Daphne Koller: What we're learning from online education

668,092 views ・ 2012-08-01

TED


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00:00
Translator: Morton Bast Reviewer: Thu-Huong Ha
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譯者: 瑞文Eleven 林Lim 審譯者: Adrienne Lin
00:15
Like many of you, I'm one of the lucky people.
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就像許多人一樣,我是很幸運的。
00:19
I was born to a family where education was pervasive.
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我出生在書香世家,
00:22
I'm a third-generation PhD, a daughter of two academics.
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我家三代出博士, 父母都是高知識份子。
00:26
In my childhood, I played around in my father's university lab.
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我的童年都是在父親的 大學實驗室玩大的,
00:30
So it was taken for granted that I attend some of the best universities,
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之後也順理成章的進了頂尖大學,
00:34
which in turn opened the door to a world of opportunity.
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為我開啓了機會的大門。
00:38
Unfortunately, most of the people in the world are not so lucky.
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不幸的是,世界上 大部分的人並非如此幸運。
00:42
In some parts of the world, for example, South Africa,
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在這世界的某些角落,例如南非,
00:45
education is just not readily accessible.
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教育並非人人可及,
00:48
In South Africa, the educational system was constructed
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南非的教育系統
是在種族隔離制度的時代 為少數白人建立的。
00:51
in the days of apartheid for the white minority.
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00:53
And as a consequence, today there is just not enough spots
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其結果是,現今卻沒有足夠的名額
00:56
for the many more people who want and deserve a high quality education.
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提供給很多想要也值得 擁有高品質教育的人。
01:00
That scarcity led to a crisis in January of this year
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今年一月在約翰尼斯堡大學,
01:04
at the University of Johannesburg.
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情況更趨惡化。
01:06
There were a handful of positions left open
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有一些正常招生 所剩餘的名額開放註冊。
01:08
from the standard admissions process, and the night before
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在登記註冊的前一晚,
01:11
they were supposed to open that for registration,
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01:13
thousands of people lined up outside the gate in a line a mile long,
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成千上萬人在大門外大排長龍,
01:17
hoping to be first in line to get one of those positions.
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希望拔得頭籌搶到這些名額。
01:21
When the gates opened, there was a stampede,
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大門一開,群眾相互踩踏,
01:24
and 20 people were injured and one woman died.
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20 個人受傷,一名婦女死亡。
01:27
She was a mother who gave her life
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她是一位為子犧牲的母親,
01:29
trying to get her son a chance at a better life.
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只為下一代有更好的將來。
01:33
But even in parts of the world like the United States
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即使在其他國家,像是美國,
01:36
where education is available, it might not be within reach.
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教育雖然存在,但不一定能得到。
01:41
There has been much discussed in the last few years
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這幾年有個很熱門的議題,
01:43
about the rising cost of health care.
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就是不斷飆高的醫療支出。
01:45
What might not be quite as obvious to people
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但民眾不易查覺的是,
01:48
is that during that same period the cost of higher education tuition
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同一時期高等教育的費用,
01:52
has been increasing at almost twice the rate,
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以將近兩倍的速度飆漲,
01:55
for a total of 559 percent since 1985.
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比 1985 年漲了 5.59 倍。
01:59
This makes education unaffordable for many people.
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對許多人來說, 高等教育因而變得遙不可及。
02:03
Finally, even for those who do manage to get the higher education,
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即使設法接受高等教育的人,
02:07
the doors of opportunity might not open.
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機會之門也沒有為他打開。
02:10
Only a little over half of recent college graduates
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近期只有略為超過一半的
02:13
in the United States who get a higher education
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美國高等教育大學畢業生,
02:15
actually are working in jobs that require that education.
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從事需受高等教育的工作。
02:19
This, of course, is not true for the students
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對於那些畢業於頂尖學府的學生, 這不成問題,
02:21
who graduate from the top institutions,
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02:23
but for many others, they do not get the value
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但對其他的許多人,
他們付出時間和努力後, 沒有獲得適當回報。
02:25
for their time and their effort.
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02:29
Tom Friedman, in his recent New York Times article,
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湯姆 • 佛里曼最近 在紐約時報的文章中,
02:32
captured, in the way that no one else could, the spirit behind our effort.
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以人所未及的方式, 掌握住我們努力的精髓
02:36
He said the big breakthroughs are what happen
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他指出所謂「重大的突破」,即是:
02:39
when what is suddenly possible meets what is desperately necessary.
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「當有迫切需求時, 有些事突然變得可能。」
02:43
I've talked about what's desperately necessary.
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我已經說過什麼是 「迫切需求(普遍的教育)」,
02:46
Let's talk about what's suddenly possible.
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接著讓我們談談 有那些事「突然變得可能」。
02:48
What's suddenly possible was demonstrated by
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在史丹佛大學的三堂大型課程,
02:51
three big Stanford classes,
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展示了什麼是「突然變得可能」,
02:53
each of which had an enrollment of 100,000 people or more.
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每堂課超過10 萬人修課。
02:57
So to understand this, let's look at one of those classes,
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要理解這點,讓我們看看其中一堂課,
03:00
the Machine Learning class offered by my colleague
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我的同事與合夥人 Andrew Ng,
03:02
and cofounder Andrew Ng.
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所開設的機器學習課 。
03:04
Andrew teaches one of the bigger Stanford classes.
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機器學習課是 Andrew 在史丹佛大學
03:06
It's a Machine Learning class,
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學生較多的課程,
03:07
and it has 400 people enrolled every time it's offered.
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每次有 400 人註冊。
03:11
When Andrew taught the Machine Learning class to the general public,
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當 Andrew 對一般大眾 線上講授機器學習時,
03:14
it had 100,000 people registered.
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有 10 萬人註冊。
03:17
So to put that number in perspective,
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從數字上來看,
03:19
for Andrew to reach that same size audience
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以 Andrew 在史丹佛大學的課堂規模,
03:21
by teaching a Stanford class,
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03:23
he would have to do that for 250 years.
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他得花 250 年 才會教到這麼多學生,
03:27
Of course, he'd get really bored.
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當然,他也會覺得單調乏味。
03:30
So, having seen the impact of this,
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了解到這樣的影響力,
03:33
Andrew and I decided that we needed to really try and scale this up,
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Andrew 與我認為 需要嘗試將規模擴大,
03:36
to bring the best quality education to as many people as we could.
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儘量給更多的人 帶來最好品質的教育。
03:40
So we formed Coursera,
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所以我們打造了線上學習網站 Coursera。
03:42
whose goal is to take the best courses
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其目標是把最好的課程、
03:45
from the best instructors at the best universities
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由頂尖大學裡的最佳講師來上,
03:48
and provide it to everyone around the world for free.
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免費提供給在全球的人們。
03:52
We currently have 43 courses on the platform
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目前我們的平臺上有 43 堂課程,
03:55
from four universities across a range of disciplines,
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來自四所大學,包含各種學科,
03:58
and let me show you a little bit of an overview
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讓我給各位看看
04:00
of what that looks like.
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它會是像什麼樣子:
04:03
(Video) Robert Ghrist: Welcome to Calculus.
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(影片)Robert Ghrist: 歡迎來到微積分課
04:05
Ezekiel Emanuel: Fifty million people are uninsured.
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Ezekiel Emanuel: 5000 萬人沒有醫療保險。
04:06
Scott Page: Models help us design more effective institutions and policies.
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Scott Page:模型幫助我們 設計更有效的體制和政策,
04:10
We get unbelievable segregation.
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我們獲得令人難以置信的分隔...
04:12
Scott Klemmer: So Bush imagined that in the future,
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Scott Klemmer: 所以布希想像,在未來
04:14
you'd wear a camera right in the center of your head.
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你會在頭上戴上相機。
04:16
Mitchell Duneier: Mills wants the student of sociology to develop the quality of mind ...
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Mitchell Duneier:米爾斯希望 研習社會學的學生增強心理素質...
04:21
RG: Hanging cable takes on the form of a hyperbolic cosine.
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RG:懸掛的電纜表現出雙曲餘弦函數
04:24
Nick Parlante: For each pixel in the image, set the red to zero.
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Nick Parlante:圖像中的每個像素, 將紅色設置為零。
04:27
Paul Offit: ... Vaccine allowed us to eliminate polio virus.
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Paul Offit: ...疫苗讓我們 得以消滅脊髓灰質炎病毒
04:30
Dan Jurafsky: Does Lufthansa serve breakfast and San Jose? Well, that sounds funny.
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Dan Jurafsky: 漢莎航空 提供早餐和聖荷西嗎?這聽上去很滑稽。
04:34
Daphne Koller: So this is which coin you pick, and this is the two tosses.
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Daphne Koller:這是你挑的硬幣, 這是兩次拋擲...
04:38
Andrew Ng: So in large-scale machine learning, we'd like to come up with computational ...
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Andrew Ng:在大規模機器學習, 我們想計算...
04:41
(Applause)
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(掌聲)
04:47
DK: It turns out, maybe not surprisingly,
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或許不令人驚訝,事實證明
04:49
that students like getting the best content
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學生們喜歡從最好的大學
04:51
from the best universities for free.
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免費得到最好的內容。
04:54
Since we opened the website in February,
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自今年二月我們開放網站以來,
04:57
we now have 640,000 students from 190 countries.
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至今我們有 64 萬名學生, 來自 190 個國家。
05:01
We have 1.5 million enrollments,
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我們有 150 萬人次的登記註冊。
05:03
6 million quizzes in the 15 classes that have launched
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在已推出的 15 堂課中, 共完成了 600 萬次小測驗。
05:06
so far have been submitted, and 14 million videos have been viewed.
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影片已有 1400 萬次的點閱。
05:11
But it's not just about the numbers,
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但不只是數字方面,
05:14
it's also about the people.
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還有人的方面,
05:15
Whether it's Akash, who comes from a small town in India
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像是來自印度小鎮的阿喀許,
05:18
and would never have access in this case
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永遠沒機會也負擔不起
05:20
to a Stanford-quality course
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接受史丹佛這樣品質的課程。
05:22
and would never be able to afford it.
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05:24
Or Jenny, who is a single mother of two
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或是像詹妮,兩個小孩的單身母親,
05:26
and wants to hone her skills
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想要磨練自己的技能,
05:28
so that she can go back and complete her master's degree.
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這樣她才可以回去完成她碩士學位。
05:31
Or Ryan, who can't go to school,
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還有瑞恩,他無法到學校,
05:35
because his immune deficient daughter
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因為他女兒有免疫缺陷,
05:36
can't be risked to have germs come into the house,
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為了不能冒險讓細菌進到房子,
05:40
so he couldn't leave the house.
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所以他不能離家去學校。
05:42
I'm really glad to say --
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我很高興地宣布,
05:43
recently, we've been in correspondence with Ryan --
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最近,我們一直在與瑞恩通信,
05:46
that this story had a happy ending.
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這個故事有一個快樂的結局,
05:48
Baby Shannon -- you can see her on the left --
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在左邊的就是小寶貝 Shannon ,
05:49
is doing much better now,
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現在過得更好,
05:51
and Ryan got a job by taking some of our courses.
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瑞恩藉由修習我們的課程 找到一份工作,
05:55
So what made these courses so different?
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是什麼讓這些課程如此不同?
05:57
After all, online course content has been available for a while.
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畢竟,線上課程已經開始了好一陣子
06:01
What made it different was that this was real course experience.
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使它不同的是,這是真正的課程體驗。
06:05
It started on a given day,
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從開課開始那天起 ,
06:06
and then the students would watch videos on a weekly basis
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學生們觀看每週的影片,
06:10
and do homework assignments.
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並且完成家庭作業。
06:12
And these would be real homework assignments
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這些是真正的作業,
06:14
for a real grade, with a real deadline.
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有真正的評分、真正的繳交期限,
06:17
You can see the deadlines and the usage graph.
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你可以看到繳交期限 和網站使用率圖表,
06:19
These are the spikes showing
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這些峰值顯示
06:21
that procrastination is global phenomenon.
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拖延是全球性的現象。
06:25
(Laughter)
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(笑聲)
06:27
At the end of the course,
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在課程結束時,
06:29
the students got a certificate.
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學生們獲得了證書,
06:31
They could present that certificate
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他們可以提出該證書,
06:33
to a prospective employer and get a better job,
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給未來的雇主並謀得更好的工作。
06:35
and we know many students who did.
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而我們知道很多學生做到了,
06:37
Some students took their certificate
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一些學生拿著他們的證書,
06:39
and presented this to an educational institution at which they were enrolled
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給他們申請入學的教育機構,
06:42
for actual college credit.
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做為實際大學學分。
06:44
So these students were really getting something meaningful
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所以這些學生從投資的時間和精力,
06:46
for their investment of time and effort.
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獲得了真正有意義的東西。
06:49
Let's talk a little bit about some of the components
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讓我們再談一些
06:52
that go into these courses.
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在這些課程中的成份:
06:54
The first component is that when you move away
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第一個成份是,
06:56
from the constraints of a physical classroom
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當你消除實體課堂的約束
06:59
and design content explicitly for an online format,
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並設計明確的線上內容,
07:01
you can break away from, for example,
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例如,你可以將獨立一小時的講課
07:04
the monolithic one-hour lecture.
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07:06
You can break up the material, for example,
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分成為到 8 至 12 分鐘 的模組化單元,
07:08
into these short, modular units of eight to 12 minutes,
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07:12
each of which represents a coherent concept.
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其中每個單元都代表一個連貫的概念,
07:15
Students can traverse this material in different ways,
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學生能用不同的方式學習這些題材,
07:17
depending on their background, their skills or their interests.
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取決於他們的背景、技能或興趣。
07:21
So, for example, some students might benefit
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例如,有些學生可以從其他學生
07:23
from a little bit of preparatory material
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可能已經學會的先備題材中得到益處。
07:26
that other students might already have.
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07:28
Other students might be interested in a particular
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有些學生可能會特別感興趣於
07:31
enrichment topic that they want to pursue individually.
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他們個別想要的豐富主題。
07:34
So this format allows us to break away
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所以這種模式使我們能夠
突破統一制式的教育模式,
07:37
from the one-size-fits-all model of education,
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07:40
and allows students to follow a much more personalized curriculum.
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並允許學生有更多個人化的課程。
07:44
Of course, we all know as educators
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當然,身為教育工作者,我們都知道
07:46
that students don't learn by sitting and passively watching videos.
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學生無法光坐著看影片就能學習。
07:49
Perhaps one of the biggest components of this effort
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也許最需要下工夫努力的部分
07:52
is that we need to have students
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是讓學生藉練習題
07:55
who practice with the material
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07:57
in order to really understand it.
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去真正理解課程。
08:01
There's been a range of studies that demonstrate the importance of this.
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已有各種研究證明此點的重要性,
08:04
This one that appeared in Science last year, for example,
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例如,去年"科學"期刊中有一篇文章,
08:06
demonstrates that even simple retrieval practice,
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談到即使只是簡單的復習,
08:09
where students are just supposed to repeat
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只需要學生回顧他們剛學過的東西,
08:12
what they already learned
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08:13
gives considerably improved results
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在之後的成就測驗,
08:15
on various achievement tests down the line
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都會有顯著的進步。
08:18
than many other educational interventions.
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分數比其他的教育干預行動都多。
08:22
We've tried to build in retrieval practice into the platform,
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我們嘗試在平臺建立回顧性的練習,
08:25
as well as other forms of practice in many ways.
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還有其他形式的題目。
08:27
For example, even our videos are not just videos.
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例如,我們的影片不光是教學,
08:31
Every few minutes, the video pauses
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每隔幾分鐘,影片會暫停並對學生提問:
08:33
and the students get asked a question.
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08:35
(Video) SP: ... These four things. Prospect theory, hyperbolic discounting,
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(影片)SP: ...這四件事。 前景理論、雙曲貼現、
08:38
status quo bias, base rate bias. They're all well documented.
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現狀偏誤、基準利率偏差。 這些都被完整的記錄下來,
08:41
So they're all well documented deviations from rational behavior.
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因此它們都是有案可稽的理性行為偏差。
08:44
DK: So here the video pauses,
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在此影片會暫停,
08:45
and the student types in the answer into the box
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在方框中打上答案後提交。
08:47
and submits. Obviously they weren't paying attention.
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很明顯他們沒有專心。
08:51
(Laughter)
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(笑聲)
08:52
So they get to try again,
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所以他們得再試一次,
08:54
and this time they got it right.
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這次就答對了。
08:56
There's an optional explanation if they want.
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這裡有題目的解說 供有需求的人參考。
08:58
And now the video moves on to the next part of the lecture.
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接著來影片才會接續下去。
09:03
This is a kind of simple question
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這類題目是我在課堂上 會提到的簡單問題,
09:04
that I as an instructor might ask in class,
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09:06
but when I ask that kind of a question in class,
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但是當我提出這種問題時,
09:09
80 percent of the students
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80% 的學生會努力 琢磨我前面所教的內容 ,
09:10
are still scribbling the last thing I said,
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09:12
15 percent are zoned out on Facebook,
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15% 的學生則沉浸在 Facebook 裡,
09:15
and then there's the smarty pants in the front row
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然後坐在前排的聰明蛋 ,
09:18
who blurts out the answer
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在其他人還沒機會思考之前, 一口氣就報出正確答案.
09:19
before anyone else has had a chance to think about it,
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09:21
and I as the instructor am terribly gratified
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為師的我當然很得意,
09:24
that somebody actually knew the answer.
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竟然有人知道答案 ,
09:26
And so the lecture moves on before, really,
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所以甚至在大多數學生 尚未注意到了我提出的問題之前,
09:29
most of the students have even noticed that a question had been asked.
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我就繼續往下講了。
09:32
Here, every single student
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但是,這裡每個問題
09:35
has to engage with the material.
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應該是每個學生都要思考回答的。
09:38
And of course these simple retrieval questions
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當然不光是這些簡單的回顧性問題,
09:40
are not the end of the story.
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09:41
One needs to build in much more meaningful practice questions,
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一個課程需要有更多有用的練習題,
09:44
and one also needs to provide the students with feedback
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也需要針對學生的回答提供回饋 。
09:47
on those questions.
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09:48
Now, how do you grade the work of 100,000 students
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問題是,如果你沒有足夠的助教,
09:51
if you do not have 10,000 TAs?
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該如何批改十萬名學生的作業?
09:54
The answer is, you need to use technology
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答案是,善用技術。
09:57
to do it for you.
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09:58
Now, fortunately, technology has come a long way,
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幸運的是,這類技術已經很成熟了,
10:01
and we can now grade a range of interesting types of homework.
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我們現在可以批改各式各樣的作業,
10:04
In addition to multiple choice
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不光是選擇題,
10:06
and the kinds of short answer questions that you saw in the video,
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和你在影片中所見的簡答題,
10:09
we can also grade math, mathematical expressions
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我們可以批改數學、 運算式及數學推導。
10:12
as well as mathematical derivations.
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10:14
We can grade models, whether it's
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我們還可以批改模型,
無論是在商業課程的財務模型,
10:17
financial models in a business class
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10:19
or physical models in a science or engineering class
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或在科學或工程課中的物理模型。
10:22
and we can grade some pretty sophisticated programming assignments.
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我們還可以批改一些 相當複雜的程式設計作業。
10:26
Let me show you one that's actually pretty simple
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讓我展示一個其實很簡單 、
10:28
but fairly visual.
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但相當視覺化的例子,
10:29
This is from Stanford's Computer Science 101 class,
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這是來自史丹佛大學的 電腦科學基礎課,
10:32
and the students are supposed to color-correct
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學生須校正這張
10:33
that blurry red image.
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暗紅色圖片的顏色。
10:35
They're typing their program into the browser,
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他們在瀏覽器中鍵入程式,
10:37
and you can see they didn't get it quite right, Lady Liberty is still seasick.
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你可以看到顏色不對, 自由女神像在暈船,
10:41
And so, the student tries again, and now they got it right, and they're told that,
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所以,現在他們搞對了,
於是學生被告知可以繼續下一個任務。
10:45
and they can move on to the next assignment.
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10:47
This ability to interact actively with the material
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與教材即時互動,
10:50
and be told when you're right or wrong
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並被告知回答正確或錯誤,
10:52
is really essential to student learning.
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對學生的學習至關重要。
10:55
Now, of course we cannot yet grade
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不過,當然我們還無法批改
10:57
the range of work that one needs for all courses.
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所有種類的作業。
11:00
Specifically, what's lacking is the kind of critical thinking work
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具體而言,我們缺少 批改批判性思維作業的能力,
11:03
that is so essential in such disciplines
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這種思維在人文社會科學、
11:05
as the humanities, the social sciences, business and others.
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商業和其他領域都極其重要。
11:09
So we tried to convince, for example,
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所以我們嘗試說服,例如
11:11
some of our humanities faculty
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人文科學教師,
11:13
that multiple choice was not such a bad strategy.
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選擇題並不是那麼差勁的題型。
11:15
That didn't go over really well.
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不過效果不佳。
11:18
So we had to come up with a different solution.
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所以我們不得不 提出另一種解決方案。
11:20
And the solution we ended up using is peer grading.
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我們最終的解決方案是同儕互評。
11:23
It turns out that previous studies show,
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比如說 Saddler 和 Good
11:26
like this one by Saddler and Good,
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之前所做的研究顯示,
11:27
that peer grading is a surprisingly effective strategy
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同儕互評是十分有效的策略,
11:30
for providing reproducible grades.
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結果與教師評分雷同。
11:33
It was tried only in small classes,
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雖然這只曾經在小規模課堂中用過,
11:35
but there it showed, for example,
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不過這裡顯示了
11:36
that these student-assigned grades on the y-axis
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學生互評的分數在 Y 軸,
11:39
are actually very well correlated
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老師批改的分數在 X 軸,
11:40
with the teacher-assigned grade on the x-axis.
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結果相當吻合。
11:42
What's even more surprising is that self-grades,
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更神的是自我評分,
11:45
where the students grade their own work critically --
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就是讓學生給自己打分數,
11:48
so long as you incentivize them properly
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只要你稍微激勵他們,
11:49
so they can't give themselves a perfect score --
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確保他們不會 隨隨便便就給自己打滿分,
11:51
are actually even better correlated with the teacher grades.
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自評結果與老師批改的成績
甚至比互評還要吻合。
11:55
And so this is an effective strategy
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因此需大規模評分時, 這是很有效的評分策略。
11:56
that can be used for grading at scale,
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11:58
and is also a useful learning strategy for the students,
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對於學生來說, 也是一個有用的學習策略,
12:01
because they actually learn from the experience.
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因為他們能從中學到了東西。
12:03
So we now have the largest peer-grading pipeline ever devised,
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因此我們有現今最大規模的互評系統,
12:08
where tens of thousands of students
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成千上萬的學生互相批改作業,
12:10
are grading each other's work,
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12:12
and quite successfully, I have to say.
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而且我必須說,這很成功。
12:15
But this is not just about students
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學生不單只是
12:17
sitting alone in their living room working through problems.
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獨自坐在客廳解答問題。
12:20
Around each one of our courses,
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我們的每個課程
12:22
a community of students had formed,
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都有學生社群,
12:24
a global community of people
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共同為知識努力的全球性社群。
12:26
around a shared intellectual endeavor.
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12:28
What you see here is a self-generated map
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這是一張系統自動產生的地圖,
12:31
from students in our Princeton Sociology 101 course,
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你看到的是普林斯頓大學
12:34
where they have put themselves on a world map,
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社會學入門課程學生的來源,
12:37
and you can really see the global reach of this kind of effort.
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參與修課的人遍及全球。
12:40
Students collaborated in these courses in a variety of different ways.
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學生在課程中 以不同的方式進行合作,
12:44
First of all, there was a question and answer forum,
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首先,我們有一個問答平臺,
12:47
where students would pose questions,
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學生可以提出問題,
12:49
and other students would answer those questions.
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然後其他學生可以回答。
12:51
And the really amazing thing is,
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最令人驚奇的是,
12:53
because there were so many students,
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因為學生太多了,
12:55
it means that even if a student posed a question
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這意味著即使有人在淩晨三點
12:57
at 3 o'clock in the morning,
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提出一個問題,
12:59
somewhere around the world,
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世界的某個角落,
13:00
there would be somebody who was awake
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總有人會醒著,
13:03
and working on the same problem.
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並正研究同一問題。
13:05
And so, in many of our courses,
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因此,在許多課程中,
13:07
the median response time for a question
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問答平臺上提問與被解答的
13:09
on the question and answer forum was 22 minutes.
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間隔時間中間值只有22分鐘,
13:13
Which is not a level of service I have ever offered to my Stanford students.
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這比我給史丹佛學生 的服務要好得多。
13:17
(Laughter)
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(笑聲)
13:18
And you can see from the student testimonials
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你可以從學生感言看到,
13:20
that students actually find
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他們發現,
13:22
that because of this large online community,
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在這個大型的線上社群,
13:25
they got to interact with each other in many ways
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可用許多方式互動,
13:27
that were deeper than they did in the context of the physical classroom.
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比在實體教室上課更密切。
13:31
Students also self-assembled,
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學生還會自組學習小組,
13:34
without any kind of intervention from us,
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我們從不干預。
13:36
into small study groups.
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13:38
Some of these were physical study groups
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其中有些是實體小組,
13:40
along geographical constraints
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受限於地域,
13:42
and met on a weekly basis to work through problem sets.
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他們每週聚會一次, 主要是進行解題。
13:44
This is the San Francisco study group,
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這是舊金山學習小組,
13:46
but there were ones all over the world.
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成員來自世界各地。
13:49
Others were virtual study groups,
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還有人組成虛擬學習小組,
13:51
sometimes along language lines or along cultural lines,
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有時會依語言或文化分組,
13:54
and on the bottom left there,
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左下角你可以看到
13:55
you see our multicultural universal study group
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我們的多元文化學習小組,
13:59
where people explicitly wanted to connect
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成員們熱切地想要
14:01
with people from other cultures.
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與其他文化的人交流。
14:04
There are some tremendous opportunities
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這種架構帶來
14:06
to be had from this kind of framework.
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絕佳的機會
14:09
The first is that it has the potential of giving us
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首先,讓我們極有可能地
14:13
a completely unprecedented look
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能用前所未有的視野
14:15
into understanding human learning.
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來瞭解人類的學習方式。
14:17
Because the data that we can collect here is unique.
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因為我們這裡 能收集到獨一無二的資料,
14:21
You can collect every click, every homework submission,
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你可以收集到每一次 點擊與提交的作業,
14:25
every forum post from tens of thousands of students.
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及成千上萬學生的貼文。
14:29
So you can turn the study of human learning
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因此,你可以將學習研究的模式
14:32
from the hypothesis-driven mode
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由假設驅動模式 (先提出假設,再設計實驗驗證)
14:34
to the data-driven mode, a transformation that,
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轉變為以資料驅動 (用線上系統收集數據來研究),
14:36
for example, has revolutionized biology.
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這類的轉變已經 徹底改變了生物科學。
14:40
You can use these data to understand fundamental questions
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你可以透過這些資料 去瞭解最基本的問題,
14:43
like, what are good learning strategies
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比如說,哪些是好的學習策略?
14:45
that are effective versus ones that are not?
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哪些是有效?哪些是無效的策略?
14:48
And in the context of particular courses,
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而在特定的幾個課程背景中,
14:50
you can ask questions
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你能提出像這樣的問題:
14:51
like, what are some of the misconceptions that are more common
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有哪些是常見的錯誤概念,
14:55
and how do we help students fix them?
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我們該如何幫助學生修正它們?
14:57
So here's an example of that,
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這裡有一個例子,
14:58
also from Andrew's Machine Learning class.
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一樣來自 Andrew 的機器學習課。
15:00
This is a distribution of wrong answers
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這是 Andrew 一題作業的
15:02
to one of Andrew's assignments.
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錯誤答案分佈圖。
15:04
The answers happen to be pairs of numbers,
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答案剛好是成對的數字,
15:06
so you can draw them on this two-dimensional plot.
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因此,你可以繪製它們的二維圖像。
15:08
Each of the little crosses that you see is a different wrong answer.
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你看到的每個小叉叉 是不同的錯誤答案。
15:12
The big cross at the top left
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左上角的大叉叉,
15:14
is where 2,000 students
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顯示有 2,000 個學生 都寫了相同的錯誤答案。
15:16
gave the exact same wrong answer.
341
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15:20
Now, if two students in a class of 100
342
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如果在 100 人的課堂中
15:22
give the same wrong answer,
343
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有兩個學生給出了 同樣的錯誤答案 ,
15:23
you would never notice.
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你肯定不會發現,
15:24
But when 2,000 students give the same wrong answer,
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不過當2000人都寫了 同樣的錯誤答案時,
15:27
it's kind of hard to miss.
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你勢必會注意到。
15:29
So Andrew and his students went in,
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因此 Andrew 和他的學生
15:31
looked at some of those assignments,
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分析錯誤的答案,
15:32
understood the root cause of the misconception,
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找到了錯誤概念的根源,
15:37
and then they produced a targeted error message
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然後他們創建了一則特別資訊,
15:39
that would be provided to every student
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專門提供給答錯的同學,
15:41
whose answer fell into that bucket,
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15:43
which means that students who made that same mistake
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這意味著那些寫出 同樣錯誤答案的學生,
15:46
would now get personalized feedback
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都可以得到個別化的回饋,
15:48
telling them how to fix their misconception much more effectively.
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告訴他們如何更有效地修正迷失概念。
15:52
So this personalization is something that one can then build
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這種個別化的東西,
15:56
by having the virtue of large numbers.
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是利用大規模數據的優勢所創建。
15:59
Personalization is perhaps
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個別化也許是
16:01
one of the biggest opportunities here as well,
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幫助我們解決
16:04
because it provides us with the potential
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一個 30 年未決問題的契機。
16:06
of solving a 30-year-old problem.
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16:09
Educational researcher Benjamin Bloom, in 1984,
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教育學者 Benjamin Bloom 在 1984 年
16:12
posed what's called the 2 sigma problem,
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提出了一個稱為 2 個標準差(sigma)問題。
16:14
which he observed by studying three populations.
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他研究三種學習類型的學生。
16:17
The first is the population that studied in a lecture-based classroom.
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第一種在課堂中 以講述法為主來學習,
16:21
The second is a population of students that studied
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第二種學生也是以講述法學習,
16:24
using a standard lecture-based classroom,
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16:25
but with a mastery-based approach,
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但是有一個精熟的門檻,
16:28
so the students couldn't move on to the next topic
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學生在繼續學習下一個主題之前,
16:29
before demonstrating mastery of the previous one.
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必須熟練掌握前一個主題。
16:33
And finally, there was a population of students
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最後一種就是
16:35
that were taught in a one-on-one instruction using a tutor.
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以一對一方式教學的學生。
16:40
The mastery-based population was a full standard deviation,
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結果在成就測驗分數上,
16:43
or sigma, in achievement scores better
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第二類學生比 單以講述法受教的第一類學生,
16:45
than the standard lecture-based class,
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高出 1 個標準差,
16:48
and the individual tutoring gives you 2 sigma
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而接受一對一教學的學生比第一類學生
16:50
improvement in performance.
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成績高出 2 個標準差。
16:52
To understand what that means,
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要理解這其中的意義,
16:53
let's look at the lecture-based classroom,
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讓我們看看以講述為主的課堂吧。
16:55
and let's pick the median performance as a threshold.
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我們把中間值當做門檻,
16:58
So in a lecture-based class,
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因此在以講述法為主的課堂中,
16:59
half the students are above that level and half are below.
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是一半對一半,
17:03
In the individual tutoring instruction,
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當教師一對一授課時,
17:05
98 percent of the students are going to be above that threshold.
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98% 的學生都高於這個門檻。
17:10
Imagine if we could teach so that 98 percent of our students
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想像如果我們教出的學生
能有 98% 在平均標準之上,
17:14
would be above average.
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2198
17:16
Hence, the 2 sigma problem.
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這就帶到 2 個標準差的問題。
17:19
Because we cannot afford, as a society,
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因為我們的社會不可能
17:22
to provide every student with an individual human tutor.
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提供每位學生一對一的教學,
17:25
But maybe we can afford to provide each student
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但是我們可以每人給一部電腦
17:27
with a computer or a smartphone.
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2019
或者一台智慧型手機。
17:29
So the question is, how can we use technology
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因此問題是, 我們如何利用科技去推動
17:31
to push from the left side of the graph, from the blue curve,
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我們擺脫左邊藍色曲線的圖像,
17:35
to the right side with the green curve?
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最後到達右邊綠色曲線的圖像呢?
17:37
Mastery is easy to achieve using a computer,
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透過電腦來達到精熟學習是很簡單的,
17:40
because a computer doesn't get tired
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因為電腦就算播放同樣的影片 5 遍,
17:41
of showing you the same video five times.
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3073
也從來不嫌累,
17:44
And it doesn't even get tired of grading the same work multiple times,
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而且多次批改同樣的作業也不會煩。
17:48
we've seen that in many of the examples that I've shown you.
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3026
在很多我舉的例子當中, 我們也看到了。
17:51
And even personalization
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就連個別化的設計
17:52
is something that we're starting to see the beginnings of,
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也正開始逐漸發展。
17:55
whether it's via the personalized trajectory through the curriculum
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不管是在課程中個別化的安排,
17:58
or some of the personalized feedback that we've shown you.
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或是一些我們展示 給大家看的個別化的回饋。
18:01
So the goal here is to try and push,
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因此我們的目標是嘗試看看,
18:04
and see how far we can get towards the green curve.
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3497
能夠多接近那個綠色的圖像。
18:07
So, if this is so great, are universities now obsolete?
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5359
好吧,如果線上學習系統這麼好, 大學是不是過時了?
18:12
Well, Mark Twain certainly thought so.
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2992
馬克吐溫顯然是如此認為,
18:15
He said that, "College is a place where a professor's lecture notes
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他說:「大學是一個教授的筆記
18:18
go straight to the students' lecture notes,
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1703
直接成為學生的筆記,
18:20
without passing through the brains of either."
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2376
並且都沒有經過兩者大腦的地方。」
18:22
(Laughter)
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(笑聲)
18:26
I beg to differ with Mark Twain, though.
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不過我不敢與馬克吐溫苟同。
18:29
I think what he was complaining about is not
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我認為他在抱怨的不是大學,
18:31
universities but rather the lecture-based format
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2750
而是以講述法為主的教學模式,
18:34
that so many universities spend so much time on.
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2784
因為這種模式在大學太普遍了。
18:37
So let's go back even further, to Plutarch,
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3159
那讓我們再往回看看, 羅馬時代的希臘作家 Plutarch 說:
18:40
who said that, "The mind is not a vessel that needs filling,
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「心智不是一個需要填充的容器,
18:42
but wood that needs igniting."
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2023
而是需要點燃的木頭。」
18:44
And maybe we should spend less time at universities
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2190
因此或許我們應該
18:47
filling our students' minds with content
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少花點時間填充學生的腦袋,
18:49
by lecturing at them, and more time igniting their creativity,
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多花些時間激發他們的創造力、
18:53
their imagination and their problem-solving skills
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想像力和解決問題的能力,
18:56
by actually talking with them.
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並透過積極與他們交流來實現。
18:59
So how do we do that?
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那我們怎麼做呢?
19:00
We do that by doing active learning in the classroom.
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我們透過主動學習來實現。
19:03
So there's been many studies, including this one,
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有很多研究表明,包括這個,
19:06
that show that if you use active learning,
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如果你使用積極學習法,
19:08
interacting with your students in the classroom,
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2416
與你的學生在課堂上互動,
19:10
performance improves on every single metric --
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2696
他們在每個項目的表現 都會不斷提升,
19:13
on attendance, on engagement and on learning
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2449
在出勤率、參與度
及標準化測驗的成績。
19:16
as measured by a standardized test.
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2055
19:18
You can see, for example, that the achievement score
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比如說,你可以看到學習成就分數
19:19
almost doubles in this particular experiment.
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在這個實驗中幾乎都翻倍了。
19:22
So maybe this is how we should spend our time at universities.
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或許這就是我們 該度過大學時光的方式。
19:27
So to summarize, if we could offer a top quality education
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最後總結一下,如果我們能免費
向世上的每個人提供高品質的教育,
19:31
to everyone around the world for free,
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1903
19:33
what would that do? Three things.
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2821
結果會怎麼樣呢?會是這樣:
19:36
First it would establish education as a fundamental human right,
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首先,教育變成了人類最基本的權利。
19:39
where anyone around the world
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世界上每個有能力和積極性的人,
19:41
with the ability and the motivation
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1921
19:43
could get the skills that they need
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都可以得到他們需要的技能,
19:45
to make a better life for themselves,
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為他們自己、家人 及社區追求更好的生活。
19:46
their families and their communities.
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1186754
2017
19:48
Second, it would enable lifelong learning.
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第二、實現終生學習的理念。
19:51
It's a shame that for so many people,
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很可惜的是,許多人
19:53
learning stops when we finish high school or when we finish college.
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3312
在高中、大學念完後 就再也不學習了。
19:56
By having this amazing content be available,
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有了內容如此豐富的系統,
19:59
we would be able to learn something new
448
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每當我們想要的時候,
我們可以學一些新知識,
20:01
every time we wanted,
449
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1136
20:03
whether it's just to expand our minds
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1329
不管是用來拓展思維,
20:04
or it's to change our lives.
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1959
或者是要改變我們的生活。
20:06
And finally, this would enable a wave of innovation,
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最後,這個系統會帶來一股創新潮,
20:09
because amazing talent can be found anywhere.
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因為到處都可能 找到有驚人天賦的人,
20:12
Maybe the next Albert Einstein or the next Steve Jobs
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或許下一個愛因斯坦或賈伯斯
20:15
is living somewhere in a remote village in Africa.
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就住在一個偏遠的非洲小鎮上。
20:18
And if we could offer that person an education,
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如果我們能為那個人提供教育,
20:20
they would be able to come up with the next big idea
457
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2356
他們可能會想出下一個極好的主意,
20:23
and make the world a better place for all of us.
458
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讓我們的世界變得更美好。
20:25
Thank you very much.
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1160
謝謝!
20:26
(Applause)
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7583
(掌聲)
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