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(单词翻译:双击或拖选)
Like many of you, I'm one of the lucky people. I was born to a family where education was pervasive1. 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.
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 scarcity2 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 registration3, 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.
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.
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.
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 desperately4 necessary. I've talked about what's desperately necessary. Let's talk about what's suddenly possible.
What's suddenly possible was demonstrated by three big Stanford classes, each of which had an enrollment5 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 enrolled6 every time it's offered. When Andrew taught the Machine Learning class to the general public, it had 100,000 people registered.
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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.
So, having seen the impact of this, Andrew and I decided7 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 instructors8 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 overview10 of what that looks like.
Ezekiel Emanuel: Fifty million people are uninsured.
Scott Page: Models help us design more effective institutions and policies. We get unbelievable segregation12.
Scott Klemmer: So Bush imagined that in the future, you'd wear a camera right in the center of your head.
Mitchell Duneier: Mills wants the student of sociology to develop the quality of mind ...
RG: Hanging cable takes on the form of a hyperbolic cosine.
Nick Parlante: For each pixel in the image, set the red to zero.
Dan Jurafsky: Does Lufthansa serve breakfast and San Jose? Well, that sounds funny.
Daphne Koller: So this is which coin you pick, and this is the two tosses.
Andrew Ng: So in large-scale machine learning, we'd like to come up with computational ...
(Applause)
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.
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 deficient14 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.
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 spikes15 showing that procrastination16 is global phenomenon.
(Laughter)
At the end of the course, the students got a certificate. They could present that certificate to a prospective17 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.
Let's talk a little bit about some of the components19 that go into these courses. The first component20 is that when you move away from the constraints21 of a physical classroom and design content explicitly22 for an online format23, you can break away from, for example, the monolithic24 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.
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 considerably25 improved results on various achievement tests down the line than many other educational interventions26.
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.
(Video) SP: ... These four things. Prospect18 theory, hyperbolic discounting, status quo bias28, base rate bias. They're all well documented. So they're all well documented deviations29 from rational behavior.
DK: So here the video pauses, and the student types in the answer into the box and submits. Obviously they weren't paying attention.
(Laughter)
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 instructor9 might ask in class, but when I ask that kind of a question in class, 80 percent of the students are still scribbling31 the last thing I said, 15 percent are zoned32 out on Facebook, and then there's the smarty pants in the front row who blurts33 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.
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.
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 blurry34 red image. They're typing their program into the browser35, and you can see they didn't get it quite right, Lady Liberty is still seasick36. 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 actively37 with the material and be told when you're right or wrong is really essential to student learning.
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 faculty38 that multiple choice was not such a bad strategy. That didn't go over really well.
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 pipeline39 ever devised, where tens of thousands of students are grading each other's work, and quite successfully, I have to say.
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.
Students collaborated40 in these courses in a variety of different ways. First of all, there was a question and answer forum41, 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.
(Laughter)
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 intervention27 from us, into small study groups. Some of these were physical study groups along geographical42 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 multicultural43 universal study group where people explicitly wanted to connect with people from other cultures.
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 unprecedented44 look into understanding human learning. Because the data that we can collect here is unique. You can collect every click, every homework submission45, 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 transformation46 that, for example, has revolutionized biology. You can use these data to understand fundamental questions like, what are good learning strategies that are effective versus47 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?
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.
So this personalization is something that one can then build by having the virtue48 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 deviation30, or sigma, in achievement scores better than the standard lecture-based class, and the individual tutoring gives you 2 sigma improvement in performance.
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.
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 trajectory49 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.
So, if this is so great, are universities now obsolete50? 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."
(Laughter)
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 vessel51 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.
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 standardized52 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.
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.
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.
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.
Thank you very much.
(Applause)
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1
pervasive
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| adj.普遍的；遍布的，(到处)弥漫的；渗透性的 | |
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scarcity
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| n.缺乏，不足，萧条 | |
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registration
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| n.登记，注册，挂号 | |
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desperately
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| adv.极度渴望地，绝望地，孤注一掷地 | |
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enrollment
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| n.注册或登记的人数；登记 | |
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enrolled
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| adj.入学登记了的v.[亦作enrol]( enroll的过去式和过去分词 )；登记，招收，使入伍（或入会、入学等），参加，成为成员；记入名册；卷起，包起 | |
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decided
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| adj.决定了的，坚决的；明显的，明确的 | |
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instructors
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| 指导者，教师( instructor的名词复数 ) | |
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instructor
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| n.指导者，教员，教练 | |
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overview
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| n.概观，概述 | |
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calculus
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| n.微积分；结石 | |
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segregation
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| n.隔离，种族隔离 | |
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vaccine
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| n.牛痘苗，疫苗；adj.牛痘的，疫苗的 | |
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deficient
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| adj.不足的，不充份的，有缺陷的 | |
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spikes
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| n.穗( spike的名词复数 )；跑鞋；（防滑）鞋钉；尖状物v.加烈酒于( spike的第三人称单数 )；偷偷地给某人的饮料加入（更多）酒精（ 或药物）；把尖状物钉入；打乱某人的计划 | |
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procrastination
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| n.拖延，耽搁 | |
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prospective
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| adj.预期的，未来的，前瞻性的 | |
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prospect
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| n.前景，前途；景色，视野 | |
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components
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| （机器、设备等的）构成要素，零件，成分； 成分( component的名词复数 )； [物理化学]组分； [数学]分量； （混合物的）组成部分 | |
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component
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| n.组成部分，成分，元件；adj.组成的，合成的 | |
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constraints
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| 强制( constraint的名词复数 )； 限制； 约束 | |
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explicitly
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| ad.明确地，显然地 | |
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format
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| n.设计，版式；[计算机]格式，DOS命令:格式化(磁盘)，用于空盘或使用过的磁盘建立新空盘来存储数据；v.使格式化，设计，安排 | |
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monolithic
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| adj.似独块巨石的；整体的 | |
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considerably
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| adv.极大地；相当大地；在很大程度上 | |
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interventions
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| n.介入，干涉，干预( intervention的名词复数 ) | |
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intervention
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| n.介入，干涉，干预 | |
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bias
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| n.偏见，偏心，偏袒；vt.使有偏见 | |
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deviations
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| 背离，偏离( deviation的名词复数 )； 离经叛道的行为 | |
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deviation
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| n.背离，偏离；偏差，偏向；离题 | |
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scribbling
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| n.乱涂[写]胡[乱]写的文章[作品]v.潦草的书写( scribble的现在分词 )；乱画；草草地写；匆匆记下 | |
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zoned
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| adj.划成区域的，束带的v.（飞机、汽车等）急速移动( zoom的现在分词 )；（价格、费用等）急升，猛涨 | |
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blurts
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| v.突然说出，脱口而出( blurt的第三人称单数 ) | |
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blurry
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| adj.模糊的；污脏的，污斑的 | |
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browser
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| n.浏览者 | |
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seasick
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| adj.晕船的 | |
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actively
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| adv.积极地，勤奋地 | |
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faculty
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| n.才能；学院，系；(学院或系的)全体教学人员 | |
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pipeline
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| n.管道，管线 | |
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collaborated
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| 合作( collaborate的过去式和过去分词 )； 勾结叛国 | |
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forum
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| n.论坛，讨论会 | |
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geographical
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| adj.地理的；地区(性)的 | |
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multicultural
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| adj.融合多种文化的，多种文化的 | |
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unprecedented
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| adj.无前例的，新奇的 | |
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submission
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| n.服从，投降；温顺，谦虚；提出 | |
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transformation
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| n.变化；改造；转变 | |
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versus
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| prep.以…为对手，对；与…相比之下 | |
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virtue
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| n.德行，美德；贞操；优点；功效，效力 | |
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trajectory
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| n.弹道，轨道 | |
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obsolete
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| adj.已废弃的，过时的 | |
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vessel
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| n.船舶；容器，器皿；管，导管，血管 | |
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standardized
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| adj.标准化的 | |
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