To install click the Add extension button. That's it.

The source code for the WIKI 2 extension is being checked by specialists of the Mozilla Foundation, Google, and Apple. You could also do it yourself at any point in time.

4,5
Kelly Slayton
Congratulations on this excellent venture… what a great idea!
Alexander Grigorievskiy
I use WIKI 2 every day and almost forgot how the original Wikipedia looks like.
Live Statistics
English Articles
Improved in 24 Hours
Added in 24 Hours
Languages
Recent
Show all languages
What we do. Every page goes through several hundred of perfecting techniques; in live mode. Quite the same Wikipedia. Just better.
.
Leo
Newton
Brights
Milds

Georgia Institute of Technology Historic District

From Wikipedia, the free encyclopedia

Georgia Institute of Technology Historic District
Location225 North Avenue, Atlanta, Georgia  United States
Coordinates33°46′21″N 84°23′40″W / 33.77250°N 84.39444°W / 33.77250; -84.39444
Area9 acres (0.036 km2),[1] 12 buildings
Built1888
ArchitectMultiple
Architectural styleBeaux Arts, Colonial Revival, Romanesque
NRHP reference #78000983
Added to NRHPOctober 25, 1978

The Historic District of the Georgia Institute of Technology, also known as the Old Campus of Georgia Tech or the Hill District, is significant in the areas of architecture, education, engineering and science, as well as landscape architecture. The area is a Registered Historic Place and part of the central campus of Georgia Tech. Located in Midtown Atlanta, Georgia, United States, it is roughly bounded by North Avenue on the South, Bobby Dodd Stadium, a 55,000 seat football stadium on the East, Bobby Dodd Way on the North and Cherry Street on the West.

YouTube Encyclopedic

  • 1/5
    Views:
    1 321
    789
    1 633
    9 259
    436
  • ✪ Experiences with CS+X Majors and Curricula
  • ✪ ARC/ORNL 2019 Middle & High School Summer Math-Science-Technology Institute Final Presentations
  • ✪ Investiture Ceremony Live Webcast
  • ✪ Teaching Computer Science to All
  • ✪ Gloria Ladson-Billings "It’s ALL de jure: Turning a critical eye on the Northern Strategy"

Transcription

Thank you all, and welcome-- my name is Eran Ben-Joseph. I am professor here and also the head of the Urban Planning Department at MIT. I'm also, I would say, probably the last person to be-- recent person to be blamed for the newest, I guess, X+CS, which is in urban planning and computer science. So I guess that's why I'm here. But it is really an interesting time for us here at MIT. And what we wanted to hear is from a panel of experts to tell us a little bit about the efforts that they have done in their institutions and through their career about integrating computer science with other disciplines and to hear about what worked, what didn't work. We'll have a discussion about what are the future possibilities. I thought it was particularly interesting that Charles, who sent an email around-- I didn't know that-- to all of us, that just about a month ago, I guess Stanford decided to cancel all their CS+X courses because of low enrollment. I don't know what that means, but I think it's something we should all look at. I'm not sure how Stanford system is different from what we have done, but I think it's an interesting observation. So to help us think, and discuss, and probe these questions, we have a great panel. I'll just introduce maybe all of them right now. And so for the sake of time, a very short introduction. We have Professor Jeanette Wing, who is the Director of Data Science and Institute Professor of Computer Science at Columbia University. Before that, she was at Carnegie Mellon University. Professor Charles Isbell is the Executive Associate Dean of the School of Interactive Computing at Georgia Tech. Professor Carla Brodley is the Dean for the Khoury College of Computer and Information Science at Northeastern University. And the last person on the panel is Professor Rob Rutenbar, who is the Senior Vice Chancellor of Research at the University of Pittsburgh and also spent many years at the University of Illinois Urbana-Champaign. So thank you all, and welcome to MIT-- and Jeannette, please. Thank you very much. I am honored to be a part of this celebration. I'm a very loyal alumn to MIT. So I don't know why I'm on this panel called CS+X. I wanted to share with you a bigger picture. And it starts with-- let's get this right-- the influence of computational thinking on education. I'm going to talk about computational thinking from the government point of view, academia, both at the college level and K through 12, and then a very short mention about what industry has been doing. So it started for me with the article I wrote in 2006. It's a three-page article. Looking back on it, it's a little poetic. It's very easy reading. You should all read it if you haven't. And I want to take you back to 2004, which is when I started thinking about these ideas. 2004 was soon after the dotcom bust. Believe it or not, there was a decline in enrolments in computer science at universities and colleges throughout the US and the world-- a decline. Some schools were wringing their hands, oh woe is me. No one's going to major in computer science anymore. Where are the students? This just sounds so ridiculous now, but that's what was happening. Some schools had told me at the time that they were thinking about shutting down their computer science departments. So I felt I had to write this little article-- I called it Computational Thinking-- for many reasons. But the first was to shake up the computer science community, say, we have so much to celebrate in terms of who we are, how we think, and how we can share who we are, our concepts, our principles, and how we think with everyone else. Moreover, we still have some outstanding scientific challenges. We as a discipline are-- still have a lot of exciting hard questions to answer. So I just wanted to tell the computer science community, stop ringing your hands. I took this opportunity to send a few more messages. The second message I wanted to send is, computer science does not equal computer programming. And one reason students were not majoring in computer science is that they would take their very first computer science course-- and it was usually called Introduction to Java Programming. And you know if you're not really into programming, you take this course, you think that's what computer science is, and you decide, well, I don't-- you run away. Well, I wouldn't want to do that for my life. We weren't teaching the more foundational concepts of computer science as a field. We were teaching mastering this particular skill of programming in this particular language. And the second message I wanted to send with this three-page article is, we should be reinventing our introduction to computer science courses. And of course, that has really happened. And I'm going to mention a few schools that did that. But the last thing I really wanted to send as a message was not just to the computer science community, but to everyone. And that is, computational thinking is for everyone. And I believe there is a whole session coming up, so I'm not going to belabor that. But you'll see that's a common theme in how I've been thinking. And it's 13 years later since that article. So soon after 2006, I joined the National Science Foundation. Oh, by the way, this article was translated to Chinese, and French, and Japanese, and Korean, and German, and Portuguese, and whatnot. I joined the National Science Foundation. And I know we're supposed to speak about education, but I just wanted to put in a little plug for a research program that I basically helped to formulate my third day arrived in DC. And it was called at the time Cyber-Enabled Discovery and Innovation. And if you read the original solicitation, it says, this is about computational thinking for scientists and engineers. And so it was my way to spread computational thinking to all science and engineering disciplines. And I was able, in my seat at the National Science Foundation, to be able to reach that huge community. It was not just research where I had an impact in terms of spreading computational thinking, but we also while I was there created two educational programs that promoted computer science. And a real test of whether you made a difference at NSF is if any of that lasts beyond your tenure at NSF. And I'm happy to say that my successors, Farnam-- and Jim Kurose, he is right there-- continued on in this tradition. So there are two educational programs I wanted to point you to that just came out. And this continues to promote the importance of teaching computer science in all disciplines, all science and engineering disciplines. And just the sort of the icing on the cake to me was Barack Obama's State of the Union Address in 2016, where he basically says, NSF is going to-- I mean, where he basically says, every student should learn computer science and math skills. And I felt after he-- after his State of the Union address, I could just retire and go home. Of course, presidents change, and all sorts of things change. But in any case, I felt quite gratified after that. OK, so now back to the universities and colleges, and I wanted to say-- I hope Maria is still here to see that I have her logo up here. But in any case, a lot had happened since around 2006, sweeping across the US, colleges, Harvard, Harvey Mudd, Carnegie Mellon, MIT revisited their introduction to computer science course. And they invented new ones. Much like Wendy said in the last panel, Carnegie Mellon had different on-ramps for different kinds of-- for students with different backgrounds. I want to also credit Randy Bryan who's sitting back there, because he wrote a little report, the dean's report on the undergraduate curriculum change. And he really helped promote computational thinking as a thread throughout the new undergraduate curriculum. And so you know, he was instrumental in helping me realize my vision. At MIT, I have to credit my former advisor John Guttag and my former colleague Eric Grimson for inventing the core 600. And now it's gone wild. And I really loved listening to the first lecture of that course that's on edX, Because Eric Grimson says, what this course is about is computational thinking. So I feel really gratified about that. So I know this is CS+X. I thought I should say something-- and also, I'm at Columbia now. So I thought I should say something about CS+X and Columbia. So we actually have a course called Computing in Context. And it's actually a course where there are joint faculty teaching this CS+X course, where X has been instantiated by digital humanities, social sciences, economics and finance, public policy, public health, and biology. And this could continue to grow. This is just one course. It's not a major. But it does go to show that, in a liberal arts education, a liberal arts university like Columbia, even computing is taken quite seriously. And basically, the bottom line is, students actually vote with their feet. So Columbia being even though a liberal arts education, it essentially is the number one major on campus if you add up all the majors from the engineering school, the college, Barnard Teachers College, and so on. And this is a phenomenal result, given that most college majors at Columbia are in economics or political science. So I now want to turn to K through 12. Tony Derose said he was going to be probably the only one to talk about K through 12, but I'm going to talk about K through 12. And here again, I actually have to credit Randy. He was on my advisory committee when I was at the National Science Foundation and I was talking about computational thinking and education, and so on. He actually challenged me to say, well, are you thinking about-- I'm paraphrasing Randy-- are you thinking about the K through 12 space? You know, here I was at NSF. And NSF doesn't do K which 12 education, pretty much. That's what the Department of Education is supposed to do. Moreover, in the United States, to do anything at the K to 12 level, it's very difficult. The United States is highly decentralized. We have 10,000 school districts. Therefore, if you want to inject computer science at the K through 12 level in the US, you have to go to each school district one by one. You have to go to all the superintendents and all the principals in the schools in that district and convince them that computer science is important. It seemed daunting, but we were able to find a way, thanks to Program Manager Dan Cooney. And I'll tell you a little bit more about that later. But what I was most impressed with was this report that came out by the British Royal Society in 2012. And basically they said, well, we in the UK can make this happen, computational thinking for our K through 12 students. And so they mandated by September 2014 that there be some curricular changes where computing was introduced as a new subject for K through 12 students. So that was pretty exciting. I use that as my poster child. There are many other international efforts now happening at the K through 12 level. The main reason that these things can happen in these countries is that there is usually a ministry of education. And the minister says, every-- this must happen. And it's a top-down mandate. That's not the way things happen in the US. OK, I'm almost done. So what have we been doing in the US? We haven't been sitting on our hands. At the high school level, we have been-- this is really thanks to Dan Cooney's effort at the National Science Foundation, has been working with the College Board, reinventing the CS AP exam and course so that it would be more about concepts in computer science, not just about programming. At the K through 12 level, the code.org was created with support from industry. CSTA, with support from CRA, has been putting out materials for high schools teachers to use. So we have been doing quite a lot. I think in the US what happens is, at the K through 12 level, things are done regionally. So we now have Chicago, New York, San Francisco, the Washington state, even Rhode Island setting goals for themselves in terms of computer science at the K through 12 level. So we're making progress. This is the way we make progress in the US at the K through 12 level. This is quite-- also quite gratifying, I think. And finally, I wanted to give credit to industry. They have also not been sitting on their hands. When I was still at Carnegie Mellon, we created this center with Microsoft support on computational thinking. And soon after, Google started a website called Exploring Computational Thinking and involving lots of K through 12 experts. And then more recently, Microsoft invests-- made a statement about investing $75 million in computer science education. I'm just picking on these two big IT companies because of their big efforts and their sincere efforts. So my closing slide really harks back to something that has been said a couple of times today, that it really is a partnership with between academia, industry, and government to really move the needle. We can't do it alone. We need to work together. So thank you very much. [APPLAUSE] The right thing will happen on the slides, right? So good afternoon, everyone. Good afternoon, everyone-- Afternoon. Thank you very much-- so I'm Charles Isbell, the Executive Associate Dean in the College of Computing at Georgia Tech, not the School of Interactive Computing, it's fine. So the question might ask yourself is, what did that have to do with CS+X? And it's actually a relatively straightforward answer. Underneath what we just heard is this fundamental idea that computing is a thing. It is its own discipline, and it's a way of thinking, and that any effort to connect with other disciplines, other parts of the university, other parts of the academic exercise have to take that as a first class citizen. So with that in mind, I want to talk a little bit about what we've been doing at Georgia Tech. So there is two things here on this slide that I want you to sort of internalize. One I'm going to kind of brush through very quickly. And the other I'm going to spend the rest of the talk on. By the way, we're running a little bit behind, so I'm going to make up for that by just talking really, really, really, really fast. So X+Computing, the design matters. So what you're going to here is at least three different versions of ways of doing Computing+X or X+Computing, depending upon where you come from. And each one is going to be a little bit different. But fundamentally, they're about making design tradeoffs. So there is a bunch of things that really matters-- people like me, administrators, really care a lot about. We're going to have to have degrees that are nimble in terms of their content. You can't have too much administrative overhead. Classes kind of have to work very well. We can't have 50 new curriculum committees. We can't have 50 sets of specialized advisors because that's too much money and too much time. I mean, we can't have 50 new courses that we're creating every five-- none of that is going to work. And so we have to solve that problem. But the more interesting thing to sort of solve or the thing to think about, which actually ends up solving the other problem, is that we have to take into account the fact that computing is a thing. It is a first-class citizen in this conversation about curriculum. And we need to make certain that our X+ computing degrees, the things that we offer, are consistent with whatever principles it is that we believe are fundamental to computing. So let me tell you a little bit about the philosophy of Georgia Tech. It boils down to a simple phrase-- contextualized computing. So here's a little history. College of Computing was founded in 1990 at Georgia Tech. We'd made a decision as a university in 1999 that every single student, no matter what their major was, had to take CS1. I don't quite know how we got the English professors to agree to that, but it happened. And everyone absolutely did it. And it was a disaster. Now, why was it a disaster? It was a disaster because we had a single class, CS1, which was motivated by things like generating Fibonacci sequences, which I agree is the single most motivating thing for taking CS. But it turns out it's not the thing that everyone sort of gets into. And we had two different kinds of students at the same time. We had the CS majors who were complaining that the non-CS majors don't know as much as they do, and the non-CS majors complaining that the CS majors are always trying to prove that they were smarter than the professor. And nobody was really learning anything. And so what we did-- and this is in large part due to the efforts of people like Mark Gusdow who was a professor at Georgia Tech and is now at the University of Michigan-- is we decided to take what we knew worked in courses, and to think very serious about how to apply it to CS1. And this is this notion of contextualization. I can show you, which I will not do, that contextualization leads not only to faster learning, but it also leads to that learning being persistent longer over time. So we had three different versions of CS1. We had-- the first version you see up there in upper-left-hand corner was CS1. The context was media. It was taught in Python. It was really taught in Jython, images and sounds. So you learned how to do things like bit blitting, which is actually kind of cool, because in order to make that work with music, sound, various other things, it's just a vector of numbers. And sometimes they represent images. And sometimes they represent sounds. And I've actually learned about for loops, and all these other kinds of things. And as we all know, programming languages only have three parts to them-- reading from a variable, writing to a variable, and conditional branching. Everything else is syntactic sugar. So you actually learn how to do programming doing that. We also did one for engineers, or most of the engineers. And its context was simulation. It was taught in MATLAB, which for the purposes of this discussion, we will pretend is a programming language. And then we had one particularly for computing, which most of our majors took, where the context was robots. So you build these robots. Those of you who have been around long enough know that there was a lot of effort in getting people into computer science by teaching them robotics. It turned out you were teaching them robotics and not computer science. So here, we try to get a completely self-contained bot that you could work with. We wanted it to be roughly the cost of a textbook, so several thousand dollars. And it just kind of worked its way through. Now, what's important here for you to get is that all of these three different CS1s are considered equivalent selections. So no matter which one you took, you were equally prepared for CS2. By the way, CS2 taught in a completely different language from any of those so that everyone would sort of come to the same place. So what does that have to do with anything? So we wanted to take this notion of contextualization, which again, we understand, and apply it to an entire curriculum. So the way you should be thinking about the way the Bachelors of Science Computer Science at Georgia Tech work is that it is X+Computing where the X is computing. So what we did is, we created something called threads, where a thread is a partial path through the curriculum, a sort of set of skills. And the idea here is that every thread is roughly 2/3 of a degree. Every pair of threads yields exactly one degree, not 3/4 of a degree, not 1 and 1/2 degrees, but exactly one degree. So there is a lot of stuff that I could talk about here. I want to leave you with one important implementation detail, which is what was important about constructing this is that there was no core. Every thread was a entirely self-contained unit. This is important, because once, I claim, you get a people-- a bunch of faculty in a room and someone asks the question, what must every computer scientist know, or what must every mechanical engineer know, you've already lost, because the answer for everyone in the room is different. Actually, the answer for everyone in the room is the same. It's whatever class I teach and whatever class I remember being hard when I was an undergrad. But that's different for everyone in the room. And you end up with a complicated huge system of courses. So what we decided to do-- apparently I've been pressing buttons. What we decided to do is, we decided that you would build things from the beginning. And you would create pairs of these things to create degrees. And it worked. It works very well. So here are the threads that we happen to have. I'll run through them very quickly. You'll notice they all start with "Computing and." And each one tells a story. And there is a lot of deep details in there about structure, which we can talk about if people are interested later. But we have names like Computing and People, Computing and Media, Intelligence, Devices, Information Systems, Theory, and Modeling. These names turn out to be very important. But what's really important about them for the purpose of this discussion is that you tell the kind of story, so that people, whether they're 17 years old or they're the parents of 17-year-old, or whether they're the faculty, or whether they're the people that are hiring our students actually understand what they mean. So Computing and People is where computing meets users, whereas Computing and Intelligence is where you model intelligence, and so on, and so forth. Media is where you deal with computing and design. By the way, Computing and People, those students not only take the computer science courses you might imagine them taking, but they also take a lot of psychology. So I do machine learning for a living. And I can tell you, many things are true about machine learning people. And one of them is that we don't really know how to run experiments. You know who knows how to run experiments? Psychologists know how to run experiments. So these people learn qualitative methods, because that's what's important when you're dealing with people not just being in the loop, but being fundamental to the loop. And the same is true for devices, where we get a lot more electrical engineering, so on, and so forth. So each of these things is not only rigorous because of the computing, but it's rigorous because of things we bring from outside computing that allows that thread to make sense. So this works. It's been great. It's awesome. Everybody should do it. But what's really nice about it is, it gave us the power to do other things. So we used to have a Bachelors of Science and a minor in computer science. And students would take it and basically just take a certain number of CS courses. It was terrible for a lot of reasons, not the least of which is the students were just taking whatever courses they wanted to. And you know how students choose courses. Well, I would take the course that was taught at 11:00 AM instead of the other one was taught at 8:00 AM. There was nothing coherent going on there. So we replaced our minor, our single minor with what turns out to be nine, but you can, for the purposes of this, think about it as eight, one per thread. So first off by the way, for the administrators in the room, this allowed us to actually predict demand over time, because we knew where they were. And we knew what they were going to be doing three years from now. But also it allowed the students to, no matter what was happening or what was motivating them to do a minor, they would actually be able to have a coherent experience in the end. And there was a story they could tell themselves and a story that we could tell them. And this is fundamentally, I think, important to getting engagement with the students. So X+Computing-- so we have a Bachelors of Science in Computational Media. It is contextualized. And it is threaded. So it is a joint degree with LMC, which is the school for Literature Media and Communications at Georgia Tech. It is by itself accredited as a computing degree by ABET, as opposed to a computer science degree. And it was the first joint undergraduate degree to span colleges at Georgia Tech. And this has been around now for about 12 or 13 years. And the way it works is, the LMC folks went out. And they created threads for what they do. They took our threads. And the way you get to CM degree is, you pick a thread from college of computing. You pick a thread from LMC. You take an immigration course, an immigration course. And you're done. And the curriculum committees follow in the way that you would expect them to follow. And music is joining us as well. So the School of Music, which is in the College of Design at Georgia Tech, is creating threads for what they have. Probably at the end, when this starts in a couple of months, it'll be Music Technology and Performance. And the way you will get a Bachelor of Science in Computational Media is, you'll pick a thread from computing and then pick a thread from either LMC or music, and you will get your Computational Media degree that way. And again, the curriculum committees work the way you would expect them to work. So we have a separate college of computing, as I noted before. And ECE is a completely separate department. This has been true since the dawn of time for Georgia Tech. CS was never in engineering at Georgia Tech. And the way you're getting a BS in computer engineering in the future, going forward in the future, is you pick a thread from CS. You pick a thread from ECE. And now you're a Comp E. By the way, counter-intuitively-- I know we're all worried about this. This will lower demand for us. And the reason it will lower demand for us is because right now, we have all these computer engineers, mechanical engineers, and so on, who switch to computer science because they need to take some computer science courses. The only way they can get our courses, only way they can get an internship, the only way they can get a job-- and they don't want to get to degrees, which by the way, is what happened at Stanford. And so as a result of that, we have all of these majors who don't want to be CS majors, but they're doing it because that's the practical way to get out and to move forward. So I just want to leave you with this notion, as I am 15 seconds over, which is that, the way we are doing our X+Computing degrees is, we thread them on both sides. We bring them together. And this allows us to get the spectrum of computational thinking out there that we want to get, from deep study in the major, [INAUDIBLE] the major, to general knowledge. And it allows us to do things like solve all of data science. And with that I will leave you alone. [APPLAUSE] And I thought I spoke quickly. So I'm waiting for the slides. Or do I have to do something here? [INAUDIBLE] Oh-- so I just want to say that we changed our name again. We're now the Khoury College of Computer Sciences. So this is a little old. We just got a naming gift. And it took us a while to settle in on what we wanted it to be. But it's finalized now. So the mission of our college is computer science for everyone. A lot of what Maria said really resonates with many of the things that we've done. And I want to talk about combined majors. So combined majors are not double majors. And they're not CS+X or X+CS. The academics take place in two different departments. And the coursework gets selected such that each department feels like the person graduating with this degree-- this is actually a degree-- has a solid foundation in both. And so Charles said he didn't want to have 1,000 curriculum committees. Well, we've done it this way. For every time we pair with another department, we sit down with them and figure out what works. So of course, we have templates of what works for science departments and what works for humanities departments. And then again, we also have-- Northeastern has a co-op program, which means that students spend six months out working in industry or in nonprofits. So they spend the first year and a half or two years in school. And then they rotate for the rest of their time, six months in industry, six months back. And so the work experience that they have can either be in computer science, or in the other field, or at the juncture of both. So when I joined Northeastern in 2014, here is where we had all of our combined majors. At that point, we had two undergraduate majors, Computer Science and Information Science. And Northeastern has seven undergraduate colleges. At that point, we were partnering with [INAUDIBLE] arts, media, and design with the College of Science with our D'Amore-McKim School of Business, and with the College of Engineering. And now we have three undergraduate degrees. We retired Information Science, because it was consumed by Data Science. We added Cybersecurity as an undergraduate degree. And we created a whole bunch more combined majors. And we create the majors that makes sense. So for example, Cybersecurity is combined with Criminology. You could imagine someone going and working for the police or for the FBI. We are in the process-- Data Science is our newest degrees, so we're in the process of combining that with every area of science. And I'm going to talk to you about how these degrees get approved through our university governance process as well. So you can see that we have added things from the humanities. We've added things from a variety of other majors. And now we have just an amazing amount of combined majors. These are the top enrollments in terms of right now. And again, since Data Science was just created two years ago, that's probably going to pop to the top. That's proving to be very popular as a way for people who are really interested in chemistry or biology to think about being immediately employable at the end of their undergraduate degree in a field that pays very well. Our co-ops are very highly sought after by the entire university, because they pay so much per hour. So students are really motivated. So here are the statistics in terms of how many combined majors we have. I'm going to show you also not-- pink is the non-combined majors, that top bar. And you can see that we have-- we started with, very robustly in 2014, with a lot of combined majors with art, media, and design. That was partially because we had game design that was very popular at that point. We had a few combined majors with the College of Engineering. That's very similar to Georgia Tech, mostly doubly or computer engineering majors. We had-- College of Science has always been very popular, and also business. In 2016, we added Social Sciences and Humanities. And you see this teeny little gray, light-gray bar. And now that's growing. One of the things that's happened is that applications in social sciences and humanities have gone up because of the combined majors. And we combine with everything-- English, Philosophy, Political Science, Economics. So the point here is that more than half of the majors in our college are combined majors. And I think that that has also increased the number of applicants to our college as well. When you apply to Northeastern, you apply to a particular college. Now, when you do a combined major, you can choose either college as your home college. So how does the university deal with this in terms of the governance? So both components must already be existing majors. At least nine courses must be from each component. Those are just required. People often take far more courses in computer science than that. The average in computer science is 10 from our major. There has to be at least one integrative course. That's often a new course that's developed by the two faculties. And all other degree requirements must be met. So where do the ideas for combined majors come from? It can come from us. We've had students-- we had a student in journalism that wanted to do a combined degree in computer science and journalism about six years ago, so we created a degree for that one student. That's a very popular combined major now. So we meet with the partner representatives. We work out everything until we're both happy. Then it goes through both colleges' voting approval. If they have departments, it goes through the department, then it goes up to the college level for a vote. And then it goes to a university-level approval, which is the undergraduate curriculum committee. It does not have to go to the senate. And you may be saying, oh my gosh, how long does this take? Well, it typically just takes one semester to get this done. It doesn't take that long, because we have a mechanism. And we're not the only college that has combined majors. There are combined majors across Northeastern, across different other units as well. So it's something that we're very fast at doing, something that the university as a whole is committed to. And it started in computer science. My predecessor, Larry, came up with the idea. It was to basically stop from being absorbed into engineering during that time period that Jeanette pointed out. He was thinking very strategically, how do we not become part of EECS? And so we thought, well, if we combine with all these other colleges because engineering is ABET-accredited, we can't be absorbed. And I think it worked really well, but it actually ended up being a really good learning outcome for the students. Here is just an example. I believe my slides will be available, so I won't go through this in detail. I'll just choose Data Science and Health Science. Everybody comes in. All the freshmen would come in. And they take the same four courses in their first year. Actually, they take almost the same six courses in the first three semesters, which includes an algorithms course because they have to be ready for technical interviewing by the time of the end of-- by the beginning of their second semester in their sophomore year, because they're going to go out on co-op, so pretty much lockstep. So they have a lot of time to think about, well, which one do I want to do? And then things start to diverge. And you can see that in Data Science, they're going to take data science, large-scale data, and so on. And in cybersecurity, they're going to take a lot of systems courses and a lot of cybersecurity classes. And we have more examples. The second innovation that I want to talk about is something that we introduced in 2016, is the idea of a meaningful minor. And I'm reminded always by my dean colleagues that there are other meaningful minors in the university, so this is a meaningful minor in computer science. And they take the first two intro courses. And then what courses they take after that depends on what their major is. So if they're a biology major, they might want to take some data science. If they they're an English major, they might want to take natural language understanding and text binding data visualization. If they're a media studies, they might want to take web design, data visualization. So the idea is that the miners should be customized to their major field of interest. And one of the things that we do in order to encourage people to think about this, is that they take at least four courses in our college. And then one course can be from another college we went through the entire university curriculum, all courses, picked out every course that my associate dean at the time thought might be something we wouldn't frankly be embarrassed to say is computer science. Then we had a committee vote on which ones were actually possible. So an example would be the data visualization course that's in Arts, Media, and Design which has a lot of computer science in it. And it teaches people really thinking about not just what are all the computer science components of data visualization, but thinking about art as well. And so students can then take one of these. And where did this idea come from? It came from a student that was my personal trainer at Northeastern. He was a physical therapy student. And they often sign up to be personal trainers. And it's a nice discounted personal training program. And I asked him, why do people do a minor? And he goes, when they can double count. And so that was the original idea for this, but it turned out to really make sense in terms of the curriculum for the students. So we have a lot of people starting to do this. I'm showing a graph that shows the increase. And this is taking off. And it's also leading people to do a combined major. And what does it mean in terms of who comes in? 36% of our combined majors are women. That's a pretty good number. We started out in 2014 with only 19% women. We're at 26% overall in the college, but the majority of them can be found in the combined majors. We have-- in their applicant pool for the incoming class next fall, 40% of our applicants are women. Almost all of them are looking for a combined major. And 14% of our combined majors are underrepresented minorities. We see no difference between that statistic with our overall statistics. So we notice that the combined majors seem to make a difference with respect to women being attracted to it, no difference at Northeastern with respect to underrepresented minorities. And questions will of course be at the end, thank you. [APPLAUSE] OK, so I'm going to talk about some things that I did, not in my current day job which is at a different university, but in my previous gig when I was running computer science at the University of Illinois. So since we've heard about exponents, I just thought I'd show one. This is sort of the landscape where things were when we were starting. This is the Illinois Computer Science major. At the University of Illinois, Urbana-Champaign, you apply to the college that happens to be the College of Engineering. And then you say what you want to major in. Those are everything you can major in. Those are fractions. If you add them all up, you get to 100. Those of you with some data science training will notice something that the specialists refer to as a pattern. We're sort of crushing everything and the horse it rode in on. Everybody everywhere wanted to be in CS. And so the interesting the interesting question is, architecturally, at the level of an organization at this scale, that suddenly this-- almost the single most popular unit, the single largest teaching unit on campus, what are you going to do going forward? So one of the responses is, you go deep, which is, you add capacity. You add faculty. You figure out space. You figure out where you're going to get startup packages to hire faculty. Let me get this right. You figure out how to just sort of teach more things. So that's a necessary, but my argument is, an insufficient response. I mean, and one of the joys of being in academia, one of the particular joys of being in a comprehensive institution is that you have the opportunity to go wide. You know, you can talk to people whose scholarship and whose discipline looks absolutely nothing like yours. So the idea behind the CS+X general style is to partner with things that are not computer science departments. And it's to simply build a different class of students, a class of cross-educated students, a class of people who look different, are recruited differently, who are very likely, you hope, to have different educational and eventually employment kinds of outcomes, and to re-pivot computing, and now computing and data, as this sort of hub thing in the middle of all of these other opportunities, STEM, humanities, arts, everything. So at Illinois, the particular architecture that we ended up with was a set of CS degrees that are literally half of the computer science degree, half of the X degree. And again, all of this, the standard general education requirements which are basically one class out of five, those things those things still obtain, and to architect that as a Bachelors of CS+X owned by the Department of X. So it's not a minor in computer science. It's not a minor in X. It's not a dual degree. It's not a CS degree with a patina of X or an X degree with a soupcon of computation. It's really half of one and half of the other. And the owned and operated by the Department of X turned out to be a big deal. There are a lot of places where engineering in general and computing in particular is the 800-pound gorilla sucking all of the oxygen out of the room. By actually approaching these degrees as the Department of X is the owner and operator and is the principal in these things, we took all of those sort of dominance relationships off the table. It is literally the case that many of the X departments that I talked to told me that my conversation with them was the first time in their lifetime that anyone from engineering had ever been nice to them. The second thing that we did-- and I'm going to talk about this in a little more detail later-- is that it is the same computer science core for all of the Xs. All right, so like lots of computer science departments in an engineering college in a large public university, we actually had some other X-ish kinds of degrees on offer when-- before we started. Lots of big schools often have a liberal arts version of their CS degree and an engineering version. That's true for Illinois. That's true for Michigan. That's true for some other places. So we had some X-ish kinds of things in math and statistics, but those were decades old. What we did when we started to do this is, we identified another set of partners in liberal arts and sciences. And those turned out to be anthropology, astronomy, chemistry, and linguistics-- so some hardcore STEM stuff, astronomy, chemistry, some stuff kind of on the bubble, linguistics, and some stuff that does not look like STEM at all, anthropology. The design of the degree started in 2010. They were approved. It took three years to get through the first set of approvals. The admittants were in 2014. The pipeline filled last year. We finally have the first graduates last year. And the pipeline is now up and operating. So there was this book about 15 years ago, All I Really Needed To Know I Learned in Kindergarten. My version of this is, everything I needed to know about CS+X I learned doing startups. I've done a bunch of startup companies. So I'm going to switch to sort of startup-guy language when I talk about this. So design principles for Illinois CS+X-- one, time to market matters. After it took us three years to get the first one of these things done, there was a big pivot to figuring out and learning, like, who are all the stakeholders? Who are all of the approval processes? You need department-level approvals. You need college-level approvals. You need campus-level approvals. You need senate-level approvals. The board of trustees has to vote on this. A set of people you've never heard of and never want to sit in front of more than once in your life, the Illinois Board of Higher Education has to approve these kinds of things. We ended up designing these things so we understood what the stakeholders were and how to move them through the approvals processes. If you're in product design space in a startup, you rapidly discover that perfectionism is a terrible idea. So the notion of a minimum viable product-- we kept talking about minimum viable degrees. Now, that sounds sort of like negative kind of a language, but really, that turned out to be a fundamentally powerful concept. The set of CS courses are the same for everybody. I did in fact get all of my faculty in a room. And I did challenge them, what do they need to know in order for us to shake their hand and call them a colleague? And there were a lot of yelling. And there were tears. And there was weeping. And there was wailing. And at the end of this, we agreed. If you take these courses, you are a CS colleague. And we were able to move that stuff forward. And we were also able to combat one of the sort of criticisms of these things. It's a watered down, it's CS-like-- no, no, no, it's actually the same stuff. The first pitch for these things for all the partners looks exactly like all of my venture capital pitches. And God knows I've done a lot of them. There is a value proposition. There is a SWOT analysis. There is a team. There is a business model. And it was 15 minutes long. It is in fact, exactly 15 slides long. There was an elevator pitch. You need early adopter customers. You need some people who want you to be there, because I hear a lot of discussions about this, and the modality of this is that we're going to do CS+X to you. And that's a really bad idea. You want to do that with you. Why is the CS+Chemistry an X in this particular thing? Because the biggest computational chemist in the Chemistry Department and I have the same personal trainer, literally. And she was on a treadmill. And I was on a treadmill. And I was talking about this. And she said, Rob, this is awesome. And 24 hours later, the head of the Chemistry Department called me and said, Rob, we have to be an X. You need people who want to be there. And you need a business model, which is not something anybody has talked about yet. So here is our business model. At Illinois, there is differential tuition. It is more expensive to be an engineer than it is to be an anthropologist. That difference is the tuition differential. We architected all the CS+Xs like engineering. We upcharge to the maximum tuition. And that extra delta, we split with the X department. So not only were we being nice to them and we were inviting into the fold, we were paying for it by actually admitting students for them. So this is what the enrollment landscape was looking like for the ones where the pipelines are filled right now. So you see some big CS chunks in red. You see some little CS chunks. And the reason I'm showing this is, you know, it is not in the nature of all departments to be as big as computer science departments. I mean, Illinois is a gigantic place. Just to calibrate that, if you take the Computer Science Department and the Electrical Engineering Department and you put all their students together, they are bigger than MIT, period. So when you are operating at this kind of a scale, one of the things to note is that, like, look, there is lots of red chunks on the bars. That's good. But all right, so that's happy. We're happy about that. But a better picture is this one. We are a significant fraction of a lot of departments now. So we are 20% of Anthropology, 30% of Astronomy, 41% of Statistics, and 48% of Linguistics. We are the reason there is organic growth in some of these departments. And in some of the ones that are unfortunately shrinking because they are on the sort of the side of humanities and social sciences where parents are concerned that there aren't jobs on the other side, we're the reason they have not shrunk as badly as some of their peers. All right, so we're actually-- we're feeling very good about this stuff. It is very likely to be the case that things like statistics and linguistics in the next five years will be dominated by the CS+X side of the world. So where is it today-- you know, up and running, math, stats, anthropology, astronomy, chemistry, and linguistics, starting, launching, approved, moving, recruiting students right now, advertising. Geoscience, music, philosophy, crop science, economics, a couple of things going on here-- for the first time we have some partner departments that are operating at the same scale as computer science. Economics is not a small department. That's gigantic. And Illinois is a land grant school. Illinois has an agricultural school operating at the same scale as the engineering college, in the same you know US News & World Report ranking. The Crop Science Degree is awesome. It's brilliantly architected. It's already got companies lining up for its graduates. And it created an existential challenge, because in Ag, crop science is called? [INAUDIBLE] OK, so before you move forward on some of these things, you might want to think about your trademark kind of action going forward. We made that one work out. The single best proposal we saw for in terms of curriculum to curriculum was the crop science proposal. You know, farmers are going to be like Uber drivers in the future. You know, they're going to have robot tractors running over fields. They're going to launch the drones in the morning. They're going to kind of commission their time on their satellites. And in every 10-meter-by-10-meter-square chunk, once a month or so, they're going to do metagenomics and figure out what exactly they should be planting this season. The computational landscape there is daunting. The single best write up for any CS+X was the Philosophy Department. Those guys can do storytelling-- really brilliant, just brilliant. Up next, there is an animal science degree. There is a whole bunch of other degrees that I'm not-- shouldn't really be talking about, because they're not quite into the sort of like moving into the approvals process. There is going to be dozens of these things moving forward. So we're feeling very, very, very, very good about that. So that's it. [APPLAUSE] It's supposed to end right now, the whole session. So I don't think that it will be a bit behind time, a good opportunity for us to also open for questions. I mean, one thing I would make a comment, or maybe somebody wants to ask or answer is, a lot of combination of very interesting majors. And I'm just wondering if those are changing the way we teach things in a combined manner? Or are still students picking from different silos? Or is there really a new form of teaching, blended teaching between people from different majors through your experiences? Maybe just-- I can address that. We have-- is this on? Yeah, we have-- for all of our combined majors, there has to be at least one integrative course. And it can be at the beginning. Or it can be at the end. Or it can be in the middle. And I'll give you an example of one in the beginning. We created a course called Bostonography with social sciences and humanities. And it's about the digital humanities. It's the very first course. They take it at the same time as they take the Introduction to Fundamentals of Computer Science. And it's all about the City of Boston being viewed through the data. So it's things like disproving the theory about that if you have rundown buildings, that there is a higher incidence of crime. It's looking at that data. It's looking at, if you changed the voting maps, how does that change who is elected? So it's looking at the data at the same time and the sociology behind it at the same time as they're learning how to program. And at the end, they have a project in the class where they get-- they choose some data to analyze. And they're able to write the code to do it. So that's an example. Any other interesting examples? For the Illinois CS+Xs, we sort of let the integrative stuff emerge organically from the groups. And sometimes it emerges early. The music degree that's launching has a very cool new set of integrative kinds of courses. Some things show up later. An early experiment that led to the CS+Advertising degree is interesting. You know, and again, one of these sort of adjacencies matter, one of the best instructors on the teaching track is married to a tenure-line faculty in the Advertising Department. And oddly enough, we ended up running a team design course between advertising majors and computer scientists. And they built a real-time baseball tracking app. And then they built this really cool website. And then I think they actually put an app together. And you can decide whether this was successful or not. They thought they were very successful. The campus thought they were very successful. Major League Baseball sued them. So I think, yes, that's a win. I'll just say quickly that, what seems-- we do the organic approach too, which basically means, we didn't think about it. And it just sort of worked out. And what seems to happen is, there is a kind of gateway course that gets created, naturally mostly on the X side. And then there is a design thing that we require. And that's where a lot of the integrative stuff happens towards the end. So it sort of happens on its own. So we should open it to questions. Eric, you get, I guess, the-- Charles, you raised this, but I'm asking the committee, what can we learn from Stanford's cutting down of their CS+X? What went wrong there that we don't want to copy there? [LAUGHTER] I don't know. Well, we'll do a-- We'll do a lightning round on this. So for me, what it looks like-- I mean, I'm not at Stanford. What it looks like reading everything is, they didn't do the thing-- so what you heard here from everyone is, half and half. I say 2/3 and 2/3. It turns out that turns out to be a half and a half when you add it all up together, really, because of the way math works. With requirements and a set-- it's complicated. We'll talk about later. As far as I can tell, what it looked like is, you basically ended up with two degrees. And it turns out, students aren't as interested in taking seven years to graduate as you think they are. I think the argument-- yes, right, I think you know, I was really not kidding when I said, I got people in a room. And I forced them to say, we are going to pick a critical core, a critical mass, a subset of our curriculum. And we're going to argue about this until we have agreement for what the spanning basis is for this stuff. And then we're going to bolt this on to other people's X in some in some thoughtful ways. And as far as I can tell, that that sort of tough set of negotiating stuff didn't really happen. And so it was most of the CS degree and most of the X degree. And you know, Stanford CS is an awesome degree. And there is a whole lot of awesome Xs, I am sure. And the two-for, all 217% of it, was probably a spectacular amount of learning. But it wasn't anything like a bachelors degree. So my name is [INAUDIBLE]. I'm an MIT PhD and an entrepreneur for the last 20 years. One of the things that you or the other panelists did not discuss yet is talent development among faculty members. So as we go into all the innovation that you each propose that you did that succeeded or didn't, there must be some things that you did in terms of professional development allowing faculty to learn to teach in new ways, to co-teach all these computer science and disciplines together. What do you do to make a course successful is also related to whether the faculty is teaching it really well and engaging in innovative ways. Maybe you can each talk a lot about that. In K-12, as Jeanette knows, a lot of the effort is going into professional development, when you want to bring computational thinking and computer science into the disciplines, or you want to do a team teaching between computer science or teachers who are teaching math, science, history. Maybe you can comment on that. Actually, I want to answer in terms of this course that I mentioned at Columbia called Computing in Context, which is really a computer scientist who teaches the first half of the course if you will. And there are recitation sections that the students take for the X throughout the entire course that's taught by Professor X. And this team of computer science professor plus the set of-- the X of set of all Xs, they work as a team. And it's actually-- what my understanding is from the computer science faculty, the challenge is, as the set of X grows, or the number Xs grow, it's really hard to coordinate this growing team. But the professor Xs were all inclined. They were all already computationally thinking in their Xs. They didn't really need training. However, I can imagine, if this spread and you need to substitute someone else in that department to teach when the guys on sabbatical or something, then there would be training needed. But I think as-- I don't know how Illinois can handle so many Xs with-- where CS and X are huge. It must be an administrative management challenge. But anyway, Illinois is probably used to doing things at scale. Yeah, you know, at Illinois, courses come in multiples of 800 people. So it's a really good question. I think it's a future-looking question. You know, you start with early adopters who are basically computationally inclined. So they're sort of meeting you halfway. One of the one of the upsides of the, we settled on a core of CSs, is that it's the same CS core. We're teaching it to everybody. And so one of the things people would say is, like, really? You know, you have anthropologists sitting in the data structures class? We went, yep. And I was really surprised. And this was interesting. I was worried that I was going to get pushback from the deans and the other folks that, you know, we weren't going to sort of design a unique set of on-ramps for all of their Xs. And they said, no. They said, this is fabulous. We were always worried about, its dumbed down. It's light. It's simplified, and everything. We acknowledge that this is not the only way to get people in the game. And we acknowledge that we are going to end up drawing from a somewhat more limited population of anthropologically inclined, say, kinds of students. But when they get through this curriculum, they are going to be absolutely as good as anybody else in terms of what they know about anthropology and what they know about computer science. What we did start doing is, I was pushing very hard for joint hires. So like, we ended up-- I managed to get a joint hire with us and advertising. I actually think that one of the ways to sort of go after this stuff is to actually organically hire some people who have a foot in both departments. And now that's-- I had a conversation at lunch, that like, that's really hard. Not everybody is good at this. But the people you find can actually be the bridges that make it possible to do this stuff. And that was just starting to happen when I left Illinois. I would just say that 25% of our faculty are cross-discipline, and that that has a large motivating factor. And the entire college also, their whole heart is behind this as one of the things that is uniquely-- well, used to be uniquely Northeastern. And now, thank goodness, it's not. I only had one more-- I'll just briefly say, I don't think we actually had-- we didn't really have that issue for a couple of reasons. One is, the level of abstraction here is not the course. The level of abstraction is the curriculum. And so actually, all the hard parts that you're talking about was in thinking about getting folks from different disciplines just to think about how it meant to have a degree and less about the individual courses, because the people who were teaching those were already, as Rob put it, computationally inclined. And we shall see what happens 10 years from now when we start bringing in people who are very far away. Although, I bet 10 years from now, they will already be computationally inclined, at least I expect that will be the case. So it won't be that big of a deal. The other thing is that, we're the largest engineering college in the country by a lot. We have been teaching everyone from outside of our majors for 15 years before any of this stuff happened. The English majors were teaching-- 75% of the students were engineers. So we already kind of had a culture that sort of supported this kind of thing, I think. I did want to make one statement for MIT. As you're thinking for the future in terms of the College of Computing and the students who are coming in-- and to pick up on what Charles just said-- if so much is happening at the K through 12 level in terms of teaching computer science, the students that you're going to get in a few years will have more of a command or a command of more computer science than they have now. And so that is something to anticipate, something to embrace. But it also means yet another change in your curricula. So be flexible, look to the future. And remember the kind of training and skill sets the students you're going to get in 4 years, in 10 years, are going to look like. Sure you want to take one? OK. So hi, Greg Morrisett from Cornell-- so a quick question about relief from too many students majoring in CS, so are you, with the CS+X activity, seeing, relatively speaking, or surveying students to find out whether you've really shifted them from computer science majors to CS+X majors? And are you seeing relief on your faculty? For example, when we had something similar to threads, we had-- we called vectors, of course. But-- Of course. --we got rid of them because of the administrative overload. And so that was sort of coupled with the big enrollment increases. So in fact, we were trying to streamline it instead of introducing more Xs. And I'm wondering if you're seeing a relief amongst your faculty for this tidal wave of students coming. So it's too hard to tell, because there is this exponential growth coming from somewhere else. So it's hard to disaggregate, at least for us. I think, well, one of the nice things about the way our-- well, nice, one of the side effects of the way the threads are structured right now is that we actually see where all the demand is. It's in the computing and intelligence thread. So I last year, last calendar year, by myself, taught 3,331 students. How? Because I love the students. Anyway, the point is, so there is a lot of demand in sort of the machine learning role. It's less in some other places, and so on. So some people are seeing the growth everywhere or not. We do have some evidence that students who have the option to do a minor in computing and be computer engineers, or be computational media majors, or whatever, take that route. And so actually, the aggregate total demand is lower on our faculty as a result, because we're seeing only roughly half of their time as opposed to a full amount of their time. So there is some evidence of that. But the truth is, with all of the forces, it's too hard to disaggregate, at least for now, at least for us. I have the exact opposite answer. If you think about who applies to computer science who is a 17-year-old, you know, a lot of the population isn't applying. And so then they get to university. And you know, they have some friends who, some other women, some other underrepresented minorities that checked computer science and say, hey, this is really funny. You should try it. We're seeing people who did not apply to the college now come into it. And so in addition to the opposite of Georgia Tech, I'm also opposite of Charles and that I taught exactly zero students last year as a dean. Well, on average, we're reasonable. By the way, we-- OK, I'll be back. We are actually, we are seeing students-- we are seeing migrations of students into computer science. But at Georgia Tech, you get admitted to the institute, not the college. Everyone gets to change majors no matter what's happening-- Yeah, same. --in their lives once for the first two years. Same. It's just really difficult to know, because there are too many other things going on. I'm going to split the difference on this one. So the question was, because of this, the existence of C+Xs, are you seeing relief? No, it's making it worse. Yup. I literally had a faculty meeting that went like this. Rob, the classes are bursting at the seams. We can't find teaching assistants. We are dying on the beaches here. And it is your vision of the future that we should start 25 new degrees-- discuss. And I managed to get people on board. I said, because it is the right thing to do for the discipline. And this was my argument. I said, this demand for intersectional degrees at the intersection of computing data and some X will be met. It will be met in one of two ways. We will lead this in supplying sort of the best quality computational data-centric kinds of stuff. Or we're going to see the rise of a million little spontaneously generated computing units across campus. Do we want our good friends in the anthropology department to build their own data structures curriculum? If the answer to that is yes, you can walk. Otherwise, we have to own this. And we have to drive this, even if it hurts for a while. And the argument is that the scale at which this thing is self-combusting is causing leadership to look and do appropriate things. I did not meet any of them individually. I have about 14,000 people in MOOC this morning. I'm sure they're all very happy. That's incredible. Thank you so much.

Contents

Environs

Cherry Street, closed to vehicle traffic, serves as a footpath along the Western border of the Historic District of Georgia Tech.
Cherry Street, closed to vehicle traffic, serves as a footpath along the Western border of the Historic District of Georgia Tech.

The Georgia Institute of Technology Historic District is situated on and around the crest of "The Hill," the highest elevation of the school's original nine-acre campus. Comprising 12 buildings, the Old Campus is a landscaped cluster of mixed-period classroom, dormitory and administrative brick buildings. Buildings of the Old Campus include the Carnegie Building, which was the campus library until 1953; the Georgia Tech President's Office is now located there. Lyman Hall Laboratory, named after Lyman Hall, one of Georgia Tech's earlier presidents, was the school's first Chemistry Building. The YMCA Building, funded by John D. Rockefeller in 1910, now houses the Georgia Tech Alumni Association Offices. The random placement of these buildings around the centrally positioned Administration Building ("Tech Tower") has created unique urban spaces. Hundred year-old trees shade the red brick buildings and enhance the sense of special enclosure.

Uncle Heinie Way seen from West of Tech Tower.
Uncle Heinie Way seen from West of Tech Tower.

A brick roadway, Uncle Heinie Way, wraps itself around the Administration Building forming a "loop" and provides both service and vehicular access to the buildings in this portion of the Campus. A new plaza, Harrison Square, (1968), which both a hard surface of brick and concrete as well as an open green space, was created after the demolition of the Old Shop, the successor of the original (a near-twin to the adjacent Administration building which burned down shortly after its completion).

Style, Form, Planning

The Old Campus of Georgia Tech is significant for more than just the design of the buildings of which it is comprised. As is evident in the placement of the buildings, little thought was actually given to the future expansion of the then young technological school. Instead, the site planning was carried out in such a manner as to meet the immediate and pressing needs of the school. This practical approach has created the significant quality of space. The harmony found within the Old Campus is attributed to the fact that almost all of the buildings were built within a short span of time—from 1885 to 1923. Though all exhibit a consistent approach in design and construction, none include a repetition of style or form.

National Register of Historic Places

In 1978, the area was added to the National Register of Historic Places. Near the entrance to Tech Tower, an historical marker maintained by the Georgia Historical Society commemorates this listing as well as the early history of the Georgia Tech campus.[2]

The twelve buildings

The Lyman Hall Laboratory of Chemistry
The Lyman Hall Laboratory of Chemistry

The Old Shop could be considered a "thirteenth building." Erected in 1888, it was destroyed by a fire in 1892 and a replacement was built in the same year. The second shop building was demolished in 1968.

See also

References

  1. ^ a b "Lettie Pate Whitehead Evans Administration Building". Georgia Tech. Retrieved 2013-10-05.
  2. ^ "Georgia Institute of Technology Historical Marker". Historic Markers Across Georgia. Retrieved 2013-12-22.
  3. ^ "History of Knowles Dormitory". Georgia Tech. Retrieved 2013-10-05.
  4. ^ "Aaron S. French Building". Georgia Tech. Retrieved 2013-10-05.
  5. ^ "Domenico Pietro Savant Building". Georgia Tech. Retrieved 2013-10-05.
  6. ^ "Janie Austell Swann Building". Georgia Tech. Retrieved 2013-10-05.
  7. ^ "Lyman Hall Laboratory of Chemistry". Georgia Tech. Retrieved 2013-10-05.
  8. ^ "Andrew Carnegie Building". Georgia Tech. Retrieved 2013-10-05.
  9. ^ "Historic Structure Report: Joseph Brown Whitehead Memorial Hospital/Chapin Building". Georgia Tech. April 2013. Retrieved 2013-10-03.
  10. ^ "L. W. "Chip" Robert, Jr. Alumni/Faculty House". Georgia Tech. Retrieved 2013-10-05.
  11. ^ "David Melville (D. M.) Smith Building". Georgia Tech. Retrieved 2013-10-05.
  12. ^ "William Henry Emerson Building". Georgia Tech. Archived from the original on 2013-10-07. Retrieved 2013-10-05.
  13. ^ "John Saylor Coon Building". Georgia Tech. Retrieved 2013-10-05.

Bibliography

External links

This page was last edited on 29 December 2019, at 23:06
Basis of this page is in Wikipedia. Text is available under the CC BY-SA 3.0 Unported License. Non-text media are available under their specified licenses. Wikipedia® is a registered trademark of the Wikimedia Foundation, Inc. WIKI 2 is an independent company and has no affiliation with Wikimedia Foundation.