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