39. 教育科技-Educational Technology.ass.txt

Hi, I’m Carrie Anne, and welcome to Crash Course Computer Science!
(｡･∀･)ﾉﾞ嗨，我是 Carrie Anne \N 欢迎收看计算机科学速成课！\N

One of the most dramatic changes enabled by computing technology
计算机带来的最大改变之一  \N 是信息的创造和传播能力

has been the creation and widespread availability of information.
计算机带来的最大改变之一  \N 是信息的创造和传播能力

There are currently 1.3 billion websites on the internet.
目前有13亿个网站在互联网上

Wikipedia alone has five million English language articles,
仅维基百科就有500万篇英文文章

spanning everything from the Dancing Plague of 1518
涵盖从"1518年的舞蹈瘟疫"

to proper toilet paper roll orientation.
到"正确的纸卷方向"

Every day, Google serves up four billion searches to access this information.
每天，Google提供40亿次搜索来访问这些信息

And every minute, 3.5 million videos are viewed on Youtube,
Youtube上每分钟有350万个视频被观看.

and 400 hours of NEW video get uploaded by users.
每分钟用户上传400小时的新视频

Lots of these views are people watching Gangnam Style and Despacito.
很多观看量都是 Gangnam Style 和 Despacito

But another large percentage could be considered educational,
但剩下的 大部分是教育型内容

like what you’re doing right now.
就像你现在看的这个.

This amazing treasure trove of information can be accessed
如今只要手机上点几下 就能访问到这些宝藏

with just a few taps on your smartphone.
如今只要手机上点几下 就能访问到这些宝藏

Anywhere, anytime.
任何时间，任何地点

But, having information available isn’t the same as learning from it.
但能获取到信息和学习不是一回事

To be clear, we here at Crash Course we are big fans of interactive in-class learning,
先说清楚，我们 Crash Course 喜欢互动式课堂学习

directed conversations, and hands-on experiences as powerful tools for learning.
课上提问，以及上手实践，它们是很棒的学习途径

But we also believe in the additive power of educational technology
但我们也相信教育型技术在课内课外带来的帮助

both inside and outside the classroom.
但我们也相信教育型技术在课内课外带来的帮助

So today we’re going to go a little meta,
今天我们要在这个教育型视频里 \N 聊教育型科技

and talk specifically about how computer science
具体讲解计算机怎么帮助我们学习

can support learning with educational technology.
具体讲解计算机怎么帮助我们学习

Technology, from paper and pencil to recent machine-learning-based intelligent systems,
从纸和笔 到用机器学习的智能系统，

has been supporting education for millennia -
科技几千年来一直在辅助教育

even as early as humans drawing cave paintings
甚至早期人类 在洞穴里画狩猎场景也是为了后代

to record hunting scenes for posterity.
甚至早期人类 在洞穴里画狩猎场景也是为了后代

Teaching people at a distance has long been a driver of educational technology.
远距离教育一直推动着教育科技的发展

For example, around 50 CE, St. Paul was sending epistles
例如公元50年左右，圣保罗就发书信 \N 给亚洲设立的新教堂提供宗教课程

that offered lessons on religious teachings
例如公元50年左右，圣保罗就发书信 \N 给亚洲设立的新教堂提供宗教课程

for new churches being set up in Asia.
例如公元50年左右，圣保罗就发书信 \N 给亚洲设立的新教堂提供宗教课程

Since then, several major waves of technological advances
从那以后，有几大技术浪潮，自称要改变教育

have each promised to revolutionize education,
从那以后，有几大技术浪潮，自称要改变教育

from radio and television, to DVDs and laserdiscs.
从广播和电视，到DVD和光碟

In fact, as far back as 1913, Thomas Edison predicted,
事实上，在1913年 托马斯·爱迪生 预测说

"Books will soon be obsolete in the schools…
"书籍很快会过时.. 用影片来教授所有知识是可能的

It is possible to teach every branch of human knowledge with the motion picture.
"书籍很快会过时.. 用影片来教授所有知识是可能的

Our school system will be completely changed in the next ten years."
学校体系将在未来十年彻底改变"

Of course, you know that didn’t happen.
当然，他的预测没有成真

But distributing educational materials in formats like video has become more and more popular.
但发布教育视频变得越来越流行

Before we discuss what educational technology research can do for you,
在讨论教育技术可以帮你做什么之前

there are some simple things research has shown you can do,
有研究表明 有些简单事情 \N 可以显著提高学习效率

while watching an educational video like this one,
有研究表明 有些简单事情 \N 可以显著提高学习效率

significantly increase what you learn and retain.
有研究表明 有些简单事情 \N 可以显著提高学习效率

First, video is naturally adjustable, so make sure the pacing is right for you,
1. 把速度调整到适合你，YouTube 的速度设置在右下角

by using the video speed controls.
1. 把速度调整到适合你，YouTube 的速度设置在右下角

On YouTube, you can do that in the right hand corner of the screen.
1. 把速度调整到适合你，YouTube 的速度设置在右下角

You should be able to understand the video and have enough time to reflect on the content.
让你能理解视频  有足够的时间思考

Second, pause!
2. 暂停！在困难的部分暂停

You learn more if you stop the video at the difficult parts.
2. 暂停！在困难的部分暂停

When you do, ask yourself questions about what you’ve watched, and see if you can answer.
问自己一些问题，看能不能回答

Or ask yourself questions about what might be coming up next,
或想想视频接下来可能讲什么 \N 然后继续播放，看猜对没有

and then play the video to see if you’re right.
或想想视频接下来可能讲什么 \N 然后继续播放，看猜对没有

Third, try any examples or exercises that are presented in the video on your own.
3. 做视频中的提供的练习

Even if you aren’t a programmer, write pseudocode on paper,
即使不是程序员，你也可以在纸上写伪代码，或试试学编程

and maybe even give coding a try.
即使不是程序员，你也可以在纸上写伪代码，或试试学编程

Active learning techniques like these
这些主动学习的技巧已被证明 \N 可以把学习效率提升10倍或以上

have been shown to increase learning by a factor of ten.
这些主动学习的技巧已被证明 \N 可以把学习效率提升10倍或以上

And if you want more information like this - we’ve got a whole course on it here.
如果想学学习技巧，有整个系列专门讲这个

The idea of video as a way to spread quality education
把高质量教育内容做成视频传播 \N 在过去一个世纪吸引了很多人

has appealed to a lot of people over the last century.
把高质量教育内容做成视频传播 \N 在过去一个世纪吸引了很多人

What’s just the latest incarnation of this idea
这个老想法的新化身

came in the form of Massive Open Online Courses, or MOOCs.
以"大型开放式在线课程"（MOOC）的形式出现

In fact, the New York Times declared 2012 the Year of the MOOC!
纽约时报宣称 2012 年是 MOOC 年！

A lot of the early forms were just videos of lectures from famous professors.
很多早期视频 直接录制著名教授上课

But for a while, some people thought this might mean the end of universities as we know them.
有段时间，有些人以为大学要终结了

Whether you were worried about this idea or excited by it,
不管你是担心还是开心，这暂时还没成为现实

that future also hasn’t really come to pass
不管你是担心还是开心，这暂时还没成为现实

and most of the hype has dissipated.
现在热度也淡去了

This is probably mostly because when you try to scale up learning
这可能是因为加大规模时 同时教百万名学生

using technology to include millions of students simultaneously
这可能是因为加大规模时 同时教百万名学生

with small numbers of instructional staff - or even none
但老师数量很少，甚至完全没有老师

- you run into a lot of problems.
- 会遇到很多问题

Fortunately, these problems have intrigued computer scientists and more specifically,
幸运的是，这引起了计算机科学家，\N 或具体一点 "教育科技家"的兴趣

educational technologists, who are finding ways to solve them.
他们在想办法解决这些问题

For example, effective learning involves getting timely and relevant feedback
比如，为了有效学习，学生要及时获得反馈

but how do you give good feedback
但如果有几百万学生，只有一名老师，

when you have millions of learners and only one teacher?
怎么提供好的反馈？

For that matter, how does a teacher grade a million assignments?
一个老师怎么给一百万份作业打成绩？

Solving many of these problems means creating hybrid, human-technology systems.
为了解决问题，很多时候需要把科技和人类都用上

A useful, but controversial insight,
一种有用 但有些争议的做法是

was that students could be a great resource to give each other feedback.
学生互相之间提供反馈

Unfortunately, they’re often pretty bad at doing so –
不幸的是，学生一般做不好

they’re neither experts in the subject matter, nor teachers.
他们既不是专家也不是老师

However, we can support their efforts with technology.
但我们可以用技术来帮助他们

Like, by using algorithms, we can match perfect learning partners together,
比如通过算法，从数百万个选择里 \N 匹配出最完美的学习伙伴

out of potentially millions of groupings.
比如通过算法，从数百万个选择里 \N 匹配出最完美的学习伙伴

Also, parts of the grading can be done with automated systems while humans do the rest.
另外，有些部分可以机器打分，剩下的让人类打分

For instance, computer algorithms that grade the
例如，给 SAT 写作部分打分的电脑算法

writing portions of the SATs have been found to be
已被证实和人工打分一样准确

just as accurate  as humans hired to grade them by hand.
已被证实和人工打分一样准确

Other algorithms are being developed that provide personalized learning experiences,
还有些算法提供个性化学习体验

much like Netflix’s personalized movie recommendations or Google’s personalized search results.
类似于 Netflix 的电影推荐 \N 或 Google 的个性化搜索结果

To achieve this, the software needs to understand what a learner knows and doesn’t know.
为了个性化推荐，\N 软件需要了解用户知道什么，不知道什么

With that understanding, the software can present the right material, at the right time,
在正确的时间提供正确的资料，

to give each particular learner practice on the things that are hardest for them,
让用户练习没理解的难的部分

rather than what they’re already good at.
而不是给出用户已经学会的内容

Such systems – most often powered by Artificial Intelligence –
这种系统一般用 AI 实现

are broadly called Intelligent Tutoring Systems.
泛称叫法是"智能辅导系统"

Let’s break down a hypothetical system that follows common conventions.
我们现在讲一个假想的辅导系统

So, imagine a student is working on this algebra problem in our hypothetical tutoring software.
假设学生在这个假想的辅导系统中，研究一个代数问题

The correct next step to solve it, is to subtract both sides by 7.
正确的下一步是两边-7

The knowledge required to do this step can be represented by something called a production rule.
我们可以用 "判断规则" 来表示这一步

These describe procedures as IF-THEN statements.
用 IF-THEN 语句来描述

The pseudo code of a production rule for this step would say
伪代码是

IF there is a constant on the same side as the variable,
*如果* 变量和常数在同一边

THEN subtract that constant from both sides.
*那么* 两侧都减去这个常数

The cool thing about production rules is that they can also be used
"判断规则" 酷的地方是也可以用来

to represent common mistakes a student might make.
代表学生的常犯错误

These production rules are called "buggy rules".
这些"判断规则"叫"错误规则"

For example, instead of subtracting the constant,
例如，学生可能不去减常数

the student might mistakenly try to subtract the coefficient.
而是去减系数

No can do!
这不行！

It’s totally possible that multiple competing production rules
学生做完一个步骤后可能触发多个"判断规则"

are triggered after a student completes a step –
学生做完一个步骤后可能触发多个"判断规则"

it may not be entirely clear what misconception has led to a student’s answer.
系统不能完全弄清 是什么原因让学生选了那个答案

So, production rules are combined with an algorithm that selects the most likely one.
所以"判断规则"会和算法结合使用，判断可能原因

That way, the student can be given a helpful piece of feedback.
让学生得到有用反馈

These production rules, and the selection algorithm,
"判断规则"+选择算法，组合在一起成为 "域模型"

combine to form what’s called a Domain Model,
"判断规则"+选择算法，组合在一起成为 "域模型"

which is a formal representation of the knowledge,
它给知识，解决步骤和一门学科 比如代数，\N 用一种"正式写法"来表示

procedures and skills of a particular discipline - like algebra.
它给知识，解决步骤和一门学科 比如代数，\N 用一种"正式写法"来表示

Domain models can be used to assist learners on any individual problem,
域模型可以用来 帮助学习者解决特定问题

but they’re insufficient for helping learners move through a whole curriculum
但它无法带着学习者 \N 以正确顺序搞定整个学科该上的所有课程

because they don’t track any progress over time.
因为域模型不记录进度

For that, intelligent tutoring systems build and maintain a student model
因此智能辅导系统 负责创建和维护学生模型

– one that tracks, among other things, what production rules a student has mastered,
- 记录学生已经掌握的判断规则

and where they still need practice.
以及还需练习的生疏部分

This is exactly what we need to properly personalize the tutor.
这正是个性化辅导系统需要的。

That doesn’t sound so hard,
听起来好像不难，

but it’s actually a big challenge to figure out what a student knows and doesn’t know
但只靠学生对一些问题的回答，\N 来弄清学生知道什么，不知道什么，是很大的挑战

based only on their answers to problems.
但只靠学生对一些问题的回答，\N 来弄清学生知道什么，不知道什么，是很大的挑战

A common technique for figuring this out is Bayesian knowledge tracing.
"贝叶斯知识追踪" 常用来解决这个问题

The algorithm treats student knowledge as a set of latent variables,
这个算法把学生的知识 当成一组隐藏变量

which are variables whose true value is hidden from
这些变量的值，对外部是不可见的

an outside observer, like our software.
比如我们的软件

This is also true in the physical world,
这在现实中也是一样的

where a teacher would not know for certain whether
老师无法知道 学生是否完全掌握了某个知识点

a student knows something completely.
老师无法知道 学生是否完全掌握了某个知识点

Instead, they might probe that knowledge using a test
老师会出考题，测试学生能否答对

to see if the student gets the right answer.
老师会出考题，测试学生能否答对

Similarly, Bayesian knowledge tracing updates its estimate of the students’ knowledge
同样，"贝叶斯知识追踪" \N 会看学生答题的正确度，更新学生掌握程度的估算值

by observing the correctness of each interaction using that skill.
同样，"贝叶斯知识追踪" \N 会看学生答题的正确度，更新学生掌握程度的估算值

To do this, the software maintains four probabilities..
它会记录四个概率

First is the probability that a student has learned how to do a particular skill.
首先是 "学生已经学会的概率"

For example, the skill of subtracting constants from both sides of an algebraic equation.
比如从代数方程的两边减去常数

Let’s say our student correctly subtracts both sides by 7.
假设学生正确将两边-7

Because she got the problem correct,
做对了

we might assume she knows how to do this step.
我们可以假设她知道怎么做

But there’s also the possibility that the student got it correct by accident,
但也有可能她是瞎蒙的

and doesn’t actually understand how to solve the problem.
没有真的学会怎么解决问题

This is the probability of guess.
这叫 "瞎猜的概率"

Similarly, if the student gets it wrong,
类似的，如果学生答错了，

you might assume that she doesn’t know how to do the step.
你可能会假设她不会做

But, there’s also the possibility that she knows it,
但她可能知道答案，只是不小心犯了个错

but made a careless error or other slip-up.
但她可能知道答案，只是不小心犯了个错

This is called the probability of slip.
这叫 "失误的概率"

The last probability that Bayesian knowledge tracing calculates
最后一个概率

is the probability that the student started off the problem
是学生一开始不会做，\N 但是在解决问题的过程中，学会了怎么做

not knowing how to do the step, but learned how to do
是学生一开始不会做，\N 但是在解决问题的过程中，学会了怎么做

it as a result of working through the problem.
是学生一开始不会做，\N 但是在解决问题的过程中，学会了怎么做

This is called the probability of transit.
这叫 "做题过程中学会的概率"

These four probabilities are used in a set of equations that update the student model,
有一组方程，会用这四个概率，更新学生模型

keeping a running assessment for each skill the student is supposed to know.
对学生应该学会的每项技能进行持续评估

The first equation asks:
第一个等式问：

what’s the probability that the student has learned a particular skill
学生已经知道某技能的概率是多少？

which takes into account the probability that it was
等式里有

already learned previously and the probability of transit.
"之前已经学会的概率"和"做题过程中学会的概率"

Like a teacher, our estimate of this probability that it was already learned previously
就像老师一样，"之前已经学会的概率"

depends on whether we observe a student getting a question correct or incorrect,
取决于学生回答问题正确与否，

and so we have these two equations to pick from.
回答正确和错误分别有2个公式

After we compute the right value, we plug it into our first equation,
算出结果之后，我们把结果放到第一个方程

updating the probability that a student has learned a particular skill,
更新"之前已经学会的概率"

which then gets stored in their student model.
然后存到学生模型里.

Although there are other approaches,
虽然存在其他方法，

intelligent tutoring systems often use Bayesian knowledge tracing
但"智能辅导系统"通常用 贝叶斯知识追踪

to support what’s called mastery learning, where students practice skills,
让学生练习技能，直到掌握

until they’re deeply understood.
让学生练习技能，直到掌握

To do this most efficiently, the software selects the
为了高效做到这点，软件要选择合适的问题

best problems to present to the student to achieve mastery,
呈现给学生，让学生学

what’s called adaptive sequencing,
这叫：自适应式程序

which is one form of personalization.
个性化算法的形式之一

But, our example is still just dealing with data from one student.
但我们的例子只是一个学生的数据

Internet-connected educational apps or sites
现在有 App 或网站

now allow teachers and researchers the ability
让教师和研究人员 收集上百万学习者的数据

to collect data from millions of learners.
让教师和研究人员 收集上百万学习者的数据

From that data, we can discover things like common pitfalls and where students get frustrated.
从数据中可以发现常见错误\N 一般哪里难倒学生

Beyond student responses to questions,
除了学生的回答，还可以看回答前暂停了多久

this can be done by looking at how long they pause
除了学生的回答，还可以看回答前暂停了多久

before entering an answer, where they speed up a video,
哪个部分加速视频，

and how they interact with other students on discussion forums.
以及学生如何在论坛和其他人互动

This field is called Educational Data Mining,
这个领域叫 "教育数据挖掘"

and it has the ability to use all those face palms and "ah ha" moments
它能用上学生所有的"捂脸"和"啊哈"时刻

to help improve personalized learning in the future.
帮助改善未来的个性化学习

Speaking of the future, educational technologists have often
谈到未来，教育技术人员经常从科幻小说中获得灵感

drawn inspiration for their innovations from science fiction.
谈到未来，教育技术人员经常从科幻小说中获得灵感

In particular, many researchers were inspired by the future envisioned in the book
具体来说，Neal Stephenson 的"钻石时代"这本书\N 激励了很多研究人员

"The Diamond Age" by Neal Stephenson.
具体来说，Neal Stephenson 的"钻石时代"这本书\N 激励了很多研究人员

It describes a young girl who learns from a book
里面说一个年轻女孩从书中学习

that has a set of virtual agents who interact with her
书中有一些虚拟助手会和她互动，教她知识

in natural language acting as coaches, teachers,
书中有一些虚拟助手会和她互动，教她知识

and mentors who grow and change with her as she grows up.
这些助手和她一起成长

They can detect what she knows and how’s she’s feeling,
直到她学会了什么，以及感觉如何，

and give just the right feedback and support to help her learn.
给她正确的反馈和支持，帮助她学习

Today, there are non-science-fiction researchers, such as Justine Cassell,
如今 有非科幻小说研究者，比如 贾斯汀卡塞尔，

crafting pedagogical virtual agents
在制作虚拟教学助手

that can "exhibit the verbal and bodily behaviors found in
助手可以"像人类一样沟通 有人类一样的行为

conversation among humans, and in doing so, build trust,
在陪伴过程中和学习者建立信任，

rapport and even friendship with their human students."
相处融洽，甚至和人类学生成为朋友"

Maybe Crash Course in 2040 will have a little John Green A.I. that lives on your iPhone 30.
2040年的"速成课" \N 可能会有一个 John Green AI，活在你的 iPhone 30 上

Educational technology and devices are now moving off of laptop and desktop computers,
教育科技和设备 \N如今在逐渐扩展到笔记本和台式电脑之外

onto huge tabletop surfaces, where students can collaborate in groups,
比如巨大桌面设备，让学生可以团队合作

and also tiny mobile devices, where students can learn on the go.
以及小型移动设备，让学生路上也能学习

Virtual reality and augmented reality are also getting people excited
"虚拟现实"和"增强现实"也让人们兴奋不已

and enabling new educational experiences for learners –
它们可以为学习者提供全新的体验 -

diving deep under the oceans, exploring outer space,
深潜海洋，探索太空，

traveling through the human body, or interacting with cultures
漫游人体，或是和现实中难以遇见的生物互动

they might never encounter in their real lives.
漫游人体，或是和现实中难以遇见的生物互动

If we look far into the future, educational interfaces might disappear entirely,
如果猜想遥远的未来，教育可能会完全消失，

and instead happen through direct brain learning,
直接在大脑层面进行

where people can be uploaded with new skills, directly into their brains.
把新技能直接下载到大脑

This might seem really far fetched,
这看起来可能很遥远，

but scientists are making inroads already - such as detecting
但科学家们已经在摸索 - 比如

whether someone knows something just from their brain signals.
仅仅通过检测大脑信号，得知某人是否知道什么

That leads to an interesting question:
这带来了一个有趣的问题：

if we can download things INTO our brains,
如果我们可以把东西下载到大脑里

could we also upload the contents of our brains?
我们能不能上传大脑里的东西？

We’ll explore that in our series finale next week about the far future of computing.
下周的最后一集，我们会讨论计算的未来

I'll see you then.
到时见