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Chris Roe: I would like to kick us off with our lunch and keynote speaker. We're really very pleased and fortunate to have with us Bob Tinker. Bob is the founder of the Concord Consortium and yes, that is at Concord, Massachusetts. And Bob has pioneered constructivist approaches to education, particularly focusing on novel uses of education technology and science and that's a theme that we may have heard once or twice during the last day or more, but he has a really unique perspective. He's earned his Ph.D. from MIT in experimental low temperature physics and learned about education on the job at a historically black college in the 1960s, must have been a very interesting time. I do remember it a little bit but I can't claim that I remember a lot about the 1960s. So I'm looking forward to hearing more about that. In the 1980s, Bob developed the idea of equipping computers with probes for realtime measurements and of using the network for collaboration, for collaborative student data sharing and investigations. Sounds like he was a little bit ahead of his time. In 1994, he started the Concord Consortium so that he could concentrate on applications of technology to improve the quality of education. His early work at Concord pioneered applications of portable computers to education and the use of the web for professional development in teaching. One of the early projects he created was the Virtual High School which has been spun out as an independent non-profit that continues to be a trend setter in online learning. His current research includes educational applications of portable computers, the development and testing of computational models in education and the development of smart graphs that are able to interact with students about important features of a graph. He's also involved with policy formation relating to education technology in his role in improving STEM education worldwide and serves as the chair of the Virtual High School. So with that I'm pleased to introduce Bob and I think he's gonna give us a great presentation and he's gonna take a little bit of Q and A after that. So please welcome Bob Tinker to the stage. Bob Tinker: Well, thank you. It's a pleasure to be here. This is an important event and I'll try not to keep you away from the rest of the event. I realized you're running a little slow so I'm gonna roar through some slides that I prepared and get on with it. I'll start by saying some things that probably don't need to be said. Is there a STEM problem? Well, yes. In many respects, our STEM educational system looks sort of like that engine. And of course you've all seen these international comparisons. He's the PISA '09 report, 15-year-olds, it's showing part of the whole report with stopping at the United States and showing all the ones that are above us. The ones in red are significantly better than us. Interestingly enough, more in math than in science but still, we are way down in the middle. And if that isn't enough to convince you, I quickly founded a 31 reports. Does Baskin and Robbins get out here? Thirty-one reports since '07 on various aspects and policy recommendations relative to STEM education. Some of them with wonderfully imaginative names like Prepare and Inspire and Innovation America and Rigor at Risk. I love that, it's alliterative. It seems to me that about the only thing these reports can do is come up with great verbiage. Let me pick that first one though, one that I was personally involved with and I think was terribly important, the PCAST report. PCAST is the President's Council on, oh boy what is it, Science and Technology. And I paraphrased the name of the report, it's the STEM for America's Future. It really is a complete plan thought through over the course of year and a half with experts from academia and politics and K-12 education. It's a complete plan that involves standards, teacher education, teacher preparation, teacher retention, educational technology--very strong emphasis on educational technology, that's one of the reasons I like it--and informal education and recommending new STEM specialty schools. So--oh and also emphasizing the importance of the sort of thing that you're trying to do here today, which is to provide leadership locally and nationally. Interestingly enough, they didn't, ever really quite release the price tag on this but we had a lot of discussions in the meetings about how much it would cost to implement all those plans and really transform STEM education in the United States. And the cost is somewhere between 100 and 300 million dollars a year for 10 years. Now that seems like a lot of money, but it is 0.005--if you take the middle number, 200,000,000--it's 0.005 of the federal budget and it's 0.3% of the Department of Education budget at the U.S. Department of Education. Also if you look at it in terms of California perspective, the equivalent cost, about a per capita basis for the recommendations in that transforming STEM education, would be something of the order between 12 and 36 million dollars a year for 10 years, which is only 0.03% of the California state budget, only 0.07% of the California education budget. So what's wrong? Why are we fiddling around? We have all the advice, we have a cost that is small compared to anything but yet, nevertheless as is pointed out over and over again, this is a severe test of our national security. We are essentially ruining our future by not paying attention to this issue. But there really appears, as you know, as we all know, the will is lacking at the federal, state, local level to make any change in the priorities. The only will seems to be to slash expenses. What could be done? What's different this time? Well I would argue that the new kid on the block is technology. Technology costs are dropping, technology power is improving, Moore's Law is true and we're doing amazing things with the education. I'm sorry, I keep flipping back and forth. We're heading to the area of technology cost roughly costing about one percent. We're not quite there yet, maybe it's one and a half, one and a half percent of the cost of education. Computers, just to give you some numbers, computers, real computers, not just, you know, abbreviated computers like tablets, are available now for education in the vicinity of $500. If you amortize that over five years, that's $10 a year compared to the average annual cost of education, which is roughly $10,000. In this state, it's a little below $10,000, I'm talking K-12. Nationwide, it's a little over $10,000 and New York City, it's $18,000 per student per year. Furthermore, incredible resources are available online and there's an incredible amount of knowhow right here in California. Astounding how much information there is available that could really transform education, STEM education. We, sort of my 3,000-mile distant view of things here in California, we regularly collaborate with SRI Incorporated, WestEd, the Exploratorium, the Lawrence Hall of Science, San Diego State University, Berkeley, UCLA, Stanford and we've applied for, involved with and had funding from Hewlett Foundation, Google Foundation, Intel, Noise. These are all resources that you have and as I heard just yesterday, I'm sure there's many other resources available just in this room with Chevron and Bechtel, who I had dinner with last night. Question is, where are we going? Are we an advanced nation or a third world country? Newsflash: Argentina is giving every high school student a real computer. They're buying 2 million of these. They're rugged, they're secure and they have a touch screen, they have lots of zorch, that is power, computational power, lots of content and included in the package is professional development. This is Argentina, Brazil, Venezuela, Portugal, Turkey. A number of what we would think of as developing countries are way ahead of us on this and they're gonna take over, exceed us. We're gonna go further down on that piece of chart pretty soon. The strategies that work are known and available here today. Things that we've been working on that we think are terribly important in science and engineering and mathematics education is getting your hand on real stuff, on real numbers, on real relationships. And they come from three main sources: from probes and sensors that give you real live data and bring it in to the computer, from databases that exist out in the larger world and from computational models that are sufficiently flexible that you can experiment with them. All of these approaches change the educational environment. The models can teach deep concepts, the probes enable collection and analysis and get rid of that stigma, the Oh the kids are working on the computer, they're getting out of the real lab, they're not doing anything real, they're just getting lost in cyberland. And both support hands-on, minds-on, thoughtful projects investigations. Exactly the sorts of things that are called for in the new science frameworks, put more emphasis than ever on deep thinking and student-based project work. The technology can transform how students interact and learn through collaboration and communication, not from being told, from guided inquiry, we don't just throw out things and expect students to invent Newton's Laws or quantum mechanics by themselves, but we guide them along the way, we give them hints, help them out and encourage them to think about it. Use vivid visualizations that they can interact with, that create images that stay in their minds, games, gaming approaches, game-like learning environments but ones that teach, not just keep, you know, involved and active and working on their brain stem, and provide lots of practice. There's all kinds of things that technology brings to the learning experience. We know there's thousands of articles and literature, experiments on what works and what doesn't work. We know how to build effective computer-based technology learning. It also provides better assessment. We can do detailed real time assessments. We can feed it back to the teachers in real time so teachers can, they're not passive observers watching kids in a classroom and then reading the newspaper. They, through the assessments that can be built into materials, they continue to be an active participant and have a much better clear view of where kids are, what they're thinking and where they need help. It can link in both formal and informal education, you can do long scale, large projects that take a lot of time that are more appropriately done outside the classroom and they can be engaging and involve kids worldwide and link directly back into the goals and objectives of what's being taught in the school. They also provide just in time professional development for teachers. One of the big problems with any major change in education of course is we all as teachers have learned a lot of what we know about our subject by teaching it over and over and over again. One of the beautiful things about technology is it allows you to do material more deeply and make deeper connections that won't be familiar for a lot of people. So the ability to provide just in time professional development as you're going along through a curriculum is an important part of the whole answer. And of course, the collaboration that is possible online is just amazing. I'm writing a proposal right now for what's called Programs of Study for students that are interested in career kinds of areas and getting them started on their career while they're still in high school. Now they're not gonna learn howthey don't know a detail thing what career they want to get into but they might know that they're vaguely interested in biotechnology, for instance, and we can offer courses to rural schools in nowhere that might only have two or three students. Doing that online provides new career opportunities and opens up careers for other underserved populations. One example of the many things you can do with online. So let me get away from generalities for a minute here and talk, I'll give you a couple of examples because these are things we're working on, things that are great fun. One thing that I like to point out over and over and over again is that through visualizations and interaction, we can give conceptual understanding to students that otherwise is relegated to abstract formulas and what have you and basically not taught because it seems out of touch, out of the capability of the students. But we can allow students to go a little deeper and through going deeper, unify concepts that otherwise seem disparate and non-unified. So for instance, here's three topics that are normally just taught totally separately and almost by road: solubility of materials, self-assembly of biological materials and biological folding. I have an example I'll show you in a minute. It stars with the oil spill, the gulf oil spill and goes a little deeper into that and talks about detergents and how detergents work and whether the detergents that were applied at the wellhead were valuable or toxic or what their role is in that, why it was used and what the science is behind it. Well the science has to do with intermolecular attractions, not bonding but just intermolecular attractions. If you go that a little deeper and do that, then all of a sudden these other three concepts which were otherwise sort of disparate and unlinked are now linked by the same underlying science. Isn't that a better way to teach? Let me show you as short as possible example of how that actually works. Here's a model. This is highly stylized showing the sky above and an oil film showed in yellow over blue water. I can shake this and some droplets come up. This is the usual thing you know of wave action will cause film of oil to break up. This is sort of imaginary view, almost microscopic size. But over a while, the oil droplets recombine and are no longer dispersed. Well what if we add different kinds of atoms to this mix? These are gonna start out as mystery atoms and if you watch them for a while, I'm giving you a very abbreviated view of a couple of days of experimenting, you will discover that these magenta atoms, molecules, try to go into one place. And I also have some green ones here and I'll scatter them around and let the model go for a while. It won't be immediately obvious to you but yeah, if you squint hard, you can see the magenta ones pretty much like to be in the oil and the green ones pretty much like to be in water. It's all statistical, it's random motion, but if you wait long enough it's pretty clear. And this doesn't have much effect on the dispersion of the oil. You get the molecules in and out and so forth. And if you want to see what these mystery molecules are, this is what they look like. The ones that like oil are long chain and uncharged and the ones that like the water are charged atoms, molecules, represented by these little red dots. So then things get interesting here when you add the black ones and you put them around, these are more mystery molecules, I'll scatter them around a bit. Let the model run for a while. And these have a funny property. They end up at the surface between the oil and the water. And then you sort of think about, why is that possible? How would you make a molecule that would like to be in the oil and in the water at the same time? And you get kids thinking about it and so forth. And of course, after a little thought, you try to see, imagine what it is and in fact they're just a combination of the types of molecules that like water and the ones that like oil and they end up at the surface. And interestingly enough, if you let this run long enough and shake it up and I'm gonna add lots more, I can stabilize these droplets. I got to cheat a little bit and put them preferentially around the oil. But if you let it go for a while, you see suddenly, something very interesting. The oil droplets cannot recombine because they are physically interfered with and in fact, they won't either recombine with each other or with the surface. And so if you shake this one up enough and work on it long enough, you disperse all the oil. That's the role of a detergent and it's a nice explanation of how it works in an interactive, guided, exploratory kind of environment. We like to go down to the atomic and molecular level. It's an area that hasn't been traditionally much part of the curriculum except in Chemistry and yet, it is critical in all different areas. It's basic physics and applied in chemistry and engineering and biology. Here's another example of self-assembly. This looks a little bit like a movie, but this is a computational model. I can move these things around, I can play with them and experiment with them. And when I let the model run, just random, oh and I can also add charges and what have you. Each one of these has a plus charge on one side and a minus charge on another. Other than that, it's just random motion and you get self-assembly. And this is, I should point out, how we use computational models in an educational environment. This is one page out of this. You can see what nine page activity about molecular self-assembly. And students work through it, they're given some background information, motivation and but the real key that makes this different than anything else is that you have these computational models that you can play with and experiment and share and learn from. And build in is assessment, one of the kinds of assessments we like particularly is taking pictures, you can write on it, annotate it and so forth and save it. And then when you get down to one of these kinds of questions that asks for a picture, you drop in your picture of the model and your explanation that supposedly illustrates the question, a whole new kind of assessment. Turns out pictures are fun for kids to work with and it's easy for teachers to grade. You can look at a second to see if they've captured a picture of the model in the state that you want them to show. And of course, game-like environments are great fun. We've worked for many years with classical and modern genetics. We've chosen for many reasons the genetics of dragons. Dragon genetics is well-known, at least by us. It also happens to be a lot simpler than human genetics and it gets around some of the concerns that people might have. Although we're not, we don't only do genetics of dragons, but it's a great way to start. So this is a game-like environment, they're given challenges, they have to get their skills up, they share with other students and background. And eventually, through a long sequence, they are led up to doing modern genetics. We connect in with the genome databases and they can do sort of, if this odd thing is associated with this genetic trait, maybe they're near each other on the DNA and we use real DNA from mice and they do explorations on this. So essentially they're up to the point of modern research and molecular biology using these huge databases that have all this information in them. So it's justwait a minute, I can't talk. Here we go. There's fascinating deep things that can be done with technology. One thing that I find frustrating is that almost all the work that we're funded to do and the work that is done by our friends and colleagues is kind of pointillistic. You get a little bit here, a little bit here and often we end up you know, not putting them together to make all the pieces work together. One of the great virtues of a textbook is the fact that it's organized, that it has a sequence, it has a beginning and a middle and an end and there's a lot of thought that's gone into how you introduce concepts, how you reach, at least in a good one, how you build back and review and work forward. We can't do that just by these pointillistic things. In fact, what we're ending up doing is throwing back on teachers a lot of the responsibility for making the curriculum coherent. We have these great individual things. Is there a way to take advantage of the technology and take advantage of the fact that we don't have any money to do this in a collaborative way that is pulled together the best of what teachers have learned through teaching and the best technology that we have and do what a lot of companies have started doing, is crowdsourcing and is depending on your users to create the stuff or make it better. One of the interesting things that I've learned about Intel is that they make computers for education. They make so called reference designs. They don't sell them. They make a reference that other people can use and adapt for their own needs. So maybe there's an analogy here that we could follow that we could create a reference design that's a curriculum that has all these pieces really well integrated and used but engage teachers and educators in customizing that material, making it work for their students in their environments, slowing it down, speeding it up, changing the vocabulary, changing the language, whatever. So they should have the reference design and many different versions of it. Now that could just lead to complete chaos unless there was an evaluation component to this. A peer review system that allows people to comment as is so often done in products where evaluate materials and put evaluative information associated with these customized materials and connect that with the recommender system so that you know, after a while, the system knows what your preferences are and we'll recommend. Teachers that have liked this will like that. Oh yes, and part of the important part of customization is that it's an extremely powerful professional development strategy. If you need to go in and change something, you need to understand it in the first place. You have to understand the math or science or engineering that's in back of it and you have to draw from your own, think carefully from your own experience what we make that, how you would expose that to students in a better way. Is that a possible way to proceed in this era of highly constrained resources? Is this our future or is this? So something to think about, something to dream about. This is all coming very fast like those trains. By the way, a lot of this material I've shown and much more, hundreds of them are available free online from our website, simply Concord.org. You should all have had found a newsletter in your packet from us and there's information on those. If you want to receive that as a hard copy, we make it available twice a year, just send me email, Bob@Concord.org. That one I was showing about, the detergents we've written up as an article and it's there simply at Deeply-Digital-Curricula-Detergents, all with hyphens. I've recently retired from the Concord Consortium. That means that I don't have to do any of the administrative work, it's so nice. Chad Dorsey is our new CEO. The work on the detergents has been done in collaboration with a postdoc at Berkeley, David Miller. We work extremely closely with a group at, Marcia Linn's group at Berkeley. And then I have to quickly say we got a lot of those pictures from Flickr and there's the acknowledgements, you got all that and my favorite picture. Thank you. So I did it with one minute to spare and I'll take some questions other than wiggling my adaptor. Roe: What's the biggest problem? Tinker: The biggest problem we run into is bandwidth and essentially technological capability at the school level. A lot of these applications we write are in Java. Java requires large downloads. You start off a class with 30 kids or 15 kids, 15 computers and everything grinds to a halt. We've also found something--it's to remind me I'm done, sorry--we've also discovered, in spite of the fact that many schools are given E-rate of connectivity, which run into schools that are so stretched for money that they've leased out their bandwidth to other users and therefore, they only have an 8th or 16th of a T1 line left and it just doesn't do the job. That frankly is somewhat of a temporary problem but right now the IT departments, the lack of IT support is a huge issue for us. More deeply of course is this problem that there's so much material in the web and it is so pointillistic and it is often very hard to distinguish good from bad. We run into this all the time with online courses. How do you tell if an online course is a good course or not? You have to take it, almost, to find out if they're using a conversation, the collaborations well, if they make a good use of the technology. And for many, many people, the answer is, Ive had a bad experience. I won't do it. So we need better vetting and better recommending systems, I think. Yeah. Jim Vanides: Jim Vanides from HP. Tinker: Oh. Vanides: Could you respond to the articles that have been published in the New York Times of late? Tinker:Yeah, last Sunday? Vanides: What do you tell them about what's really going on? Tinker: We get this all the time. This is the Whitehurst phenomenon. If technology isn't proved in a double-blind medical model, gold-standard research with thousands of students and the only thing you've changed is whether the computer is running or not, by and large a computer doesn't show any benefit, and so this reoccurs every few years. Why do we waste all this money on educational technology? The reason they get this result is, as you must appreciate, educational research is extraordinarily hard. How do you get a double-blind kind of comparison? How do you compare one person to another? The technology is great help but it isn't the answer. It's the combination of the teacher, the school environment, the students and the technology that works well in one situation and not in another. The net result is that the data are very noisy. Also, the kinds of things they're testing are relatively trivial. The paper, the article in the paper on Sunday the New York Times, you should all read it. It's compelling until you think about it. They blasted Carnegie Learning's math tutor which again is a third, a quarter, I don't know, it might even be a tenth of the total time student in a class in that algebra class is in there, he's working on the computer. So it depends entirely on the school and teacher and it is just basically elaborate drill and practice. You noticed I didn't mention much about drill and practice. I think drill and practice is a terrible use of computers and it has nothing to do with the kinds of things that might transform science and math and engineering education. Marco Molinaro: Hello, Marco Molinaro. Tinker:And by the way, thank you for offering me that. I've been, I was gonna write a letter to the editor, I just didn't have a chance yet. Yeah, okay. Molinaro: So thank you for the wonderful talk about--my name is Marco Molinaro from UC Davis, and first of all, thank you for wiggling your adaptor. My question is, you mentioned the repository idea with the crowdsourcing. I had hoped that that would be the National Science Digital Library that started out early in 2000 and showing great promise. How do you feel that such an effort, that was a huge effort, a lot of money was put into it. Tinker:Yup. Molinaro: Still there's lot of material, wonderful material there, lots of projects have been contributed to it but it hasn't become this crowdsourced approach. I don't know how many of you use it on a regular basis. Show of hands how many of you? Okay. Tinker:There you go, that's typical. Molinaro: Yeah and I just wonder your comments on that you know, such a very large-scale effort didn't go very far. Tinker: Yeah. Well it was always conceived of as an archival and it in fact spend half of its time just collecting PDFs of things, of research and what have you. Toward its end, it began working with interactive, showed to our foundation, got some interactive material in there and we're working on interactive material to be put in the NSDL, but it was too little too late. It was not designed to handle materials that students could interact with and teachers could change. It has this complicated library base betting procedure that really discourages the kind of interchange that's needed. It's too bad because I got all excited about it in the beginning. National Digital Library for Science, boy that sounds like just what we need. But it's interesting, the details matter. Okay. Thanks again.