The Future of Higher Education in the Age of Disruption

The Future of Higher Education in the Age of Disruption

August 29, 2019 12 By Ronny Jaskolski


[APPLAUSE] So when MIT decided to commit
to creating the MIT Stephen A. Schwarzman College of
Computing, it was clear to us that this was what
we needed to do. What was less clear to us was
exactly how we should do it. In a somewhat
uncharacteristic fashion for an academic
institution, we basically decided that we should
simply declare our intentions to establish this
college, then set about the business of
creating the college together as a community. In that context, it seemed
entirely appropriate that we convene
an event like this to discuss how to
teach computer science, not only amongst ourselves,
but with experts and friends from other institutions. We also recently created
five working groups that are contemplating various
aspects of the College. Many of the members of
those working groups are here to listen
and learn today. And I’m confident that
what we learn today will, indeed, shape our
framing of the College. I want to thank the
organizers for this event, in particular Saman, Asu, and
Sanjay, who worked diligently to pull together what looks
like an extraordinarily exciting program. And also the speakers
that we’ll hear from today, both our
colleagues from MIT and other institutions. At the risk of repeating
some things that many of you have heard me say, let me offer
my reflections on the College to set a context for
today’s discussion. A little over a year
ago, President Reif launched us on a
campus conversation about what we should
do about computing. In some respects, we are
facing enormous challenges. And one example of
that was an explosion of interest and an inefficient
allocation resources. 40% of MIT undergraduates
are majoring either in computer science or
computer science combined with another degree at MIT. And yet, only 7% of the MIT
faculty are computer science– faculty appointed in the
Computer Science EECS department. That’s a tremendous imbalance
in allocation of resources. In addition, and very
importantly, we’re hearing from all
corners of the Institute a narrative that basically
followed along the following lines, which is that my field– insert the blank–
is being transformed by modern computational methods. In fact, I was at a dinner
just last night upstairs, a meeting of the
visiting committee for the political science
department, and spoke to one of our colleagues
who’s using large data sets scraped from public
records about legislation and lobbying and applying
natural language processing to identify how individual
corporations are influencing the development of
legislation, a fundamentally different approach to a
political science challenge. That story repeats
all over campus. And what our colleagues
in these disciplines were saying to us as we
had this conversation was that they needed GPUs. They needed access to
software professionals. They needed engagement from our
computer science colleagues, both to think
about what are some of the more advanced algorithms
that could facilitate their work, but also to assist
in developing curricular offerings for students
in their discipline that needed to
master these skills. Lastly, we saw everyone
feeling, particularly in the climate we’re
in, an intense need to think more holistically
about the societal impact of the technologies
they were developing, and to think about that
before it was deployed, and thus appropriately
shape the deployment. Amongst this sea
of need as we were going through this
conversation, what we realized was an enormous opportunity. As is true for many of
the institutions that are present in this
auditorium today, we at MIT are blessed with
an extraordinarily remarkable computer science
talent pool at MIT. And we have an opportunity
to invest in that talent pool to advance the fundamental
work that’s being done. But in addition, we realize that
if we could build a structure, the college would strengthen the
links between computer science and the departments that
want to leverage computing, not only to the advantage
of those departments, but so that what we learn
through those linkages would certainly feed back into
the research and teaching we do in computer science. And if we could do that, we
would really seize a really profound opportunity while
addressing the challenges we were feeling. And lastly, in
creating something new, we have a clean sheet of paper. And we have the
opportunity to think what I believe will be
creatively about how we integrate a comprehension
to the societal impact of the technologies
that will emerge from MIT and this college into
the education and research agenda. I’m not suggesting
that this will be easy. In fact, it’s going
to be quite hard. But I can’t think of a more
transformative opportunity for this institution. And that makes me get up
every morning very excited about the potential
of the college. So that’s the aspiration for
our college and the journey we’ve embarked upon. I look forward to hearing
from today’s remarks that are going to help us
point in the right direction. So with that, let me now
introduce our keynote speaker Dr. Farnam Jahanian. Farnam was appointed the 10th
president of Carnegie Mellon University in March of 2018. He’s a nationally recognized
computer scientist, entrepreneur, public servant,
and higher education leader. And in that regard,
we’re extraordinarily fortunate that he would
take time to join us today. He first joined CMU as vice
president for research in 2014, and later assumed
the role of provost and chief academic officer
for May 2015 to June 2017. In July 2017, he
stepped up at CMU to serve as interim president
before, in the infinite wisdom of their board, they tapped
him to be the president. Prior to coming into CMU, he led
the National Science Foundation directorate for computer
and information science and engineering
from 2011 to 2014. And before serving in NSF,
he was the Edward S. Davidson collegiate professor at
the University of Michigan, where he served as chair
for computer science and engineering
from 2007 to 2011, and is director of
the software systems laboratory from 1997 to 2000. In addition to his academic
and government work, he co-founded in 2001 the
internet security company Arbor Networks, where
he served as chairman until its acquisition in 2010. He holds a PhD in computer
science for the University of Texas in Austin. Hook ’em, horns. He’s a fellow of the ACM,
the IEEE, and the AAAS. On a personal note,
I would say that I’ve come to know him very well
through a form of what I consider to be group therapy,
which is when collections of provosts get together. In that setting, I’ve really– can sincerely say that
I’ve benefited tremendously from his advice,
wisdom, and friendship. And today Farnam is
going to address us on the future of
higher education in the age of disruption. Please join me in extending
a warm welcome to Farnam. [APPLAUSE] Good morning, and it’s good
to be with you this morning. And Marty, thank you so much
for that kind introduction. I should tell Marty that
having served as president and also as provost, provost
job is the hardest job on campus, Marty. I think you know that already. Once again, thank you very
much for the invitation. First, on behalf of
Carnegie Mellon University, I would like to congratulate
the entire MIT community as you celebrate the launch
of the Stephen A. Schwarzman College of Computing this week. I want to especially extend
a warm congratulations to President Reif for his
extraordinary leadership, and also to Steve Schwarzman
for his generosity, vision, and continued
commitment to the future of higher education and
the economic prosperity of our country. Please join me in congratulating
the entire MIT community. [APPLAUSE] MIT continues to be a world
class institution that offers a distinctive
education and cutting edge research, of course. And this latest development will
certainly increase its impact in the changing world. I’m also grateful, I should
say, to Anant and the organizing committee for the invitation to
deliver this morning’s keynote. The theme of today is
centered around the importance of education, and especially
within the context of the unprecedented
advances that we have seen in technology. So this morning,
what I would like to talk about is the
changing role of higher education in this
age of disruption, with a particular focus on
the way computation and data are underpinning these changes. To begin, I think
we all recognize, and as Marty pointed
out, we’re in the midst of a global
transformation that’s catalyzed by rapid acceleration
of digital technologies, including unprecedented access
to computation and data. The scale and scope and
pace of these advances are truly unprecedented
in human history. In particular– let me see
if I can find my clicker. Oh, thank you very much. To put this in perspective,
if you look at this scale, we’re not only dealing with a
singular technology, but rather a set of interrelated
breakthroughs. This dynamic of
interrelated technologies necessitates cross collaboration
across disciplines. When you look at
the scope of it, the impact of these
emerging technologies are ubiquitous, reaching almost
every sector of our economy with a wide range of
applications from health care to finance to
transportation to energy, manufacturing, and far beyond. And of course, the
pace of it, I don’t need to tell this audience, is
that the pace of innovation, of course, is
accelerating dramatically. This requires new
strategies for partnership, not only within a
campus community, but also across to government,
as well as industry partners. Let’s consider for a
moment just what we have seen in the last 10 years. We could have scarcely imagined
that just about 10 years ago. Imagine a day, if I said to
you, by integrating biomedical, clinical, and scientific
data we can predict the onset of diseases, identify
unwanted drug interactions, automated diagnosis, and
personalized therapeutics. Imagine a day that by coupling
roadway sensors, clinical and– I should say roadway
sensors, traffic cameras, and individual GPS devices, we
can reduce traffic congestion and generate significant savings
in time and fuel efficiency. Imagine a day that by accurately
predicting natural disasters such as hurricanes
and tornadoes, we can employ lifesaving
preventative measures that mitigate their potential impact. Imagine a day by using
biometrics and unconstrained facial recognition techniques
we can correlate disparate data streams to enhance
public safety. Imagine a day that by using
autonomous technologies we can have our
cars drive us safely and securely without the
danger of– or at least mitigating the danger of
traffic accidents caused by human error. Just imagine a day
that by cataloging data from millions of photos and
videos posted on social media from conflict areas
we can move rapidly to investigate and understand
human impact of conflicts, disasters, and
political violence. And finally, imagine a
day that by integrating emerging technologies such as
AI enabled learning techniques and inverted classrooms
we can achieve personalized
outcome-based education. Now, all of these applications
and advances I talked about, they’re not science fiction. In fact, this audience
can attest to that, every one of these
scenarios is possible. At least to some extent, in some
cases, we can do this today. And that’s been as
a result of advances that we’ve seen in
science and technology over the last couple of decades. In fact, if we
step back and look at what’s happening as a result
of the unprecedented emerging technologies, we see that
they’re catalyzed, in fact, by three major trends. One, obviously, is
an enormous expansion that we’ve seen in our
computation, storage, and connectivity. Again, at the same
time, what we’re seeing is that exponential growth
in power and reduction in cost of computation,
storage, and bandwidth just to consider that
there’s essentially a supercomputer in
everyone’s pocket. And it’s always on and
it’s always connected. The second trend, of course,
has to do with digitization, data explosion, and
advances that we’re seeing in machine learning. We’re in a period,
of course, that’s called a period of
data and information that’s enabled by experimental
methods, observational studies, scientific instruments,
email, video, images, click streams,
internet transaction, and so on and so on. Data represents, of course,
a transformative currency. It’s a new currency for
science, for engineering, for commerce, and education. And it’s transforming
almost every business model and industry. It’s also accelerating
the pace of discovery in almost every
field of inquiry. And finally, the third
major trend that we’ve seen over the last decade– or 20 years, I should say–
is the ubiquitous deployment of sensors that has enabled
smart systems all around us– complemented, of course,
with advances that we’re seeing in automation and robotics. Bottom line is that we’re deeply
integrating computation data and control into physical
systems and a melding of, if you will– excuse me, melding of cyber
and the physical world has become a reality. The truth is that the digital
innovation that we’re seeing is not just additive. It’s the combination of
those is leading to advances that are exponential in nature. In fact, often there is a
major gap between milestones that we have seen
in the past has been reduced in recent decades. And the breakthroughs
that we see are coming to fruition in
a matter of sometimes years and months. Today, in some cases,
computational technologies are out-stripping,
essentially, the performance of even the most
experienced human beings. Consider, for example,
advances we’ve seen in speech recognition,
in computer vision, in facial recognition,
in robotic surgery. And in other cases,
they’re augmenting. And that’s what’s
the beauty of it is our cognitive and our
physical capabilities as we’re seeing this
in medical diagnosis, in financial market analysis,
in recommendation systems, and the list goes on. In fact, the future
is even brighter than I described to you. We’re now facing a future
that the impossible seems very achievable. We’re only a few years away
from groundbreaking discoveries that are potentially
going to transform our system of health care
and our understanding of human brain. Just give you an example of it. We’re working toward,
for example, a greater understanding of new brain areas
and kinds of synaptic changes that occur during learning
disease states and treatment conditions. Overall, these type
of technologies are poised to transform
our entire health care system to go from something that
was very reactive and episodic to a health care system that’s
much, much more proactive, is evidence based, and
focuses on quality of life. We can also envision, for
example, much smarter cities and connected cities. By 2050, it’s estimated that
2/3 of the world’s population– it’s projected to be
about 9.7 billion– will live in urban areas. Just imagine that we
can transform our cities through, essentially,
introduction and integration of technologies. But it’s going to require us
not just to bring scientists and engineers
together, but you’re going to have to
bring public policy and you’re going to have private
and public partnerships that are finding innovative
solutions to transform our cities and urban areas. And of course, when you
look at, for example, the area of global
decision making, there are now approaches that
bring layers of global data into interactive
visual systems that are going to allow us
to better understand environmental and
population changes. And when it comes
to deforestation, when it comes to
refugee flows, sea level rises, surface water changes,
pandemics, urban growth, and so on and so on. And of course the
area of transportation is completely being transformed. So what I’m sharing
with you is something that I think, to a large
extent, the academic community– and as Marty mentioned, the
computer science community– has recognized. Computational and data
intensive approaches are underpinning our economic
prosperity and global security. They’re accelerating the pace
of discovery and innovation across nearly all
fields of inquiry and are crucial to achieving
our major societal priorities. I think there is broad
recognition that is happening. Of course, technological
innovations have always disrupted the
status quo and underpinned dynamic economic changes. Today, however, as
I mentioned earlier, the scale, the scope, and
the pace of– and the impact is unprecedented. And it’s disrupting many
markets and industries. Adoption is happening as
breakthrough, speed, and scale. And of course, we’re
seeing an acceleration of the economic impact. And the society
and its structure, including our
education system, must adapt to this new paradigm. In fact, I should
step back and tell you that while in this
country we have enjoyed having the gold standard
for higher education, which is a model for the rest of the
world to copy, if you will, throughout our
history, every period of significant
technological change has been met with corresponding
waves of innovation in education. In fact, if you think back 100
years ago or so, or 100 years or so plus ago, land grant
universities to expand access– consider German-style
university ideas that were brought to this country. Consider, for example,
the Carnegie unit for standardization of higher
education, the California Master Plan. And in fact, I would go
as far as saying that many universities in this country
that are leading our higher education– including
Cornell, Johns Hopkins, MIT, University of Chicago, in fact– have run experiments. And some of them
actually were created as part of an experiment
to deal with changes that we see in technology and
the transformation of higher education that we have seen
throughout our history. So the current
environment that we’re in, I would argue that we’re at the
cusp of the next transformation of higher education. Are we at a tipping point? I’m not sure. But we’re probably at
a very close to it. As I mentioned, there’s
unprecedented pace of societal changes due
to the advances that we see in technology. There’s, of course,
greater pressure on higher education as
the engine of progress in knowledge-based
economy, and many of our higher academic
institutions in this country are at the center
of that, of course. And of course, we’re
seeing this shift from industrial, somewhat
transactional model of education that’s based on
tradition and rigid pathways to a much, much more
personalized outcome-based model of education. In fact, there’ve been number
of studies in recent years that have looked at the
impact of technology on education, on the nature of
workforce, on business models, on income inequities, and so on. In fact, one of those
highly influential reports was a report by your
colleague, Erik, from MIT and my
colleague Tom from CMU who co-authored and co-led
this national report that was commissioned by the
National Academies of Science, Engineering, and Medicine. And this report, which was
titled information Technology and the US Workforce,
Where Are We and Where Do We Go
From Here argued that recent advances in
computing and communication technologies have
had and will continue to have a profound
impact on society, and will affect almost
every occupation. This is creating large
economic benefits, but is also leading
to significant changes for our workforce. So looking at this
context, before we examine how we can incorporate these
changes into our higher education system, I want
to take a very quick look at some of the challenges
we face in higher education. And you see the context that
it provides for the discussion later on today. One challenge I think
that everyone recognizes has to do with college
affordability and access. The second has to do with
increasing demand for college educated workforce. But in particular–
as, again, Marty mentioned– demand
for students who have computational and
data-intensive knowledge. And finally, adaptability as
we see the rise of automation in the workforce. So let me spend a
couple of minutes on each of these topics. Let me first shock you
by sharing some data. Let’s look a little closer
at access and affordability. The runaway cost of education
are why so many Americans are increasingly concerned
about their children’s future in this country. And there’s no doubt that
higher education in this country has been a pathway
to social mobility. I think that’s been
one of the reasons that we have benefited
as a society. There is undoubtedly
also some skepticism about the value of
higher education. I’m going to refute
that in a moment. Consider the fact that aggregate
student debt has tripled from 2006 from about $500 million to
about $1.5 trillion dollars– that was a T, folks– in 2018. [INAUDIBLE] I’m sorry? $500 million to $1.5 trillion. $5.– trillion, that’s right. I’m sorry. $1.5 trillion,
thank you, in 2018. It should have been
billion, you’re right. Thank you. There’s a correction
on this slide. Although, I’ve shown
this a few times. Nobody else caught it. [LAUGHTER] Maybe that was wishful thinking. I’m not sure. But more seriously, consider
the fact that this $1.5 trillion dollars is actually larger
than the entire credit card debt of our nation. That’s really staggering. And by the, way
every time I show this slide, that $1.5 trillion
goes up by $100 million. Every year it’s going up
by about $100 million. The second data point, the
college tuition in this country has risen by about 538%
comparing the consumer price index increase of 121%, which
is, again, fairly significant if you consider that. In fact, despite
the rising cost, however, there is no
denying that the kind of social mobility that
education provides– this graph breaks down,
essentially, wage trends over time by education level– a chasm has opened up. And it’s actually growing
between the best educated and the least educated
in our country. Our most educated
citizens have continued to see their wages rise
robustly since the early ’70s. And I think if you just look at
the salaries of freshmen that are coming from MIT or CMU
or any other institution in this room, you can see
that there are six figure salaries for
undergraduates, for example, in computer science or
computer engineering. But our less educated
citizens, on the other hand, have seen their real income
fall since the early ’70s. In fact, labor
economists predict that the next wave of disruption
of innovation that we’re going to see that’s going
to have an impact on higher education is going
to further grow the inequality that
has placed strain on our national politics. Let me build on
that for a moment. The other data point
that’s driving the urgency of our conversation– and in fact, the initiative
that MIT has taken– is the increasing demand and
relevance for a college degree. There was a Georgetown
study a couple of years ago that showed that the
number of people with at least some post-secondary credentials
have increased by about 1% a year, but the demand
for these workers is growing about by
2% a year annually. But for much of
the 20th century, supply of college
educated workers has kept up with the demand. But for the past
three decades or so, the supply has not kept up. In fact, today, while the
job market is churning and the future is
constantly evolving and we hear all this sort
of concerns about automation displacing workers,
there’s also one thing that’s patently clear– this is a future that needs
higher education more than ever before. Let’s talk about
the issue of demand. To understand this point,
consider, for example– and this is– a
couple of studies have pointed out to
this result that I’m going to share with you. That’s 65% of students
entering elementary school now will one day work in jobs
that do not exist today. Think about that. Actually, that shouldn’t
be as surprising to us, because if you consider
over the last 10 years all the kind of jobs
that have been created as a result of advances that
we have seen in technology that didn’t even exist 10 years ago. It shouldn’t be surprising
to us that a five or a six-year-old will,
in fact, most of them, will have jobs that have
not been invented yet. I often share this
other data point, which is almost trivial, but
somewhat surprising to people. A student that comes
to MIT, an 18-year-old, or to Carnegie Mellon, after he
or she graduates in four years will be in the workforce
for the next 40 to 50 years. Think about it. 40 to 50 years. Imagine the kind of
education and foundation we have to give these students,
the next generation, that will enable them to thrive
in an economy for the next 40 or 50 years, given the
context of some of the data that I’ve shown you. So there are two
forces, of course, driving this future work. One– and MIT is in the
middle of all of this, of course– has to do
with autonomy, then the digital revolution,
which, of course, many talk about how it displaces
blue collar workers performing routine jobs. But the truth is
that it also changes the nature of work for
white collar workers in a knowledge-based economy. And in fact, there are
estimates that, for example, 50% of these jobs are
at risk at some level for significant change. The second force has to
do with the gig economy. We see a much more
liquid force that’s contributing to the shift that
we’re seeing in education. That will contribute
to the shift that we need to see in
education, I should say. Of course, I don’t need to
tell you about the gig economy. But one data point that
was quite intriguing is that the estimates are that
much of the growth that we have seen in the job sector in the
workforce over the last decade or so has been due to the
rise of the gig economy. And in fact, one
of the data points that supports that is, in fact,
the percentage of the workforce due to gig economy has
gone from 10.1% to 15.8% over the last several years. And as I mentioned,
estimated that almost all of the employment growth that
we’ve seen in the US since 2005 is due to the gig economy. I’m not trying to depress you. On the contrary, these trends
have the potential, in fact, to shape the educational
landscape significantly, hence the need for
experimentation, hence the need for thinking
about the future of the country and the future of
higher education, but not continuing to
follow the path that we have been on for many years. These trends, of course,
have the potential to reshape the
educational landscape, bringing focus on self-directed
education, lifelong learning, and topics such as
entrepreneurship as a foundational skill. So how do we
prepare our students for a changing
workforce and workplace? I want to– in the
remaining minutes that I have– to talk
about the solution space in three dimensions. One, having to do with
reimagining curriculum, second, rethinking
structure and pedagogy, and finally, considering
new models of collaboration within an institution
as well as across our academic institutions
and external partners. First, about reimagining the
curriculum to both enhance digital core skills, as well
as incorporating human skills. I think it’s pretty
well-documented. And I know that, in fact, Jim
Kurose, who’s sitting here, talks about this in
his presentations representing the
National Science Foundation that the first trend
that we must be mindful of is that growing reliance
on technology and science as drivers of new jobs. In fact, growth in STEM jobs
have outstripped overall job growth in this country. And a lot of that, of course,
in computational areas. But the US department of Labor
estimates that the STEM jobs– or I should say,
STEM-related jobs– will grow at almost double
the rate of non-STEM jobs for the next 10 years. There’s also an important
point to highlight, that– and the growth that we’re
seeing is not just because of the technology companies. Virtually every industry
and organization has become dependent on
technology for this business, and in particular computation
and data centric approaches. You see this in finance. You see this in transportation,
healthcare, energy production, distribution, and
so on and so on. I’m sure most of you, in
fact, in the CS community are familiar with
the two reports that I’m showing on the screen. And in fact, these two
influential reports have looked at how
do we deal, in fact, with broadening participation
in computing and STEM fields? How can we increase the Computer
Science core competencies across the educational system? The report was prepared by a
National Academy of Sciences Committee on the growth
of computer science undergraduate enrollment. It was co-chaired by Jerry
Cohen from CMU and CRA a vice chair at the
time Susanne Hambrusch from Purdue University. In fact, it builds
on the work that was published in CRA’s Next
Generation CS report, which was chaired by Tracy Camp. I want to acknowledge
their work. And this report
had, first of all, identified this, of
course, as a major issue. But equally
important, highlighted that context matters. And approaches taken
by one institution is not necessarily workable
in other institutions. The report highlights
limiting participation, growing programs, leveraging
resources creatively. But equally important,
their report is very forceful
about rethinking organizational structure
for computer science, both in terms of
interdisciplinary collaborations,
CS Plus X, which I know is going to be
discussed, or X Plus CS which is going to be discussed as one
of the panels later on today, as well as considering
college of computing and new organizational
structures that allow a much more porous
boundaries between units on campus. And in fact, the report
mentions that there is no one-size-fits-all. All institutions need to assess
the role of CS and related fields, and should see
this as an opportunity to plan for future success
across the entire institution, which obviously is the
model that MIT is employing. By the way, I should
also highlight that we’ve seen a
significant– while we’ve seen a significant growth in
the number of undergraduates in computer science, as Marty
mentioned, much of the growth is also happening as a
result of all other students on campus who need to learn
computational approaches, and approaches that
are data centric. So the need is
fairly broad-based across the university. But STEM is only one
part of the picture. And I want to underscore
that and spend a minute or so on that in particular. The argument that can be
made that a liberal arts education and core
human skills are just as important in the new economy. In this uncertain, and
constantly shifting, of course, landscape,
non-automatable, if you will, human skills should perhaps
serve as a foundational core competency, I should say. These skills include things such
as communication, leadership, problem solving,
critical thinking, organizational skills,
creativity, and so on. I’m really fond of this quote
from Geoff Colvin, who is one of the editors at Fortune. And in a book that he wrote
a couple of years ago, called Humans Are
Underrated– and I’ll just read the quote to you. It says, “Our greatest
advantage lies in our deepest, most
essential human abilities– empathy, creativity, social
sensitivity, storytelling, humor, relationship building,
and expressing ourselves with greater power than
logic can achieve.” So I want to underscore this,
that it is extremely important as we think about
STEM education, we also think about the
importance of developing the whole individual and
developing the students who go out to the real world having
not only disciplinary expertise in one area, but
at the same time, be able to connect
to other disciplines. At the same time that we’ve
seen the rate of progress continues to accelerate,
the societal issues and intersection of
technology and humanity will continue to become
really important. A number of institutions,
including MIT, are looking at the issues
of ethics and technology. But I want to argue that we
need to expand the discussion to include discussion of
the critical intersection between not only
ethics and technology, but also addressing issues of
security, privacy, fairness, trust, and so on. And this has to become part
of our educational system. And it has to become part of the
curriculum at our higher ed– at our academic
institutions, including dealing with issues
such as mitigating algorithmic bias, the spread of
fake news, and so on and so on. This argues that,
in fact, we need to think about this
much more holistically to bring together the
intersection of technology with policy, design,
psychology, economics, and other disciplines. I know that there is a session
later on this morning at 10:45 that’s going to
focus on this topic. And I want to acknowledge my
colleague, David Danks, who’s here, who’s going to be
serving on this panel. In fact, at CMU we’ve been
thinking about this very hard and deeply integrating
some of these issues into our curriculum. I hope that he’ll have a chance
to share some of that with you. As I’m going to
run out of time, I want to highlight the other
two points very quickly. The second one has to do
with rethinking structure and pedagogy, moving away
from transactional nature of education and potential
disciplinary silos that we have. I want to argue,
in fact, that we need to consider
experimentation, assessment, and ways of scaling
and eventually consolidation of new educational
models and structures. If you remember what I said
a few minutes ago, which is in the last 100
years every time we’ve had major technological
advances, what we’ve seen in this country is
significant experimentation and innovations in higher ed. And I absolutely believe
we’re at the cusp of one of those moments. For example, consider learning
as a lifelong endeavor. Should we rethink the relevance
of a four year degree? Should we focus on outcome
and competency, not just a transactional model
that we have bought into? Another example is to
rethink disciplinary silos. If the future is going to be
increasingly interdisciplinary, department boundaries may
need to become much, much more porous. In fact, not to make the
department heads and the deans in this room nervous, are
departments and colleges as important as they were
a few decades ago? Some of you are saying no. So I will hold my
comment any further. So these connections
that we have to build across departments
and across colleges are becoming
extremely important. In fact, every time we
experimented with this at Carnegie Mellon,
what we have seen is that the response
of our students and the response
of, essentially, the external world, people
who hire our students, has been tremendous. We experimented with that
in our neuroscience program. We introduced a neuroscience
undergraduate such that you can come into the
program having essentially the same set of
foundational courses, but you can get a degree
from our science college with a biology focus
in neuroscience. Or you can get a degree from our
social science and humanities college with a focus on
cognitive neuroscience. Or you can actually get a
degree from our computer science college with a focus on
computational neuroscience. And it’s been received
extremely well. Another example that
I’m really fond of is the IDeATe, which is at the
intersection of technology, design, media, and art. And we launched it as a minor. And the result of that has been
about 850 to 900 undergraduates out of a 7,000 population
in our institution are minoring and taking
courses in our IDeATe program. And there are a number of
other examples such as that. And I know MIT has
also experimented with similar things. The other important
point related to pedagogy is that there is, in fact, an
important role for technology that could not be understated– technology enhanced learning
and potential disruption of it on innovation. I think of a grand
challenge for education to be one that each student
has a dedicated tutor or teacher delivering
personalized learning and marginal cost. In fact, studies have
been shown that could have a significant impact
on learning outcomes. So this desire for
personalization, desire for better learning
outcomes, desire for control and controlling costs and
access can potentially be addressed by technology
and enhanced learning. Finally, the item that
I want to highlight has to do with considering
new models of engagement with the private
sector and government. And this is really beyond the
scope of the discussion today, but I want to just
get this off my chest and plant a seed with you. We need new
collaboration models. We need new policies,
public policies, that, in fact,
build that support, building human capital. Let me just share a
couple of that with you. In this country, we
need to start rethink as human capital development
as long-term investment by the private sector. In fact, we need
to think about it in the public sector or the
government tax incentives fiscal policies for
investing in human capital. In fact, much of our tax
policy in this country supports capital investment but
not human capital development. Another example of it that–
and I’m quite fond of– is thinking about creative
options for financial aid, such as income share
agreements and so on. But the list goes
on and I wanted to just share that with you. In this country, we need
to have really fairly progressive policies towards
supporting the next generation as they look at education
as the source of mobility. A couple of slides
before I wrap up. I was asked to share, also, some
thoughts on our experiences. And of course, I
want to first start by saying that local
context matters. And that’s my last bullet. That’s actually the
most important thing. There is no single
recipe for, in fact, creating the kind of higher
education that’s much, much more porous,
provides our students the kind of
foundational knowledge and competencies that they need. So the local context matters. The organizational, cultural,
budgetary environment of every institution
is different. What we have observed is that
intellectual and practical justifications are often
mutually reinforcing. By that I mean, whether
for intellectual reasons, such as we need to make sure
students have computational and data intensive skills,
or practical reasons, as Marty mentioned– 40% of the students
on this campus are majoring in computer
science or related fields. It turns out these are
mutually reinforcing and there are opportunities
for all academic institutions. We– and when I say we, I
mean computer scientists– have to take a much more
expansive but inclusive view of computing. And I think that’s
been recipe for success for institutions
like Carnegie Mellon. Also, we need to keep
disciplinary boundaries, as I mentioned, porous. And that allows us to
strengthen connections between academic units. Furthermore, continuous
experimentation and risk taking requires stakeholder
commitments. But I can’t underestimate the
importance of experimentation in this environment. And finally– again, for my
fellow computer scientists– we have to be very,
very sensitive– and I’ve lived through
this in my career– to the perception
that computer science may become insular by
becoming a separate, larger unit on a campus. I think to a large
extent, the experiments that we have had in other
institutions shows that’s probably a perception. But we have to be
really aware of it. And we have to be very
sensitive to that notion. To wrap up, Horace
Mann in 1848 said, “Education, beyond all other
devices of human origin, is the great equalizer
and a balance-wheel of social machinery.” Indeed, higher education
is unique in its power to catalyze social mobility. It can bridge social, economic,
racial, geographical divides like no other force. But if we want
education to continue to be an active force
for equality and not the inadvertent, I should
say, engine for inequality, we need to commit ourselves
to major transformations. The future is arriving
faster than ever before. And it’s looking vastly
different what we have seen it. So we must embrace
a system that allows these unbounded connections
across organization and disciplines. It further encourages and
nurtures continuous innovation new models, and of course,
supports lifelong learning as a guiding principle. With that, once again, I want
to congratulate our colleagues at MIT on the launch of
the computing college. And I look forward to watching
their success in the future. Thank you very much. [APPLAUSE]