand all those details without losing themselves, especially for children, teenagers, and incoming college freshmen. For example, if novices misunderstand some difficult concepts, such as reference passing and value passing, if not accurately explained, it will quickly lead to incorrect programs that are difficult to debug (Gupta 2004), which could be easily frustrating and confusing for them. Consequently, it is not suitable for beginners who are with no programming experience because of Java’s complexity and verbosity. Whereas complexity does not mean negative, once they know those basic concepts and logical framework of programming, it is easier for them to learn the next language (Ivanović et al. 2015).

Figure 1. Python is much simpler than Java (Farooq et al. 2014)

In contrast, using Python could allow both students and teachers to achieve their educational objectives and it seems like Python is a suitable programming language for beginners who are trying to engage in the IT industry for the first time (Sanders and Langford 2008). Due to Python’s learning curve is quite simple, it is easier to study, understand, read, and write, which means Python could help the developer to build applications that are much simpler and easier to grasp than Java (Adhianto et al. 2010). There are more brief grammar structures and fewer clear keywords because of its English-like grammar, which means students could focus more on logic and algorithms (You et al. 2018, Farooq et al. 2014). It’s easier to learn because it’s the human’s way of thinking. Figure 1 shows the comparison of simplicity between Java and Python, it clearly shows that Python is much simpler than Java.

Meanwhile, learners have a chance to easily understand the basic concepts of programming language, such as control structure, data types, and loops, which means it is suitable for learners without programming experience (Kruglyk and Lvov 2012). Additionally, It could be more suitable for K-12 (kindergarten through twelfth grade) students (Lye and Koh 2014) to use Python as their first programming language for children’s programming. K-12 students could use Python to develop their brains and promote their thinking ability (Xinogalos et al. 2018). According to Sanders and Langford (2008), their survey indicated more than 50 students with both experienced and inexperienced considered Python is more suitable as a first programming language, and only a few experienced students did not wish to move Java to Python. Their scale is not reliable and too small to represent the whole trend, but it could still show Python’s growth in the small sector (Xinogalos et al. 2018). Yadin (2011) states in his paper that reducing almost 80% dropout rates of students when their academic institutes use Python as the introductory programming course for several years. Obviously, Yadin’s research is more convincing because his research is much larger and longer. Nevertheless, their researches both have weaknesses because they did not take gender and countries into account. It could be argued that learners whose native language is English tend to prefer Python because of its English-like grammar, and vice versa.

Although it seems like Python is simple enough, students need to know that simplicity is not necessarily a positive aspect. It could be argued that Python could avoid many difficult underlying concepts of programming, and they do not have a chance to understand these important and useful concepts. Hence, Python is not strong enough transition to another difficult programming language, such as C++, Java.

Popularity and Applicability of Languages
Python and Java are both quite fashionable in business and academic contexts, and they both have many application scenarios (Chen et al. 2019). Nevertheless, Java is still the NO.1 global popular programming language and its usage rate is still the highest in the past decade (Sherman, Shehane, and Todd 2018). Because Java is a global software platform and its enterprise-level applications have developed rapidly in the 21st century, widely used in telecommunications, e-commerce, finance, transportation, web applications, back-end service, big data (Lin 2016), Android (mobile applications), and other industries of information systems (Kruglyk and Lvov 2012). Also, there are still numerous companies that use Java systems, such as Alibaba, Google, and Airbnb (Yin et al. 2018). Students are typically conscious of Java’s success in the industry and they could be inspired to learn Java (Ivanović et al. 2015).

However, the popularity of Java does not mean it is the most suitable programming language for learners, they also need to pay attention to the trends in the IT industry. Although Python is typically smaller and scarce, it rises more rapidly in recent years because of the development of AI and big data. Python is the main language for AI mainly because scripting languages are easy to write (You et al. 2018), and that is the reason why Java cannot do that. It is applied to web development, AI, machine learning, data mining, data science, scientific computing, statistical education, and back-end development in areas of software development (Roth 2019, Adhianto et al. 2010, Kovács and Ghous 2020). As AI is the future of IT, it could be regarded as the programming language of future development.

Java and Python are both dominant in their respective fields, and their programmers are also in high demand (Farooq et al. 2014). Generally, students want to know the popularity and applicability of programming languages because they want to find a job, they seek a skill that can increase the success rate of the interview. It is reasonable for most students to know what is most popular when entering a new field. On the other hand, their salary level is higher than in other languages (Kruglyk and Lvov 2012). Consequently, students also need to take into account those programming languages’ usage scenarios when choosing the first language.

Efficiency and Usability of Languages
There is enough friendly ecosystem (include a large number of tools, third-party libraries, and frameworks) for Java (Farooq et al. 2014), which increase the development efficiency because coders do not need to code many lines, they can use libraries’ code directly (Kruglyk and Lvov 2012). Efficiency is especially significant for individuals who can complete the task more efficiently in the workplace, because they could save more time, energy, and cost in future work.

Nevertheless, the development efficiency of Python is much higher. Not only because Python has numerous libraries, but also because with the same function, Java needs a dozen lines of code and It is a huge waste of time. However, Python only needs a few lines of code, the number of Python’s code is much less (You et al. 2018), which reduces development times (see Figure 1).

On the other hand, Python’s execution speed is not fast (Holm, Brodtkorb, and Sætra 2020) because Python is a dynamically typed language, which can run directly without compiling (Programming languages’ source code need to compile first, then they could execute in the operating system), it has to compile and execute concurrently, that is the reason why Python’s speed is slow. In contrast, Java needs to compile first to run Java programs. Therefore its execution speed is faster and it seems like when coders need the scenario of running fast, Java could be their better choice. Whereas users also have to consider Python’s libraries, and libraries could fix Python’s performance problem (Roth 2019). Consequently, Python can be used in a production environment, as Java does.

Moreover, one of the most powerful features of Python’s usability is that it could add other languages easily. Python is called “glue language” because it can combine modules written in other languages to form a new program. For example, it can easily combine modules written in C (C is one of the most famous programming languages all over the world) and Java to work together. In that case, there are advantages of two different languages in one complete program (Kruglyk and Lvov 2012), and that is a huge advantage that Java cannot compete with.

Conclusion
In conclusion, this paper demonstrates the suitability of Java and Python depends on three different aspects: learnability, popularity, and efficiency. Based on the argumentation above, they have both advantages and disadvantages. Although Java is popular and it has a large number of application scenarios. It is not appropriate for beginners such as children, teenagers, college freshmen, and some individuals who have never learned to program. In contrast, Python is more suitable for them because of its quick-start feature and simplicity. Furthermore, it could be the most powerful language shortly because of the rise of the AI industry.

On the other hand, users could select a suitable language based on their purpose. If learners choose the AI or machine learning direction, it is apparent that Python is more suitable. Java seems now more advantageous for back-end development because there is a larger share of the back-end market of its legacy systems, Java would be a better choice for learners who want to work on the back-end services.

With the development of the IT industry, the debate about which programming language is more suitable to learn could never stop. Developers of Java should have the awareness of Python’s challenges and simplify particularly complex concepts so that Java could be more friendly to beginners.

References
Adhianto, L., Banerjee, S., Fagan, M., Krentel, M., Marin, G., Mellor-Crummey, J., and Tallent, N.R. (2010) ‘HPCTOOLKIT: Tools for Performance Analysis of Optimized Parallel Programs’. Concurrency Computation Practice and Experience 22 (6), 685–701
Chen, C., Haduong, P., Brennan, K., Sonnert, G., and Sadler, P. (2019) ‘The Effects of First Programming Language on College Students’ Computing Attitude and Achievement: A Comparison of Graphical and Textual Languages’. Computer Science Education [online] 29 (1), 23–48. available from https://doi.org/10.1080/08993408.2018.1547564
Farooq, M.S., Khan, S.A., Ahmad, F., Islam, S., and Abid, A. (2014) ‘An Evaluation Framework and Comparative Analysis of the Widely Used First Programming Languages’. PLoS ONE 9 (2), 1–25
Gupta, D. (2004) ‘What Is a Good First Programming Language?’ XRDS: Crossroads, The ACM Magazine for Students 10 (4), 7–7
Holm, H.H., Brodtkorb, A.R., and Sætra, M.L. (2020) ‘GPU Computing with Python: Performance, Energy Efficiency and Usability’. Computation 8 (1), 1–24
Ivanović, M., Budimac, Z., Radovanovic, M., and Savić, M. (2015) ‘Does the Choice of the First Programming Language Influence Students’ Grades?’ ACM International Conference Proceeding Series 1008, 305–312
Ivanovića, M. and Budimac, Z. (2013) ‘First Programming Language - Never-Ending Story’. AIP Conference Proceedings 1558 (October 2013), 353–356
Kovács, L. and Ghous, H. (2020) ‘Efficiency Comparison of Python and RapidMiner’. Multidiszciplináris Tudományok 10 (3), 212–220
Kruglyk, V. and Lvov, M. (2012) ‘Choosing the First Educational Programming Language’. CEUR Workshop Proceedings 848, 188–198
Lin, D. (2016) ‘Application of Big Data Platform in Course of Java Language Programming’. International Journal of Emerging Technologies in Learning 11 (10), 16–21
Lye, S.Y. and Koh, J.H.L. (2014) ‘Review on Teaching and Learning of Computational Thinking through Programming: What Is next for K-12?’ Computers in Human Behavior [online] 41, 51–61. available from http://dx.doi.org/10.1016/j.chb.2014.09.012
Roth, G. (2019) ‘Machine Learning with Python: An Introduction’. JavaWorld [online] 1–5. available from https://www.javaworld.com/article/3322898/application-development/machine-learning-with-python-an-introduction.html
Sanders, I.D. and Langford, S. (2008) ‘Students’ Perceptions of Python as a First Programming Language at Wits’. Proceedings of the Conference on Integrating Technology into Computer Science Education, ITiCSE (March 2000), 365
Sherman, S.J., Shehane, R.F., and Todd, D.W. (2018) ‘Quantitative Model for Choosing Programming Language for Online Instruction’. Journal of Instructional Pedagogies [online] 20, 1–16. available from http://www.aabri.com/copyright.html
Xinogalos, S., Pitner, T., Ivanović, M., and Savić, M. (2018) ‘Students’ Perspective on the First Programming Language: C-like or Pascal-like Languages?’ Education and Information Technologies 23 (1), 287–302
Yadin, A. (2011) ‘Reducing the Dropout Rate in an Introductory Programming Course’. ACM Inroads 2 (4), 71–76
Yin, F., Dong, D., Lu, C., Zhang, T., Li, S., Guo, J., and Chow, K. (2018) ‘Cloud-Scale Java Profiling at Alibaba’. ICPE 2018 - Companion of the 2018 ACM/SPEC International Conference on Performance Engineering 2018-Janua, 99–100
You, F., Gong, H., Guan, X., Cao, Y., Zhang, C., Lai, S., and Zhao, Y. (2018) ‘Design of Data Mining of WeChat Public Platform Based on Python’. Journal of Physics: Conference Series 1069 (1)

【为什么选这个topic】

The reason I chose my topic was because I have several years of working experience and I’m kind of familiar to this area. Besides, I’m curious and interested in computer science and programing languages, so I can maintain motivation throughout the research process. In addition, there was much debate about it in the field of computer science.

【topic变过吗】

It’s easy to choose and I just change a little bit of my topic, because my personal tutor told me my original topic is kind of broad, my first version of my topic is “What’s the best programing language in the world?”, that’s kind of vague. I cannot explain what’s the meaning of the “best”. It’s not suitable at all.

I just can’t cover so many things in only 2000 words paper. So I have to narrow down and focus on the specific details.
Then I change my topic to “which programming language, Java or Python is more suitable for learners”, then I put three different aspects to compare the two different languages. So problem solved.

【哪部分最重要】

it’s easy to decide, the most important part in my paper is “Learnability and Simplicity of Languages”, it easy to decide, because my topic is “which suitable programming language is more suitable for beginners”, so I have to pay more attention on learnability. Learnability is an important criterion of a first programming language to students, which means how fast learners could learn and how easily they could start.

【读过但不用的idea，为什么】

Yes, I’ve read something about the difference between Java coders’ salary and Python coders’ salary, but there is no more detail information. I’m really interested in this area, then I just go on searching and I figure it out that I cannot search more articles related to salary thing. I’ve already changed many keywords, but it still won’t work. So I have to ignore this idea because I don’t have any supporting arguments, I just can’t use my personal idea without source support.

【遇到过什么困难吗】

actually, I have one big problem with finding sources for my paper. this is my first time to do the English academic writing paper. At the beginning, I just confuse a little bit about whether one source is suitable for me. After I search some keywords in the NU search , then I found out there are so many academic articles out there. But they are just too long to read. Besides there are some complex sentences that I can’t easily understand, so I have to waste my time to check those words in the dictionary. And most importantly, I just cannot read it faster. Then I figure it out that my original idea is totally wrong after my tutor told me, I have to use the skills called “skim reading”, which means I have to only read the abstract, introduction, conclusion, every topic sentences of the main body paragraph, and search the keywords in the article. those skills could help me to decide which article should I use. So problem solved.

Academic writing can be challenging to understand – how were you able to overcome problems or what can you do to improve in the future?

【如何理解主题发生变化，学到了什么最重要】
the most significant parts I learned are

Firstly, I need to be critical and objective.
There are both positive and negative ways out there, so we should learn to think critically and can’t believe all the professors’ opinions.

Secondly, I need to focus on more specific areas.

my first version of my topic is “What’s the best programing language in the world?”, that’s kind of vague. I cannot explain what’s the meaning of the “best”. It’s not suitable at all.

I just can’t cover so many things in only 2000 words paper. So I have to narrow down and focus on the specific details.
Then I change my topic to “which programming language, Java or Python is more suitable for learners”, then I put three different aspects to compare the two different languages. So problem solved.

【学到了什么学术写作技能】
I’ve learned Academic skills such as
Be critical, thinking and critical reading, critical comment.

And summarising, paraphrasing, synthesising, finding and using academic sources and how to use them in our academic work.

And something about citations and quotations.

【有趣的事情】

I’m enjoyable with my teammates in the group preparation part. That’s the most interesting part in my research process. Because we have a change to communicate with each other and exchange our ideas and opinions about the topics and tasks, we could help each other. Most importantly, my classmates are that type of the person, they’re really funny, they could always make me laugh, besides, they could talk some little jokes. we really have a good time.

Besides, we could practice our speaking skills.

【正面或负面的事情】
I think learning in PEAP4 is positive, it helped me learn a lot of writing skills, such as improve my listening and speaking skills. To be honest, I feel a little negative when my friend asks me to travel but I need to do some tasks, but in general, this lesson taught me a lot and it is meaningful for my future study.

【对于未来的帮助】
Yes it is very important and helpful

because:
It could avoid making mistakes and we have skills to write academic writing paper, beside, Improve ourselves as an independent researcher.

【下次再来，有什么不同，有什么缺点】

I just made a big mistake this time, so I won’t do that again in the future. For this time, firstly, I have my original ideas or positions about the topic, then I just wrote down my ideas in my paper, secondly I search some keywords to find out some related academic articles. However, I figure it out this is totally wrong, I just can’t find some useful information related to my ideas. I just can’t use my original ideas for supporting argument, that’s not reliable. So I have to cut down those ideas from my paper, I just waste my time to do that. So next time , I will read first , in case I can’t find anything useful sources, and this move will save me a lot of time.

Researching and finding sources? The importance of doing the reading before writing? Making use of referencing software? Deciding on a topic earlier? Deciding on a more appropriate topic? Keeping good notes of things you read?

【有问题的文献】

there are so many academic articles, which are just too long to read. Besides there are some complex sentences that I can’t easily understand, so I have to waste my time to check those words in the dictionary. And most importantly, I just cannot read it faster. Then I figure it out that my original idea is totally wrong after my tutor told me, I have to use the skills called “skim reading”, which means I have to only read the abstract, introduction, conclusion, every topic sentences of the main body paragraph, and search the keywords in the article. those skills could help me to decide which article should I use. So problem solved.

【最好的文献】

yeah, I just search the key words in the NU search and found one academic article called ”Students’ perspective on the first programming language” published in 2018, which is similar to my own paper, we have several same points, such as the perspective on Python and Java, I can still remember that he said Python’s simple grammar helps students concentrate on problem solving, I just can’t agree more on this point. Besides there are many useful information for my paper and several content could be used in my citations. In addition, there are researches on the programming languages. That’s pretty match for me.
it makes my arguments more stronger by using lots of evidence.

【不够学术的文献，但你还用了】

Most of them are reliable, because our tutors just asked us to search the key words on the Google Scholar or NU search, and the results are academic writing papers. But only one source I’ve used is pretty old to this topic, ”What is a good first programming language?" published in 2004 by Gupta Diwaker, it’s almost 16 years and old-fashion. I still use it because there are several description of Java is still suitable for my paper.

【文献的充足】
Xinogalos, S., Pitner, T., Ivanović, M., and Savić, M. (2018) ‘Students’ Perspective on the First Programming Language: C-like or Pascal-like Languages?’ Education and Information Technologies 23 (1), 287–302

充足的argument，evidence，research，data

【文献的不足】
Sanders, I.D. and Langford, S. (2008) ‘Students’ Perceptions of Python as a First Programming Language at Wits’. Proceedings of the Conference on Integrating Technology into Computer Science Education, ITiCSE (March 2000), 365

Yadin, A. (2011) ‘Reducing the Dropout Rate in an Introductory Programming Course’. ACM Inroads 2 (4), 71–76

their researches both have weaknesses because they did not take gender and countries into account. It could be argued that learners whose native language is English tend to prefer Python because of its English-like grammar, and vice versa.

【结论是什么】

（改写一下）Although Java is popular and it has a large number of application scenarios. It is not appropriate for beginners such as children, teenagers, college freshmen, and some individuals who have never learned to program. In contrast, Python is more suitable for them because of its quick-start feature and simplicity. Furthermore, it could be the most powerful language shortly because of the rise of the AI industry.
On the other hand, users could select a suitable language based on their purpose. If learners choose the AI or machine learning direction, it is apparent that Python is more suitable. Java seems now more advantageous for back-end development because there is a larger share of the back-end market of its legacy systems, Java would be a better choice for learners who want to work on the back-end services.

【后果与建议】

If Java is still difficult to learn, it’s quite unfriendly to beginners. so maybe fewer and fewer people would choose to learn Java, which would lead to the continuous decline of the market share of Java. So I recommend Java developers have to see the Python’s challenges and they really need to make Java easier to learn and attract more beginners.

【解释别人的立场，以及你自己的立场】

One of positions in my paper is “There are a large number of Java courses in the universities and colleges because Java is more simple than C++”, but I don’t think so. I have several years working experience about development of Java. I know that Java is not simple at all. When I learn Java, I spend lots of time and I figure that out there are so many complex ideas and concepts out there, and they are hard to explain and understand. It’s easy to misunderstand those concepts. It really drives my crazy. However, Python is more simple because of several reasons, such as English-like grammar, or something like that.

【你的立场变过吗】

Yes, I’ve changed my own position about Java is more suitable for beginners. Because I have several years of working experience in Java development. In other words, I am familiar with Java. Before I wrote my paper, I thought Java was the best. But I changed my initial idea after I read some academic papers
about Java and Python. I figure it out that Java has some disadvantages and Python is easier to learn.

【多大程度不同意，如何批判，能反驳吗】

Yeah, I don’t agree Java is simple and more suitable for beginners with no programming experience. In deed, Java is difficult to learn and there are a large number of complex concepts to learn, which drives beginners crazy. But what if the learners’ long-term goal is to learn more complex programming language, then that should be OK, because Java is a good enough transition for other new language because Java’s basic programming concepts, such as “inherit”.

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