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A Campus Coming Alive

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One of the great myths about universities is that they close up shop in the summer months. From a provost’s vantage point, the summer months do have a distinct character but not much of a slowdown. There are many summer professional workshops providing research exchanges among scholars, high school college preparatory institutes, faculty in laboratories and offices doing their research, and many traditional summer classes. So, there is a large amount of activity, but it is different than that of the academic year.

In the summer, many traditional degree students are away from campus, in internships or jobs. Many faculty are not on campus but working away, in archives or in the home institutions of collaborators. Study abroad opportunities exist for degree students.

In essence, the summer campus is busy, but the bodies on campus are disproportionately visitors.

With each passing day over the last week, as we approach the start of the fall semester, the atmosphere on campus is changing.

Construction workers, painters, and repair technicians are working overtime to finish updating classrooms, offices, and dormitories in preparation for the new semester that starts next week. Grounds crews are cleaning and weeding (between what seems to be constant thunderstorms).

Those who organize the move-in process for students have been in constant preparation for weeks. Emails have gone out to staff to either telecommute or take public transportation to campus on move-in days. A campus with multiple construction projects ongoing makes the move-in process more complicated than usual.

This week, the orientation leaders for New Student Orientation (NSO) arrived, and the level of noise and laughter is rapidly increasing. The NSO leaders are upper level students who come early to welcome the first-year students and help them move in. It’s a great tradition and is a great example of the spirit of Georgetown intent on building community. Friday, they will be spread throughout campus, singing, dancing and extending welcoming hands at the move-in.

The average age of people walking across campus is plummeting, seemingly hour by hour. There are some early family groups walking the campus, with apparent first year students, probably combining a short DC vacation with move-in. The heat has been oppressive the last few days and the groups are uniformly wilted. (I wonder whether we should put out the public water stations earlier than move-in day itself.) Each day, there seem to be more students pulling rollerboards across campus, loaded down with more than they will carry the rest of the year in their comings and goings. Empty boxes from summer storage companies are starting to appear near dumpsters.

Walking into Healy Hall, there was a line of young folks taking turns standing in front of or sitting on the statue of John Carroll. He’ll get more attention in the next few days than the last few months. I like to think he’s missed the students, too.

The changes bring back the not-too-distant memories of the buzz that exists on all residential university campuses when the academic year is in full throttle. The gang is back. They animate the space in a way quite distinctive of the research and service activities of the university. Part of this is the optimism of youth. Part of it is the excitement that comes with building one’s future and exploring life in new ways.

Each fall the coming of new students reminds us of the deep honor we have to hold the positions we have.

Eating our Seed Corn

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The expression “eating our seed corn” comes from times of severe drought or other weather induced tragedies when farm families’ near-term survival threatened their long-term survival. With each harvest of corn, ideally some of the seed is not consumed, but retained for planting the next year’s crop. Without this “seed corn,” no next year’s crop can be produced. Eating the seed corn provides short-run survival, but portends disaster for the family in the following year. This phenomenon may offer a metaphor for the impact of social trends on support for scholarly inquiry.

We are a world fascinated by the newest device or platform invented through new technology. These new technologies have offered transformative and positive changes to billions of people. The innovation culture spawning these benefits has several key ingredients – disruption of key features of the status quo, quick iterative improvements through a “fail fast” feature, and equally quick abandonment of ideas that fail to ramp. The culture is weakened by another feature, the so-called Gartner phases of innovation, which includes a “hype phase” that generally greatly overstates the likely benefits of a new technology.

These features have proven themselves amazingly efficient when a) the basic components of a new solution have been developed and proven useable in another domain, b) the assembly of new components offers a set of capabilities that were never before packaged in one service/entity, and c) a market for the new assembled entity is demonstrable. For example, Uber’s success builds on the messaging features of an internet platform, real-time GPS locational information for matching cars to requestors, credit-card electronic payment systems, the ubiquity of smart phones, and the untapped capacity of owners of autos seeking income supplementation. In short, the success of the idea rested on an effective assembly of existing components.

The genius of new technology can easily overshadow one important feature of societal innovation – the basic inquiry that led to the various components being available for assembly. For example, the current hype is focused on artificial intelligence, with boasts that it will replace most human thought and activity within a few years. It’s fair to argue that many AI applications find one of their roots in a 1948 paper by Claude Shannon, “A Mathematical Theory of Communication,” a paper that proffers the key framework underlying most of the key components of artificial intelligence (but also cryptography and data compression). In what could have been criticized as idle play by others, Shannon built a mechanical mouse that could “learn” its way through a maze in 1950; an act that might be easily criticized as child’s play. Theoretical breakthroughs often start with trivial implementations (if any implementation occurs at all), decades before their impactful application.

Would we be seriously planning for autonomous cars and trucks without the 1948 paper? What forces created the environment for Shannon to write the paper? What investments in talent are we making now that gives us assurance that the basic inquiry that is necessary for the innovation of the year 2060 is now occurring?

At one time scientists studying how a computer could play chess were ridiculed as frivolous. What basic questions are being addressed now that are easy candidates for ridicule (e.g., a study of the immune system of shrimp compromised in farmed waters using a treadmill)?

In a way, much of wonderful technological innovation we are experiencing now is based on a harvest of knowledge components that whose “planting” occurred years ago. Are we supporting the young minds who will discover the basic breakthroughs that will permit the innovation of 2060? Or, are they being drawn to other environments that support the next big innovation possible based on merely recombining existing knowledge? These minds and the basic discoveries they can make are our seed corn.

Mine the Gap

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I’m having a great time these days working on episodes of a podcast with Georgetown faculty about their research lives. (see SoundCloud: and on Spotify:

It’s really fun for me to see how different faculty choose the projects they work on. I learn about their passion for a set of questions that is so fundamental that they devote their entire lives becoming more and more sophisticated in their knowledge about them.

Many episodes also discuss how their teaching and research lives intersect and reinforce one another. However, as I reflect on the various discussions, I am impressed how stark are the differences between many traditional classes and the research lives of the faculty who teach those classes.

The typical course at a university is a highly curated collection of content. Typically, the content consists of the very best scholarship in the field. The latest consensus of the field is presented through readings and lectures using the best research products that produced that consensus. Syllabi typically arrange the content in a manner that emphasizes a cumulative set of themes. The order of the course promotes a synthesis of the content of various weeks to achieve the given learning goals. When controversies in a field exist, the course carefully presents the alternative conceptual underpinnings or alternative interpretations, each of them undergirded with the best evidence behind the alternative viewpoints. Clarity of content and purposeful organization predominate.

Missing from a typical class, is content that is of lower quality, content that offers intermediate findings, content of studies that did not replicate, content of work that was presented at professional conferences and never published. The product of failed or mediocre scholarship rarely appears in syllabi. The chaos that typically exists at the edges of knowledge in a field is often absent.

The research lives of faculty members, on the other hand, are rarely as settled and as perfectly organized as the courses they teach. The beautifully designed syllabus, delivered in highly polished class activities, bears little resemblance to the day-to-day, step-by-step process of extending one’s understanding of a field.

First, faculty read much literature that could never pass the standards of a class syllabus. They puzzle over contradictory findings and interpretations not yet resolved within a field. They listen to substandard presentations at professional meetings. They read outside their field, looking for new angles of attacking their favorite problem inside their field. There is typically a lot of chaff hiding the wheat.

Second, research lives focus on the absence of existing content. Scholars are searching for the unanswered questions. They are seeking to fill the gaps in the literature of the field. They “mine” the gap (sorry for the pun). If human knowledge is like Swiss cheese, students are given the cheese, and researchers are fascinated with the holes.

Third, the research life of the faculty is filled with failures. The “hit rate” is very low for doing an experiment with notable results, for finding a document of key importance in an archive, for creating an interpretation that sustains criticism, or for inventing a book project that merits completion and publication. The most successful scholars are masters of failing fast and often, all in a quest to disrupt the current accepted knowledge. Each of them has files filled with ideas that didn’t pan out.

It occurs to me that much of life’s challenges are more like the research lives of faculty than the experiences of students in a traditional class. Life presents the absence of content — a poorly described problem, an inarticulate question, a puzzle without an obvious solution. The successful learn how to fill the gap with newly acquired knowledge.

In this context, those class experiences that incorporate problems, devoid of obvious content regarding their solutions, can perhaps offer students a useful lasting lesson. Georgetown faculty who are working so inventively to create experience-based  and research-based learning environments are doing this. They are teaching our students how to mine the gap.

Trust in Others and Trust in Government

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I have written before on the diminishing level of trust reported by the US public in key institutions.

A new Pew Research Center report on trust  updates some existing measures and adds some new ones.

The overall level of trust in institutions seems quite similar to prior measured levels. Things don’t look like they’re getting dramatically better.  Interestingly, however, only a minority of respondents report that lack of confidence is a “top-tier” problem facing the country. The issues garnering widespread views as top tier problems are things like drug addiction, health care coast, ethics in government, and affordability of education. On the other hand, a majority of people report that the greater lack of trust in government makes it difficult to solve a whole host of commonly mentioned problems faceting the nation (e.g., health care, immigration, climate issues). Over 2/3 report they believe the government intentionally withholds important information from the public, which could be safely released. Further, those who believe this trend to report less trust of the federal government. In speculating how could trust be rebuilt, calls for greater transparency commonly are mentioned.

What was interesting in the new report are measures of interpersonal trust. Do we have trust and confidence in one another? A majority believe that Americans have too little trust in other another, and that that makes it harder to address real problems facing the country. When asked why they believe we trust one another less than earlier, they speculate that how media treats negative news or list a set of societal problems that indirectly promote distrust. About 10% say that Americans have become more “lazy, greedy, and dishonest.”

Trust seems highly correlated to age, with older adults usually expressing more trust in institutions and groups (e.g., military, scientists, religious leaders). It’s also true that more of those with higher educational attainment report trust in others.

On the hopeful side, the findings show that people have faith in others doing the “right thing,” but the percentage is deeply dependent on what issue is being considered. For example, over ¾ believe others will report a serious problem to local authorities or will obey federal and state law, but less than half believe others cast well-informed votes in elections or have civil conversations with people with views different from their own.

There is good news in that a vast majority (over 90%) of people believe it’s important to raise the level of confidence Americans have in the Federal government. Similar results pertain to interpersonal trust. In speculating on what could be done to improve our level of confidence in one another, they often point to the role that local communities can play in trust-building.

Finally, it seems that perceived levels of interpersonal trust are related to trust in the federal government; the two tend to go hand in hand.

Although only a minor finding of the work, the notion that smaller communities may be the source of rebuilding trust is intriguing. The finding fits other work that trust requires long term reciprocated acts of benefit among actors. With the shared knowledge that is common in smaller groups, transparency is easier to attain. I could imagine followup studies to ask the question about whether those of us who enjoy trusting relationships in smaller groups, tend to more easily hold or regain trust in societal-level entities like the federal government. Greater insight into how trust in institutions can be rebuilt would benefit us all.

A Network Model for Research and Implementation

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In the long history of universities, a hierarchical organizational model predominates. Faculty are organized into departments or similar units; unit heads administratively report to a dean of a school; deans report to a provost; the provost reports to the president; the president reports to a board.

There exists, however, an alternative organization that arises among scholars throughout the world. As human knowledge becomes more and more sophisticated, it’s likely that an individual faculty has more ties with scholars working far away from his/her campus, but concentrating on topics very similar to their own. These scholars find each other in professional meetings or through followup communication about individual publications. They form networks. The network collaborates on defining the pressing issues of the field. When possible, they collaborate on research, sharing their expertise, learning from each other, and increasing the depth of knowledge in the field. They link together their own students to become new members. When possible, they nurture each other’s careers. In some sense, they form their own community.

There are attempts in research funding agencies to deliberately form networks. The MacArthur Foundation used a network model to help define needed research in aging and in transitions to adulthood, among others. It labels these as “research institutions without walls.” Sometimes, sustainable networks form around shared facilities. Some of the creation of research centers by the National Science Foundation bring with them a network model of scientists connected to each center. CERN forms a network of facilities but also teams of scientists throughout the world, sharing that infrastructure. Libraries and archives sometimes have formal programs of building networks of scholars.

One of the ongoing duties of university leadership is to build environments for faculty to be maximally productive in their research lives. To what extent can networks of units be a tool to increase this productivity?

Georgetown proudly has many different units addressing issues related to one another. One example is the set of units tackling the impacts of technology on social norms, regulations, and governance, which we’ve labeled the Georgetown Tech and Society Initiative (Center for Privacy and Technology, Institute for Technology Policy and Law, Massive Data Institute, Beeck Center, and hoped for new units, the PolicyLab, and the Center for Digital Ethics) form a natural network of synergistic activities. In what ways could a network model increase the impact of these individual units by offering infrastructure support for collaboration among them?

There are other Georgetown examples that could be added to that above.

The preference of a network model over a hierarchical model is that the individual units retain their identity and autonomy to fulfill their mission unimpeded by the need for adoption of a new mission of a higher-order entity.

So, the university issue is what makes for sustainable networks of autonomous but synergistic units? There seem to be multiple answers to this question. Some are shared infrastructure that may be expensive for each unit alone to support. This would include proposal development support to garner more financial resources for the units. It would include business and financial functions that are necessary to fulfill the obligations to external funders. It would include a communications function for larger networks, to publicize the work of the units and promote the network as enhancing the productivity of the centers. It would include postdoctoral fellows, graduate fellows, undergraduate fellows, who would work across the units and form an intellectual glue among the units. Perhaps, most importantly it would offer a common home, a space in which network nodes could interact and nurture their collaboration.

The goal of networks would be that collaborative activities among the constituent units would increase in volume. Through that collaboration, one would hope that the units would collectively be freed to achieve great impact.

Overdosing on Change?

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We all seem to be living through a time of rapid change.

Part of these feelings of experiencing rapid change comes, no doubt, from careful attention to news media, alerts we receive on our mobile phone, the ubiquitous “Breaking News” moniker on every screen. We now learn of a bus accident that killed three people in a remote rural area of a country far away from us, complete with pictures, within hours of the event. With such connectedness, there is a lot to cover, and we can literally see it all with a few clicks on a mobile phone. So, it seems that events are occurring faster and faster because we can follow so many events simultaneously.

Immediately after the 9/11 events, I mounted a repeated survey of a national sample of adults, containing a battery of self-report psychological health measures. We tracked such self-reported well-being over time as the days and weeks passed. A finding I will always remember is that those respondents who kept close attention to the media stories of the events after the attack, suffered from reduced well-being for a much longer period of time, relative to those who paid less attention to such media. Such attention seemed to keep the psychological wounds fresher for longer periods of time. Based on this finding, one wonders how much of a sense of living in a moment of rapid change is a function of how much attention is paid to very short cycled new media.

These thoughts may also apply to anyone in a work organization or some institution that also is undergoing change. For example, US universities are facing threats to Federal government financial support and increasing costs from demands for new academic programs, facilities, and student services. Just as economic inequality is a concern among US households, inequality in financial resources among elite private universities, state universities, and small liberal arts colleges is inducing change in the eco-system of US higher education. Further, the coming cohorts of students will come from life experiences very different from those of the last two decades. To optimally serve those students, changes in US universities must occur.

Similarly, in private sector organizations, externally influenced changes abound. Retail stores of a “brick and mortar” type and small businesses are being rapidly affected by internet-based consumption. Shopping malls, once thriving, are filled with empty storefronts. Some close completely. Department stores, offering large inventories of diverse products, are most vulnerable. Generational effects in consumer behavior seem large; younger shoppers claim never to visit stores except virtually. The manufacturers to the retail sector, once a stable industry, are undergoing real change.

With such rapid changes externally induced on organizations, one wonders how it affects the taste for voluntary change within the organizations. In this context, are proposals for change within organizations affected by the general feeling that the rate of change in the larger society is rapid, out of one’s control, and filled with fearful consequences? Do more and more people tend to seek stability in their social worlds (which they partially control) and in their work organizations, in reaction to the feeling of overwhelming rapid change in the larger world? Alternatively, do the feelings of unrelenting change in the external world spur a sense of need for innovation in other aspects of their lives?

Social Justice Technology

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Over the last few days, I was introduced to a very clever idea that serves clear social justice goals. It’s worth noting.

As we all know, there are large numbers of workers who clean houses and apartments, provide home care for children, provide services to elderly residents, deliver repeated lawn and garden services to homeowners. These workers share the attribute that they have many different clients or employers. They work for one client perhaps only a few hours every two or three weeks. Their clients change over time.

The demographics of the labor market that provides these services is disproportionately female. They are people of color in higher proportions than is true in the total population. Their education levels are lower than average. They tend to work without contracts with their clients. They are vulnerable to nonpayment for services and other abuses, as payment is often dependent on direct request from the worker.

There are between 2 and 3 million such workers at any given point. The vast majority have no provision for paid days off. When their family needs their time for care, they lose all income for their days away. Most have no insurance coverage.

So, the NDWA labs, partnering with, built out Alia (, a portable benefits platform for domestic workers. NDWA is the National Domestic Workers Alliance, an organization that seeks to support domestic workers and improve their conditions.

If you are a client of a domestic worker, you create an account on Alia, providing the mobile phone number of the worker, and enter an amount of benefits that you wish to provide to the worker. The benefit total is charged against the client’s credit card automatically at the amount and timing specified by the client. (Alia suggests $5 per cleaning event, for example.) Since most domestic workers have 5-10 clients, Alia estimates an average accumulation of $75 per month. This amounts to perhaps as many as 7 paid days off per year. In addition, Alia membership provides a $5,000 life insurance benefits to the worker.

In short, the platform is the accumulator of relatively small contributions from each client. When a client is dropped, they cease their Alia payments; when a new client is obtained, they are added as contributors to the worker’s account. Alia benefits are attached to the worker, not the job.

When the worker needs to draw on their Alia account, for example, for a sick day or for care for their own child, s/he requests a VISA gift card, which s/he can use to purchase anything needed from that day’s earnings.

The current outreach of the platform is to the clients of the domestic worker. It portrays the contribution as an expression of appreciation and care for the well-being of the worker by the client. It urges a set of clients of one domestic worker to offer these benefits jointly, by contacting one another and sharing the cost of the benefit accumulation. Rather that each client adding a small amount of money to each cash payment to the worker, Alia keeps track of the benefits for the worker and the client. The cumulated benefits stay with the worker until they are used.

The platform can obviously grow in many different directions – offering retirement benefit support, assisting clients in calibrating their contributions, etc.

I found it an interesting example of how technology can provide real benefits to an underserved set of workers. I wish Alia great good fortune.

Data Wastage, Data Recycling

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Using the metaphor of food production and consumption offers some interesting insights into efforts to use data for social good. For example, many reports speculate that over 40% of the food produced in the US is wasted; that is, not consumed by humans. Organizations like Food Rescue attempt to collect such food for distribution to those in need of food. Some of this collection is from organizations that had planned for human consumption but failed (e.g., restaurants, grocery stores).

One difference between food and data is that old data, for some uses, doesn’t spoil. Indeed, “consuming” data for one purpose doesn’t destroy them for another purpose. If there is another use for the same data, they can be used over and over.

Much of machine learning and artificial intelligence uses of data begin with a stock of data to extract prediction of some phenomenon of interest. For example, given all data known, what is the probability that a specific type of person will click on a popup internet ad? The practical use of these analyses is to price displays for ads to maximize sales of the advertising entity. Every click or failure to click then provides a new observation, which (along with any other new observations on a case) can be added into the data resources in hopes of improving the prediction at the next moment. The key feature is to predict the next state of some process, ideally in real time. Computation speed and richness of data permit such modeling to drive automobiles with enough effectiveness that driverless cars are an active endeavor.

Much academic use of data is quite different. Analyses seek understanding (e.g., to understand income dynamics of families, to measure the precursors to health conditions, to monitor the productivity changes in the economy). The use of the data attempts to gain insights into the processes that produce some phenomena (e.g., does divorce lead to economic hardship for children in families, to what extent does physical exercise prevent chronic health conditions, does implementation of new computer technology increase the dollar output of a company per employee?) The questions often involve multiple outcomes simultaneously in attempt to understand whether there are important mechanisms across different phenomena. For example, how does educational attainment affect health-related behaviors (e.g., dietary habits)? Do any effects flow through the fact that higher education groups tend to have incomes that permit access to better food options? Does education itself teach people the linkage between diet and health? Do the social environments of higher education people provide social support for enhanced physical activity and through that produce higher concern for healthy eating? Such questions are of interest from the perspectives of seeking identification of the causal connections among attributes. Such causal understanding is important is designing interventions that attempt to improve the final outcome of interest (e.g., would it be more efficient and effective to introduce healthy-eating messaging in public spaces frequented by low education groups or to launch a campaign for physical fitness?).

Some uses of data in companies are for prediction of the next observation. Hourly retail sales in a retail company can be used for staff deployment, just in time stock replacement and a variety of other management decisions. This “nowcasting” or real-time decision guidance is in sharp contrast to the theory-testing or identification of causal mechanisms for phenomena of interest. Indeed, the value of “old” data for nowcasting is minimal. On the other hand, some of these data might be reused to provide benefits of greater understanding of social processes.

Since the primary purpose of much digital data now being produced is real-time prediction, one wonders whether a data recycling movement might be usefully launched now. Just as repurposing unused food can serve users who were not the intended first uses, so too data recycling might provide social benefits for secondary purposes. Since the data from many commercial transactions with the public arise solely from actions of individual people, this data recycling notion can be viewed as a service back to those whose data records are collected by the companies. If the uses of the data were beneficial to the whole society, public trust in the company might increase as a function of their recycling efforts.

Data wastage can be reduced through data recycling for the benefit of all.

The Science of Learning in Practice

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I was treated to a lecture a few days ago about the mismatch between the cumulated research in the social science and neuroscience of learning and the traditional 20th century university class organization. It was both confirming of much of the educational innovation occurring at Georgetown and humbling at what more needs to be done.

It began with an attack on a mindset among many of us devoted to our individual fields or professional affiliations. It’s an error, the speaker asserted, to state “this is what one needs to know to be called an X,” where X might be a chemist or an economist or a classicist. Our goal as educators was not to seek among our students a mastery of certain content. Such a goal was inappropriate for two reasons: a) the “content” of the field is constantly changing; disciplines and fields evolve; what is knowable today is not what will be knowable tomorrow, and b) mastering only content cheats the student of the field’s “way of thinking.” By “way of thinking” is meant how the field will incorporate new observations, findings, events, into its cumulative accepted knowledge. Knowing just the content of a field doesn’t inform how a field determines was is new and important versus what is false or trivial.

The problem with thinking of curricula as merely transmitting content to a student is that it overlooks the need for the student to take ownership of their own learning the field’s way of thinking.

The speaker relayed the story of an alumnus returning to campus to visit his favorite college instructor. The student received two top grades in the instructor’s class. Out of curiosity the instructor asked the student what were the most important facts that he learned in the classes. The alumnus was stumped, could not remember a single “fact” reviewed in the class, despite utter mastery of the content at the end of the class. The moral of the story? Humans don’t retain the ability to verbalize content knowledge very long. The distinct belief in the alumnus that the courses were most productive for him had become implicit to his knowledge base – he could not verbalize them even though he knew they would important to him. Instead of a set of recallable facts, the knowledge had become how he approached problems he encountered.

Research on learning appears to have some basic replicated findings:

  • Humans appear to do better at learning when the lessons are spread over time. Short intensive bursts of attention, when not reinforced in later encounters, threatened later memory retrieval problems. (This fits surveys of alumni finding that the greatest satisfaction with learning among those who as students spent a whole year working on a topic, for example, in a two-semester integrated course sequence.)
  •  “Interleaved learning,” where students study multiple topics in a mix appears to generate more lasting learning than an approach where intensive work on one topic exclusively is then followed by intensive work on a separate topic. (Interdisciplinary work, which treats one problems from multiple perspectives, may feature such properties.)
  • Learning that requires actions on the part of the students outperforms passive learning consistently. (This fits all the experiences of Georgetown faculty introducing project-based and research-based learning into their courses.)

The 50-minute lecture, followed by out-of-class readings, followed by content-based examinations seems far away from these principles.

Other of the principles suggest more coordination among classes within a program, an intentional layering with designed repetition.

Finally, all the research is pointing in the direction of the superior value of learning through action, under the mentorship of one more deeply knowledgeable. Learning in the midst of problem-solving offers a nature layering of actions that would seem quite well suited to the engaged learning that leads to lifetime learning skills.

We’re living at the time in which designing learning environments can truly be guided by well-established research findings. We’re lucky.


If you’re interested in learning about the research lives and scholarly passions of Georgetown faculty, try a listen to the Provost’s Podcast, “Faculty in Research.”  The newest episode is with philosopher/psychologist Nancy Sherman. Listen at

Ingredients of Problem-Oriented Centers and Institutes

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Georgetown, as a Jesuit institution, has special mission among universities. Like all of them, its task is to educate the next generation of leaders in many different fields, to expand and disseminate new human knowledge and understanding, and, as an institution, to contribute to the common good of the society.

Unlike some other institutions, however, Georgetown attempts to integrate these three goals. For example, it seeks to form “women and men for others” through its educational activities. This means that the education mission and the common good mission are joined together. It has courses which motivate, design, and implement community outreach and social justice interventions.

Over the past few months, some ideas that are coming to the fore from faculty and students could be characterized as attempts to coordinate not just two, but all three of the goals of the university – formation, inquiry, common good. Indeed, taken together these ideas form a logical evolutionary step for the university. They seek research leading to action serving the common good integrated into the educational activities through new Centers or Institutes.

Many of the ideas are defined around problems that face all parts of the world. They tend to be complicated issues, not yielding themselves to solutions from one campus, one school, or one department. Many of them disproportionately affect the poor or otherwise disadvantaged. These are areas like the threats of epidemic infectious diseases, the impacts of technology on society, economic and social development, environmental amelioration, and so on.

Although these ideas require very different sets of knowledge and human resources, they seem to share various needs.

Because all are interdisciplinary or transdisciplinary they will succeed only by combining talent across traditional pillars of the university. However, one can’t assemble a strong interdisciplinary group without the existence of strong disciplines.

Because the initiatives are all problem-oriented, the initiatives will succeed only if the participants are passionate about seeking solutions to the problem. The actors must leave their disciplinary allegiances at the door. They must be curious about different perspectives; they must be respectful of knowledge extracted from different fields. They must let the problem define what parts of their disciplinary knowledge is relevant.

Because the initiatives seek to integrate serving the common good and research, the participants in the initiatives need to mount activities to implement the knowledge in practical settings. Theory development must serve effective action. For some disciplines, an action step implementing knowledge in the real world is unusual.

Because of this action step, the initiatives need an intentional mix of tenure-line faculty, research faculty, postdoctoral fellows, graduate and undergraduate assistants, and professional staff. The diversity of staff is quite unlike that of a traditional teaching department.

The initiatives will profit from mixing different ways of approaching a problem. For example, they might profit from mixing design thinking, systems engineering approaches, computational intensive analysis, entrepreneurial approaches, and others.

Assembling an effective team, with alternative perspectives, but all passionate about finding solutions to an important problem requires perseverance and time, to exchange perspectives and language, in order to discover previously undetected insights from multiple fields. Effective interdisciplinary groups rarely quickly succeed on complex problems, but often only such teams can succeed.

Finally, teams solving big problems require their own physical home, where those passionate about the work can interact and teach one another. Novel mixes of students, faculty, and staff can achieve ambitious shared goals when they are “down the hall” from one another.

As we aspire to serve others more impactfully, we must build an environment that supports those aspirations.

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