The quote I was going to use to introduce this topic — “You’re entitled to your own opinion, but not to your own facts” — itself illustrates my theme for today: that truths are often less than well founded, and so can turn policy discussions weird.

I’d always heard the quote attributed to Pat Moynihan, an influential sociologist who co-wrote Beyond the Melting Pot with Nathan Glazer, directed the MIT-Harvard Joint Center for Urban Studies shortly before I worked there (and left behind a closet full of Scotch, which stemmed from his perhaps apocryphal rule that no meeting extend beyond 4pm without a bottle on the table), and later served as a widely respected Senator from New York. The collective viziers of Wikipedia have found other attributions for the quote, however. (This has me once again looking for the source of “There go my people, I must go join them, for I am their leader,” supposedly Mahatma Gandhi but apparently some French general — but I digress.). The quote will need to stand on its own.

Here’s the Scott Jaschik item from Inside Higher Education that triggered today’s Rumination:

A new survey from ACT shows the continued gap between those who teach in high school and those who teach in college when it comes to their perceptions of the college preparation of today’s students. Nearly 90 percent of high school teachers told ACT that their students are either “well” or “very well” prepared for college-level work in their subject area after leaving their courses. But only 26 percent of college instructors reported that their incoming students are either “well” or “very well” prepared for first-year credit-bearing courses in their subject area. The percentages are virtually unchanged from a similar survey in 2009.

This is precisely what Moynihan (or whoever) had in mind: two parties to an important discussion each bearing their own data, and therefore unable to agree on the problem or how to address it. The teachers presumably think the professors have unreasonable expectations, or don’t work very hard to bring their students along; the professors presumably think the teachers aren’t doing their job. Each side therefore believes the problem lies on the other, and has data to prove that. Collaboration is unlikely, progress ditto. This is what Moynihan had observed about the federal social policy process.

The ACT survey reminded me of a similar finding that emerged back when I was doing college-choice research. I can’t locate a citation, but I recall hearing about a study that surveyed students who had been admitted to several different colleges.

The clever wrinkle in the study was that the students received several different survey queries, each purporting to be from one of the colleges to which he or she had been admitted, and each asking the student about the reasons for accepting or declining the admission offer. Here’s what they found: students told the institution they’d accepted that the reason was excellent academic quality, but they told the institutions they’d declined that the reason was better financial aid from the one they’d accepted.

More recently, I was talking to a colleague in a another media company who was concerned about the volume of copyright infringement on a local campus. According to the company, the campus was hosting a great deal of copyright infringementl, as measured by the volume of requests for infringing material being sent out by BitTorrent. But according to the campus, a scan of the campus network identified very few hosts running the peer-to-peer applications. The colleague thought the campus was blowing smoke, the campus thought the company’s statistics were wrong.

Although these three examples seem similar — parties disagreeing about facts — in fact they’re a bit different.

• In the teacher/professor example, the different conclusions presumably stem from different (and unshared) definitions of “”prepared for college-level work”.
• In the accepted/decline example, the different explanations possibly stem from students’ not wanting to offend the declined institution by questioning its quality, or wanting think of their actual choice as good rather than cheap.
• In the infringement/application case, the different explanations stem from divergent metrics.

We’ve seen similar issues arise around institutional attributes in higher education. Do ratings like those from US News & World Report gather their own data, for example, or rely on presumably neutral sources such as the National Center for Educational Statistics? This is critical where results have major reputational effects — consider George Washington University’s inflation of class-rank admissions data, and similar earlier issues with Claremont McKenna, Emory, Villanova, and others.

I’d been thinking about this because in my current job it’s quite important to understand patterns of copyright infringement on campuses. It would be good to figure out which campuses seem to have relatively low infringement rates, and to explore and document their policies and practices lest other campuses might benefit. For somewhat different reasons, it would be good to figure out which campuses seem to have relatively high infringement rates, so that they could be encouraged adopt different policies and practices.

But here we run into the accept/decline problem. If the point to data collection is to identify and celebrate effective practice, there are lots of incentives for campuses to participate. But if the point is to identify and pressure less effective campuses, the incentives are otherwise.

Compounding the problem, there are different ways to measure the problem:

• One can rely on externally generated complaints, whose volume can vary for reasons having nothing to do with the volume of infringement,
• one can rely on internal assessments of network traffic, which can be inadvertently selective, and/or
• one can rely on external measures such as the volume of queries to known sources of infringement;

I’m sure there are others — and that’s without getting into the religious wars about copyright, middlemen, and so forth I addressed in an earlier post).

There’s no full solution to this problem. But there are two things that help: collaboration and openness.

• By “collaboration,” I mean that parties to questions of policy or practice should work together to define and ideally collect data; that way, arguments can focus on substance.
• By “openness,” I mean that wherever possible raw data, perhaps anonymized, should accompany analysis and advocacy based on those data.

As an example what this means, here are some thoughts for one of my upcoming challenges — figuring out how to identify campuses that might be models for others to follow, and also campuses that should probably follow them. Achieving this is important, but improperly done it can easily come to resemble the “top 25” lists from RIAA and MPAA that became so controversial and counterproductive a few years ago. The “top 25” lists became controversial partly because their methodology was suspect, partly because the underlying data were never available, and partly because they ignored the other end of the continuum, that is, institutions that had somehow managed to elicit very few Digital Millennium Copyright Act (DMCA) notices.

It’s clear there are various sources of data, even without internal access to campus network data:

• counts of DMCA notices sent by various copyright holders (some of which send notices methodically, following reasonably robust and consistent procedures, and some of which don’t),
• counts of queries involving major infringing sites, and/or
• network volume measures for major infringing protocols.

Those last two yield voluminous data, and so usually require sampling or data reduction of some kind. And not all queries or protocols they follow involve infringement. It’s also clear, from earlier studies, that there’s substantial variation in these counts over time and even across similar campuses.

This means it will be important for my database, if I can create one, to include several different measures, especially counts from different sources for different materials, and to do that over a reasonable period of time. Integrating all this into a single dataset will require lots of collaboration among the providers. Moreover, the raw data necessarily will identify individual institutions, and releasing them that way would probably cause more opposition than support. Clumping them all together would bypass that problem, but also cover up important variation. So it makes much more sense to disguise rather than clump — that is, to identify institutions by a code name and enough attributes to describe them but not to identify them.

It’ll then be important to be transparent: to lay out the detailed methodology used to “rank” campuses (as, for example, US News now does), and to share the disguised data so others can try different methodologies.

At a more general level, what I draw from the various examples is this: If organizations are to set policy and frame practice based on data — to become “data-driven organizations,” in the current parlance — then they must put serious effort into the source, quality, and accessibility of data. That’s especially true for “big data,” even though many current “big data” advocates wrongly believe that volume somehow compensates for quality.

If we’re going to have productive debates about policy and practice in connection with copyright infringment or anything else, we need to listen to Moynihan: To have our own opinions, but to share our data.

Dinner talk turned from Argo and Zero Dark Thirty to movies more generally. A 21-year-old college senior—I’ll call her “S”—recognized most of the films we were discussing. She had seen several, but others she hadn’t, which was a bit surprising, since S was an arts major, wanted to be a screenwriter, and was enthusiastic about her first choice for graduate school: the screenwriting program at a major California institution focused on the movie industry.

S had older brothers in the movie business, and she already had begun writing. What she needed, S said, was broader and deeper exposure to what made good screenplays. Graduate school would provide “deeper.” Her plan for “broader” was to watch as many well-regarded classics as possible, and apparently we were helping her map out that strategy.

But many of the films she wanted to see weren’t available on cable in her dormitory, even as pay-per-view. “Buying” or “renting” them online she found too expensive and awkward, especially given the number of films she wanted to see. So S was doing what unfortunately many students (and others) do: looking for movies on the Internet, and then streaming or downloading the least expensive version she could find. Since S’s college dormitory provided good Internet connectivity, S used that to download or stream her movies. Usually, she said, the least expensive version was an unauthorized copy, a so-called “pirate” version.

Some of us challenged her: Didn’t S realize that downloading or streaming “pirated” copies was against the law? Was she not concerned about the possible consequences? As a budding screenwriter, would she want others to do as she was doing, and deprive her of royalties? Didn’t it just seem wrong to take something without the owner’s permission?

S listened carefully—she was pretty sharp—but she didn’t seem convinced. Indeed, she seemed to feel that her choice to use unauthorized copies was reasonable, given the limited and unsatisfactory alternatives provided by the movie industry.

In so believing, S was echoing the persistent mythology of the lost generation. I first heard Cary Sherman, the President of the Recording Industry Association of America (RIAA), use “the lost generation” to describe the approximately 25 million students who became digital consumers between two milestones: Napster‘s debut in 1999, which made sharing of MP3s ripped from CDs easy, and Apple’s discontinuing digital rights management (DRM) for most iTunes music in 2009, which made buying tracks legally almost as easy and convenient.

Even without the illusion that infringing materials were “free,” there were ample incentives to infringe during that period: illegal mechanisms were comprehensive and easy to use, for the most part, whereas legal mechanisms did not exist, were inflexible and awkward, and/or did not include many widely-desired items.

Because of this, many members of the lost generation adopted a mythology comprising some subset of

• digital materials are priced too high, since it costs money to manufacture CDs and DVDs but the Internet is free,
• profits flow to middlemen rather than artists, and so artists aren’t hurt by infringement,
• DRM is just the industry’s mechanism for controlling users and rationing information,
• people who stream or download unauthorized copies wouldn’t have bought legal copies anyway, and so copyright holders don’t lose any revenue because of unauthorized copying,
• there’s no way to sample material before buying it, and so unauthorized sources are the only easy way to explore new or arcane stuff,
• the entertainment  industry has no interest in serving customers, as evidenced by its keeping so much material unavailable,
• copyright is wrong, since information should be free and users should just pay what they think it’s worth, and
• (the illegitimate moral leap S and others make) therefore it’s “okay” to copy and share digital materials without permission.

Unfortunately, the lost generation’s beliefs, most of which have always been exaggerated or invalid, have been passed down to successor generations, a process accelerated rather than slowed by the current industry emphasis on monitoring and penalizing network users.

Why does the mythology persist?

There are the obvious technical and financial arguments: if illegal technology is more convenient that legal, and illegal content costs less than legal, then it’s not surprising that illegal stuff remains prominent.

But in addition, as the Captain might observe, what we have here is failure to communicate:

• There’s lots of evidence that convenient, comprehensive services like Netflix, Amazon Prime Instant Video, Hulu, Pandora, and Spotify draw users to them even when there are illegal “free” alternatives. But for this to happen, users must know about those services. S clearly didn’t—we asked her specifically—and that’s a marketing failure.
• Shoplifting and plagiarism are relatively rare, at least among individuals like S. Yet they have the same appealing features as “pirate” music and video. Somehow S and her peers have come to understand that shoplifting, plagiarism, and various similar choices are unethical, immoral, or socially counterproductive. Yet they don’t put copyright infringement in the same category. That’s a social, educational, and parental failure.
• For all kinds of arguably irremediable licensing, contractual, competitive, and anti-trust reasons, it remains stubbornly difficult to “give the lady what she wants“: in S’s case, a comprehensive, reasonably priced, convenient service from which she could obtain all the movies she wanted. Whether this is customers not conveying their wants to providers (in part because they can bypass the latter), or whether this is providers stuck on obsolete delivery models, it’s a business failure.
• Colleges and universities are supposed at least to tell their students about copyright infringement, and to implement technologies and other mechanisms to “effectively combat” it. S had no idea that the consequences of being caught downloading or streaming unauthorized copies were anything beyond being told to stop. So far as she knew, no one, at least no one at her college, had ever gotten in trouble for that. And she’d never heard anything from her college—which was also her Internet service provider—about the issue. That’s a policy failure.

To be fair, S’s dinner comments endorsed only a small subset of the lost generation’s tenets, she seemed generally interested in the streaming services we told her about, and she was now thinking about the consequences of being caught downloading or streaming unauthorized copies—and about how lots of people doing that might affect her future earnings. So there was progress.

But ganging up on 21-year-olds at dinner parties is a very inefficient way to counteract the mythology of the lost generation. We—and by this I mean everyone: users, parents, schools, artists, producers, network providers—need  to find much better ways to communicate about copyright infringement, to help potential infringers understand the choices they are making, and to provide and use better legal services.

Especially until we do that last, this will be hard, and progress will be slow. But it’s progress we need if the intellectual-property economy is to endure.

…as Oscar Wilde well might have titled an essay about campus-wide IT, had there been such a thing back then.

Enterprise IT it accounts for the lion’s share of campus IT staffing, expenditure, and risk. Yet it receives curiously little attention in national discussion of IT’s strategic higher-education role. Perhaps that should change. Two questions arise:

• What does “Enterprise” mean within higher-education IT?
• Why might the importance of Enterprise IT evolve?

What does “Enterprise IT” mean?

Here are some higher-education spending data from the federal Integrated Postsecondary Education Data Service (IPEDS), omitting hospitals, auxiliaries, and the like:

Broadly speaking, colleges and universities deploy resources with goals and purposes that relate to their substantive mission or the underlying instrumental infrastructure and administration.

• Substantive purposes and goals comprise some combination of education, research, and community service. These correspond to the bottom three categories in the IPEDS graph above. Few institutions focus predominantly on research—Rockefeller University, for example. Most research universities pursue all three missions, most community colleges emphasize the first and third, and most liberal-arts colleges focus on the first.

• Instrumental activities are those that equip, organize, and administer colleges and universities for optimal progress toward their mission—the top two categories in the IPEDS graph. In some cases, core activities advance institutional mission by providing a common infrastructure for the latter. In other cases, they do it by providing campus-wide or departmental staffing, management, and processes to expedite mission-oriented work. In still other cases, they do it through collaboration with other institutions or by contracting for outside services.

Education, research, and community service all use IT substantively to some extent. This includes technologies that directly or indirectly serve teaching and learning, technologies that directly enable research, and technologies that provide information and services to outside communities—for examples of all three, classroom technologies, learning management systems, technologies tailored to specific research data collection or analysis, research data repositories, library systems, and so forth.

Instrumental functions rely much more heavily on IT. Administrative processes rely increasingly on IT-based automation, standardization, and outsourcing. Mission-oriented IT applications share core infrastructure, services, and support. Core IT includes infrastructure such as networks and data centers, storage and computational clouds, and desktop and mobile devices; administrative systems ranging from financial, HR, student-record, and other back office systems to learning-management and library systems; and communications, messaging, collaboration, and social-media systems.

In a sense, then, there are six technology domains within college and university IT:

• the three substantive domains (education, research, and community service), and
• the three instrumental domains (infrastructure, administration, and communications).

Especially in the instrumental domains, “IT” includes not only technology, but also the services, support, and staffing associated with it. Each domain therefore has technology, service, support, and strategic components.

Based on this, here is a working definition: in in higher education,

“Enterprise” IT comprises the IT-related infrastructure, applications, services, and staff
whose primary institutional role is instrumental rather than substantive.

Exploring Enterprise IT, framed thus, entails focusing on technology, services, and support as they relate to campus IT infrastructure, administrative systems, and communications mechanisms, plus their strategic, management, and policy contexts.

Why Might the Importance of Enterprise IT Evolve?

Three reasons: magnitude, change, and overlap.

Magnitude

According data from EDUCAUSE’s Core Data Service (CDS) and the federal Integrated Postsecondary Data System (IPEDS), the typical college or university spends just shy of 5% of its operating budget on IT. This varies a bit across institutional types:

We lack good data breaking down IT expenditures further. However, we do have CDS data on how IT staff distribute across different IT functions. Here is a summary graph, combining education and research into “academic” (community service accounts for very little dedicated IT effort):

Thus my assertion above that Enterprise IT accounts for the lion’s share of IT staffing. Even if we omit the “Management” component, Enterprise IT comprises 60-70% of staffing including IT support, almost half without. The distribution is even more skewed for expenditure, since hardware, applications, services, and maintenance are disproportionately greater in Administration and Infrastructure.

Why, given the magnitude of Enterprise relative to other college and university IT, has it not been more prominent in strategic discussion? There are at least two explanations:

• relatively slow change in Enterprise IT, at least compared to other IT domains (rapidly-changing domains rightly receive more attention that stable ones), and
• overlap—if not competition—between higher-education and vendor initiatives in the Enterprise space.

Change

Enterprise IT is changing thematically, driven by mobility, cloud, and other fundamental changes in information technology. It also is changing specifically, as concrete challenges arise.

Consider, as one way to approach the former, these five thematic metamorphoses:

• In systems and applications, maintenance is giving way to renewal. At one time colleges and universities developed their own administrative systems, equipped their own data centers, and deployed their own networks. In-house development has given way to outside products and services installed and managed on campus, and more recently to the same products and services delivered in or from the cloud.
• In procurement and deployment, direct administration and operations are giving way to negotiation with outside providers and oversight of the resulting services. Whereas once IT staff needed to have intricate knowledge of how systems worked, today that can be less useful that effective negotiation, monitoring, and mediation.
• In data stewardship and archiving, segregated data and systems are giving way to integrated warehouses and tools. Historical data used to remain within administrative systems. The cost of keeping them “live” became too high, and so they moved to cheaper, less flexible, and even more compartmentalized media. The plunging price of storage and the emergence of sophisticated data warehouses and business-intelligence systems reversed this. Over time, storage-based barriers to data integration have gradually fallen.
• In management support, unidimensional reporting is giving way to multivariate analytics. Where once summary statistics emerged separately from different business domains, and drawing inferences about their interconnections required administrative experience and intuition, today connections can be made at the record level deep within integrated data warehouses. Speculating about relationships between trends is giving way to exploring the implications of documented correlations.
• In user support, authority is giving way to persuasion. Where once users had to accept institutional choices if they wanted IT support, today they choose their own devices, expect campus IT organizations to support them, and bypass central systems if support is not forthcoming. To maintain the security and integrity of core systems, IT staff can no longer simply require that users behave appropriately; rather, they must persuade users to do so. This means that IT staff increasingly become advocates rather than controllers. The required skillsets, processes, and administrative structures have been changing accordingly.

Beyond these broad thematic changes, a fourfold confluence is about to accelerate change in Enterprise IT: major systems approaching end-of-life, the growing importance of analytics, extensive mobility supported by third parties, and the availability of affordable, capable cloud-based infrastructure, services, and applications.

Systems Approaching End-of-Life

In the mid-1990s, many colleges and universities invested heavily in administrative-systems suites, often (if inaccurately) called “Enterprise Reporting and Planning” systems or “ERP.” Here, again drawing on CDS, are implementation data on Student, Finance, and HR/Payroll systems for non-specialized colleges and universities:

The pattern of implementation varies slightly across institution types. Here, for example, are implementation dates for Finance systems across four broad college and university groups:

Although these systems have generally been updated regularly since they were implemented, they are approaching the end of their functional life. That is, although they technically can operate into the future, the functionality of turn-of-the-century administrative systems likely falls short of what institutions currently require. Such functional obsolescence typically happens after about 20 years.

The general point holds across higher education: A great many administrative systems will reach their 20-year anniversaries over the next several years.

Moreover, many commercial administrative-systems providers end support for older products, even if those products have been maintained and updated. This typically happens as new products with different functionality and/or architecture establish themselves in the market.

These two milestones—functional obsolescence and loss of vendor support—mean that many institutions will be considering restructuring or replacement of their core administrative systems over the next few years. This, in turn, means that administrative-systems stability will give way to 1990s-style uncertainty and change.

Growing Importance of Analytics

Partly as a result of mid-1990s systems replacements, institutions have accumulated extensive historical data from their operations. They have complemented and integrated these by implementing flexible data-warehousing and business-intelligence systems.

Over the past decade, the increasing availability of sophisticated data-mining tools has given new purpose to data warehouses and business-intelligence systems that have until now have largely provided simple reports. This has laid foundation for the explosive growth of analytic management approaches (if, for the present, more rhetorical than real) in colleges and universities, and in the state and federal agencies that fund and/or regulate them.

As analytics become prominent in areas ranging from administrative planning to student feedback, administrative systems need to become better integrated across organizational units and data sources. The resulting datasets need to become much more widely accessible while complying with privacy requirements. Neither of these is easy to achieve. Achieving them together is more difficult still.

Mobility Supported by Third Parties

Until about five years ago campus communications—infrastructure and services both—were largely provided and controlled by institutions. This is no longer the case.

Much networking has moved from campus-provided wired and WiFi facilities to cellular and other connectivity provided by third parties, largely because those third parties also provide the mobile end-user devices students, faculty, and staff favor.

Separately, campus-provided email and collaboration systems have given way to “free” third-party email, productivity, and social-media services funded by advertising rather than institutional revenue. That mobile devices and their networking are largely outside campus control is triggering fundamental rethinking of instruction, assessment, identity, access, and security processes. This rethinking, in turn, is triggering re-engineering of core systems.

Affordable, Capable Cloud

Colleges and universities have long owned and managed IT themselves, based on two assumptions: that campus infrastructure needs are so idiosyncratic that they can only be satisfied internally, and that campuses are more sophisticated technologically than other organizations.

Both assumptions held well into the 1990s. That has changed. “Outside” technology has caught up to and surpassed campus technology, and campuses have gradually recognized and begun to avoid the costs of idiosyncrasy.

As a result, outside services ranging from commercially hosted applications to cloud infrastructure are rapidly supplanting campus-hosted services. This has profound implications for IT staffing—both levels and skillsets.

The upshot is that Enterprise, already the largest component of higher-education IT, is entering a period of dramatic change.

Beyond change in IT, the academy itself is evolving dramatically. For example, online enrollment is becoming increasingly common. As the Sloan Foundation reports, the fraction of students taking some or all of their coursework online is increasing steadily:

This has implications not only for pedagogy and learning environments, but also for the infrastructure and applications necessary to serve remote and mobile students.

Changes in the IT and academic enterprises are one reason Enterprise IT needs more attention. A second is the panoply of entities that try to influence Enterprise IT.

Overlap

One might expect colleges and universities to have relatively consistent requirements for administrative systems, and therefore that the market for those would consist largely of a few major widely-used products. The facts are otherwise. Here are data from the recent EDUCAUSE Center for Applied Research (ECAR) research report The 2011 Enterprise Application Market in Higher Education:

The closest we come to a compact market is for learning management systems, where 94% of installed systems come from the top 5 vendors. Even in this area, however, there are 24 vendors and open-source groups. At the other extreme is web content management, where 89 active companies and groups compete and the top providers account for just over a third of the market.

One way major vendors compete under circumstances like these is by seeking entrée into the informal networks through which institutions share information and experiences. They do this, in many cases, by inviting campus CIOs or administrative-systems heads to join advisory groups or participate in vendor-sponsored conferences.

That these groups are usually more about promoting product than seeking strategic or technical advice is clear. They are typically hosted and managed by corporate marketing groups, not technical groups. In some cases the advisory groups comprise only a few members, in some cases they are quite large, and in a few cases there are various advisory tiers. CIOs from large colleges and universities are often invited to various such groups. For the most part these groups have very little effect on vendor marketing, and even less on technical architecture and direction.

So why do CIOs attend corporate advisory board meetings? The value to CIOs, aside from getting to know marketing heads, is that these groups’ meetings provide a venue for engaging enterprise issues with peers. The problem is that the number of meetings and their oddly overlapping memberships lead to scattershot conversations inevitably colored by the hosts’ marketing goals and technical choices. It is neither efficient nor effective for higher education to let vendors control discussions of Enterprise IT.

Before corporate advisory bodies became so prevalent, there were groups within higher-education IT that focused on Enterprise IT and especially on administrative systems and network infrastructure. Starting with 1950s workshops on the use of punch cards in higher education, CUMREC hosted meetings and publications focused on the business use of information technology. CAUSE emerged from CUMREC in the late 1960s, and remained focused on administrative systems. EDUCOM came into existence in the mid-1960s, and its focus evolved to complement those of CAUSE and CUMREC by addressing joint procurement, networking, academic technologies, copyright, and in general taking a broad, inclusive approach to IT. Within EDUCOM, the Net@EDU initiative focused on networking much the way CUMREC focused on business systems.

As these various groups melded into a few larger entities, especially EDUCAUSE, Enterprise IT remained a focus, but it was only one of many. Especially as the y2k challenge prompted increased attention to administrative systems and intensive communications demands prompted major investments in networking, the prominence of Enterprise IT issues in collective work diffused further. Internet2 became the focal point for networking engagements, and corporate advisory groups became the focal point for administrative-systems engagements. More recently, entities such as Gartner, the Chronicle of Higher Education, and edu1world have tried to become influential in the Enterprise IT space.

The results of the overlap among vendor groups and associations, unfortunately, are scattershot attention and dissipated energy in the higher-education Enterprise IT space. Neither serves higher education well. Overlap thus joins accelerated change as a major argument for refocusing and reenergizing Enterprise IT.

The Importance of Enterprise IT

Enterprise IT, through its emphasis on core institutional activities, is central to the success of higher education. Yet the community’s work in the domain has yet to coalesce into an effective whole. Perhaps this is because we have been extremely respectful of divergent traditions, communities, and past achievements.

We must not be disrespectful, but it is time to change this: to focus explicitly on what Enterprise IT needs in order to continue advancing higher education, to recognize its strategic importance, and to restore its prominence.

9/25/12 gj-a  

Looking into the near-term future—say, between now and 2020—we in higher education have to address two big challenges, both involving IT. Neither admits easy progress. But if we don’t address them, we’ll find ourselves caught between a rock and a hard place.

• The first challenge, the rock, is to deliver high-quality, effective e-learning and curriculum at scale. We know how to do part of that, but key pieces are missing, and it’s not clear how will find them.
• The second challenge, the hard place, is to recognize that enterprise cloud services and personal devices will make campus-based IT operations the last rather than the first resort. This means everything about our IT base, from infrastructure through support, will be changing just as we need to rely on it.

“But wait,” I can hear my generation of IT leaders (and maybe the next) say, “aren’t we already meeting those challenges?”

If we compare today’s e-learning and enterprise IT with that of the recent past, those leaders might rightly suggest, immense change is evident:

• Learning management systems, electronic reserves, video jukeboxes, collaboration environments, streamed and recorded video lectures, online tutors—none were common even in 2000, and they’re commonplace today.
• Commercial administrative systems, virtualized servers, corporate-style email, web front ends—ditto.

That’s progress and achievement we all recognize, applaud, and celebrate. But that progress and achievement overcame past challenges. We can’t rest on our laurels.

We’re not yet meeting the two broad future challenges, I believe, because in each case fundamental and hard-to-predict change lies ahead. The progress we’ve made so far, however progressive and effective, won’t steer us between the rock of e-learning and the hard place of enterprise IT.

The fundamental change that lies ahead for e-learning
is the the transition from campus-based to distance education

Back in the 1990s, Cliff Adelman, then at the US Department of Education, did a pioneering study of student “swirl,” that is, students moving through several institutions, perhaps with work intervals along the way,before earning degrees.

“The proportion of undergraduate students attending more than one institution,” he wrote, “swelled from 40 percent to 54 percent … during the 1970s and 1980s, with even more dramatic increases in the proportion of students attending more than two institutions.” Adelman predicted that “…we will easily surpass a 60 percent multi-institutional attendance rate by the year 2000.”

Moving from campus to campus for classes is one step; taking classes at home is the next. And so distance education, long constrained by the slow pace and awkward pedagogy of correspondence courses, has come into its own. At first it was relegated to “nontraditional” or “experimental” institutions—Empire State College, Western Governors University, UNext/Cardean (a cautionary tale for another day), Kaplan. Then it went mainstream.

At first this didn’t work: fathom.com, for example, a collaboration among several first-tier research universities led by Columbia, found no market for its high-quality online offerings. (Its Executive Director has just written a thoughtful essay on MOOCs, drawing on her fathom.com experience.)

Today, though, a great many traditional colleges and universities successfully bring instruction and degree programs to distant students. Within the recent past these traditional institutions have expanded into non-degree efforts like OpenCourseWare and to broadcast efforts like the MOOC-based Coursera and edX. In 2008, 3.7% of students took all their coursework through distance education, and 20.4% took at least one class that way.

Learning management systems, electronic reserves, video jukeboxes, collaboration environments, streamed and recorded video lectures, online tutors, the innovations that helped us overcome past challenges—little of that progress was designed for swirling students who do not set foot on campus.

We know how to deliver effective instruction to motivated students at a distance. Among policy issues we have yet to resolve, we don’t yet know how to

• confirm their identity,
• assess their readiness,
• guide their progress,
• measure their achievement,
• standardize course content,
• construct and validate curriculum across diverse campuses, or
• certify degree attainment

in this imminent world. Those aren’t just IT problems, of course. But solving them will almost certainly challenge IT.

The fundamental change that lies ahead for enterprise technologies
is the transition from campus IT to cloud and personal IT

The locus of control over all three principal elements of campus IT—servers and services, networks, and end-user devices and applications—is shifting rapidly from the institution to customers and third parties.

As recently as ten years ago, most campus IT services, everything from administrative systems through messaging and telephone systems to research technologies, were provided by campus entities using campus-based facilities, sometimes centralized and sometimes not. The same was true for the wired and then wireless networks that provided access to services, and for the desktop and laptop computers faculty, students, and staff used.

Today shared services are migrating rapidly to servers and systems that reside physically and organizationally elsewhere—the “cloud”—and the same is happening for dedicated services such as research computing. It’s also happening for networks, as carrier-provided cellular technologies compete with campus-provided wired and WiFi networking, and for end-user devices, as highly mobile personal tablets and phones supplant desktop and laptop computers.

As I wrote in an earlier post about “Enterprise IT,” the scale of enterprise infrastructure and services within IT and the shift in their locus of control have major implications for and the organizations that have provided it. Campus IT organizations grew up around locally-designed services running on campus-owned equipment managed by internal staff. Organization, staffing, and even funding models ensued accordingly. Even in academic computing and user support, “heavy metal” experience was valued highly. The shifting locus of control makes other skills at least as valuable: the ability to negotiate with suppliers, to engage effectively with customers (indeed, to think of them as “customers” rather than “users”), to manage spending and investments under constraint, to explain.

To be sure, IT organizations still require highly skilled technical staff, for example to fine-tune high-performance computing and networking, to ensure that information is kept secure, to integrate systems efficiently, and to identify and authenticate individuals remotely. But these technologies differ greatly from traditional heavy metal, and so must enterprise IT.

The rock, IT, and the hard place

In the long run, it seems to me that the campus IT organization must evolve rapidly to center on seven core activities.

Two of those are substantive:

• making sure that researchers have the technologies they need, and
• making sure that teaching and learning benefit from the best thinking about IT applications and effectiveness.

Four others are more general:

• negotiating and overseeing relationships with outside providers;
• specifying or doing what is necessary for robust integration among outside and internal services;
• striking the right personal/institutional balance between security and privacy for networks, systems, and data; and last but not least
• providing support to customers (both individuals and partner entities).

The seventh core activity, which should diminish over time, is

• operating and supporting legacy systems.

Creative, energetic, competent staff are sine qua non for achieving that kind of forward-looking organization. It’s very hard to do good IT without good, dedicated people, and those are increasingly difficult to find and keep. Not least, this is because colleges and universities compete poorly with the stock options, pay, glitz, and technology the private sector can offer. Therein lies another challenge: promoting loyalty and high morale among staff who know they could be making more elsewhere.

To the extent the rock of e-learning and the hard place of enterprise IT frame our future, we not only need to rethink our organizations and what they do; we also need to rethink how we prepare, promote, and choose leaders for higher-education leaders on campus and elsewhere—the topic, fortuitously, of a recent ECAR report, and of widespread rethinking within EDUCAUSE.

We’ve been through this before, and risen to the challenge.

• Starting around 1980, minicomputers and then personal computers brought IT out of the data center and into every corner of higher education, changing data center, IT organization, and campus in ways we could not even imagine.
• Then in the 1990s campus, regional, and national networks connected everything, with similarly widespread consequences.

We can rise to the challenges again, too, but only if we understand their timing and the transformative implications.

Old joke. Someone writes a computer program (creates an app?) that translates from English into Russian (say) and vice versa. Works fine on simple stuff, so the next test is a a bit harder: “the spirit is willing, but the flesh is weak.”  The program/app translates the phrase into Russian, then the tester takes the result, feeds it back into the program/app, and translates it back into English. Result: “The ghost is ready, but the meat is raw.”

(The starting phrase is from Matthew 26:41 — the King James version has “indeed” before “willing”, ASV doesn’t, and weirdly enough, if you try this in Google Translate, the joke falls flat, because you get an accurate translation to Russian and back, except for some reason you end up with an extra “indeed” in the final version. It’s almost as though Google Translate has figured out where the quotation came from, and then substituted the King James version for the ASV one, but not quite correctly. Spooky. But I digress.)

Old joke, yes. Tired, even. But, as usual, it’s a metaphor, in this case for a problem that will only become larger as higher education outsources or contracts for ever more of its activity: we think we’ve doing the right thing when we contract with outside providers, but the actual effect of the contract, once it takes effect, isn’t quite what we expected. If we’re lucky, we figure this out before we’re irrevocably committed. If we’re unlucky, we box ourselves in.

Two examples.

1. Microsoft Site Licensing

About a decade ago, several of us were at an Internet2 meeting. A senior Microsoft manager spoke about relations with higher education (although looking back, I can’t see why Microsoft would present at I2. Maybe it wasn’t an I2 meeting, but let’s just say it was — never let truth get in the way of a good story). At the time, instead of buying a copy of Office for each computer, as Microsoft licenses required, many students, staff, and faculty simply installed Microsoft Office on multiple machines from one purchased copy — or even copied the installation disks and passed them around. That may save money, but it’s copyright infringement, and illegal.

Microsoft’s response to this problem had been threefold:

• it began incorporating copy protection and other digital-rights-management (DRM) mechanisms into its installation media so that they couldn’t be copied,
• it began berating campuses for tolerating the illegal copying (and in some cases attempted to audit compliance with licenses by searching campus computers for illegally obtained software), and
• it sought to centralize campus procurement of Microsoft software by tailoring and refining its so-called “Select” volume-discount program to encourage campuses to license software campus-wide.

Problem was, the “Select” agreement required campuses to count how many copies of software they licensed, and to maintain records that would enable Microsoft to determine whether each installed copy on campus was properly licensed. This entailed elaborate bookkeeping and tracking mechanisms, exposed campuses to audit risk, and its costs into the future were unpredictable. The volume-discount “Select” program was clearly a step forward, but it fell far short of actually appealing to campuses.

So the several of us in the Internet2 session (or wherever it was) took the Microsoft manager aside afterwards, told him Microsoft needed a more attractive licensing model for campuses, and suggested what that might be.

To our surprise, Microsoft followed up, and the rump-group discussions evolved into the initial version of the Microsoft Campus Agreement. The Campus Agreement (since replaced by Enrollment for Education Solutions, EES) was a true site license: glossing over some complexities and details, its general terms were that campuses would pay Microsoft based on their size and the number of different products they wished to license, and in return would be permitted to use as many copies of those products as they liked.

Most important from the campus perspective, the Campus Agreement included no requirement to track or count individual copies of the licensed products, thereby making all copies legal; in fact, campuses could make their own copies of installation media. Most important from the Microsoft perspective, Campus Agreement pricing was set so that the typical campus would still pay Microsoft about as much as Microsoft had been receiving from that campus’s central or departmental users for Select or individual copies; that is, Micorsoft’s revenue from campuses would not decline.

The Campus Agreement did entail a fundamental change that was less appealing. In effect, campuses were paying to rent software, with Microsoft agreeing to provide updates at no additional cost, rather than campuses buying copies and then periodically paying to update them. Although it included a few other lines, for the most part the Campus Agreement covered Microsoft’s operating-system and Office products.

Win-win, right? Lots of campuses signed up for the Campus Agreement. It largely eliminated talk about “piracy” of MS-Office products in higher education (enhanced DRM played an important role in this too), and it stabilized costs for most Microsoft client software. It was very popular with students, faculty, and staff, especially since the Campus Agreement allowed institutionally-provided software to be installed on home computers.

But at least one campus, which I’ll call Pi University, balked. The Campus Agreement, PiU’s golf-loving CIO pointed out, had a provision no one had read carefully: if PiU withdrew from the Campus Agreement, he said, it might be required to list and pay for all the software copies that PiU or its students, faculty, and staff had acquired under the Campus Agreement — that is, to buy what it had been renting. The PiU CIO said that he had no way to comply with such a provision, and that therefore PiU could not in good faith sign an agreement that included it.

Some of us thought the PiU CIO’s point was valid but inconsequential. First, some of us didn’t believe that Microsoft would ever enforce the buy-what-you’d-rented clause, so that it presented little actual risk. Second, some of us pointed out that since there was no requirement that campuses document how many copies they distributed, and in general the distribution would be independent of Microsoft, a campus leaving the Campus Agreement could simply cite any arbitrary number of copies as the basis for its exit payment. Therefore, even if Microsoft enforced the clause, estimating the associated payment was entirely under the campus’s control. Those of who believed these arguments went forward with the Campus Agreement; Pi University didn’t.

So the ghost was ready (higher education had gotten most of what it wanted), but the meat was raw (what we wanted turned out problematic in ways no one had really thought through).

Now let’s turn to a more current case.

2. Outsourcing Campus Bookstores

In February 2012 EDUCAUSE agreed to work with Internet2 on an electronic textbooks pilot. This was to be the third in a series of pilots: Indiana University had undertaken one for the fall of 2011, it and a few other campuses had worked with Internet2 on second proof-of-concept pilot for the spring of 2012, and the third pilot was to include a broader array of  institutions.

Driving these efforts were the observations that textbook prices figured prominently in spiraling out-of-pocket college-attendance costs, that electronic textbooks might help attenuate those prices, and that electronic textbooks also might enable campuses to move from individual student purchases to more efficient site licenses, perhaps bypassing unnecessary intermediaries.

A small team planned the pilot, and began soliciting participation in mid-March. By April 7, the initial deadline, 70 institutions had expressed interest. Over 100 people joined an informational webinar two days later, and it looks as though about 25 institutions will manage to participate and help higher education, publishers, and e-reader providers understand their joint future better.

The ghost/meat example here isn’t the etext pilot itself. Rather, it’s something that caused many interested institutions to withdraw from the pilot: campus bookstore outsourcing.

According to the National Association of College Stores (NACS), there are about 4500 bookstores serving US higher education (probably coincidentally, that’s about the number of degree-granting institutions in the US, of which about two thirds are nonspecialized institutions enrolling more than just a few students). Many stores counted by NACS are simply stores near campuses rather than located on or formally associated with them.

Of the campus-located, campus-associated stores, over 820 are operated under outsourcing contracts by Follett Higher Education Group and about 600 are operated by Barnes & Noble College Booksellers. Another 140 stores are members of the Independent College Bookstore Association (ICBA), and the remainder — I can’t find a good count — are either independent, campus-operated, or operated by some other entity.

The arrangements for outsourced bookstores vary from campus to campus, but they have some features in common. The most prominent of those is the overall deal, which is generally that in return for some degree of exclusivity or special access granted by the campus, the store pays the campus a fee of some kind. The exclusivity or special access may be confined to textbook adoptions, or it may extend to clothing and other items with the campus logo or to computer hardware and software. The payment to the campus may be negotiated explicitly, or it may be a percentage of sales or profit. Some outsourced stores are in campus-owned buildings and pay rent, some own a building part of which is rented to campus offices or activities, and some are freestanding; the associated space payments further complicate the relationship between outsourced stores and campuses but do not change its fundamental dependence on the exchange of exclusivity for fees.

For the most part outsourcing bookstores seems to serve campuses well. Managing orders, inventories, sales, and returns for textbooks and insignia items requires skill and experience with high-volume, low-margin retail, which campus administrators rarely have. Moreover, until recently bookstore operations generally had little impact on campus operations and vice versa.

Because bookstore operations generally stood apart from academic and programmatic activities on campus, negotiating contracts with bookstores generally emphasized “business” issues. Since these for the most part involved money and space, negotiations and contract approvals often remained on the “business” side of campus administration, along with apparently similar issues like dining halls, fleet maintenance, janitorial service, lab supply, and so forth. Again, this served campuses well: the campus administrators most attuned to operations and finance (chief finance officers, chief administrative officers, heads of auxiliary services) were the right ones to address bookstore issues.

Over the past few years this changed, first gradually and then more abruptly.

• First, having bookstores handle hardware and software sales to students (and in some cases departments) came into conflict with campus desires to guide individual choices and maximize support efficiency through standardization and incentives, none of which aligned well with bookstores’ need to maximize profit from IT sales — an important goal, with campus bookstore sales essentially flat since 2005-2006 despite 10%+ enrollment growth.
• Second, the high price of textbooks drew attention as a major component of growing college costs, and campuses sought to regain some control over it — NACS reports that the average student spends $483 on texts and related materials, that the average textbook price rose from $56 in 2006-2007 to $62 in 2009-2010, and that the typical margin on textbooks is about 22% for new texts and 35% for used ones.
• Third, as textbooks have begun to migrate from static paper volumes to interactive electronic form, they have come to resemble software more than sweatshirts in that individual student purchases through bookstores may not be the optimal way to distribute or procure them.

That last point — that bookstores may not be the right medium for selling and buying textbooks — potentially threatens the traditional bookstore model, and therefore the outsourcing industry based on it. Not surprisingly, bookstores have responded aggressively to this threat, both offensively and defensively. On the offensive front (I mean this in the sense “trying to advance”, rather than “trying to offend”), the major bookstore chains have invested in e-reader technology, and have begun experimenting extensively with alternative pricing and delivery models. On the defensive front, they have tried to extend past exclusivity clauses to include electronic texts and other new materials.

Many campuses expressed interest in the EDUCAUSE/Internet2 EText Pilot, going so far as to add themselves to a list, make preliminary commitments, and attend the webinar. Filled with enthusiasm, many webinar attendees began talking up the pilot on their campuses, and many of them then ran into a wall: they learned, often only when they double-checked with their counsel in the final stages of applying, that their bookstore contracts — Barnes & Noble and Follett both — precluded their participation in even a pilot exploration of alternative etext approaches, since the right to distribute electronic textbooks was reserved exclusively for the outsourced bookstore.

The CIO from one campus — I’ll call it Omega University — discovered that a recent renewal of the bookstore contract provided that during the 15-year term of the contract, “the Bookstore shall be the University’s  …exclusive seller of all required, recommended or suggested course materials, course packs and tools, as well as materials published or distributed electronically, or sold over the Internet.”  The OmegaU CIO was outraged: “In my mind,” he wrote, “the terms exclusive and over the Internet can’t even be in the same sentence!  And to restrict faculty use of technology for next 15 years is just insane.”

If the last decade has taught us anything, it is that the evolutionary cycle for electronic products is very short, requiring near-constant reappraisal of business models, pricing, and partnerships. That someone on campus signed a contract fixing electronic distribution mechanisms for 15 years may be an extreme case, but we’ve learned even from less pernicious cases  that exclusivity arrangements bound to old business models will drastically constrain progress.

And so the ghost’s readiness again yielded raw meat: technological progress translated well-intentioned, longstanding bookstore contracts that had served campuses well into obstacles impeding even the consideration of important changes.

3. So What Do We Do?

It’s important to draw the right inference from all this.

The problem isn’t simply Microsoft trying to lock customers into the Campus Agreement or bookstore operators being avaricious; rather, they’re acting in self-interest, albeit self-interest that in each case is a bit short-sighted.

The compounding problem is that we in higher education often make decisions too narrowly. In the case of the Campus Agreement, we were so focused on the important move from per-copy to site licensing, a major win, that we didn’t pay sufficient negotiating time or effort to the so-called exit clauses — which, in retrospect, could certainly have been written in much less problematic ways still acceptable to Microsoft. In the case of bookstore contracts, we failed to recognize that what had been a distinct, narrow set of activities readily handled within business and finance was being driven by technology into new domains requiring foresight and expertise generally found elsewhere on campus.

Sadly, there’s no simple solution to this problem. It’s hard to take everything into account or involve every possible constituency in a decision and still get it done, and decisions must get done. Perhaps the best solution we can hope for is better, more transparent discussion of both past decisions and future opportunities, so that we learn collectively and openly from our mistakes, take joint responsibility for our shared technological future, and translate accurately back and forth between what we want and what we get.

It’s been an interesting few weeks:

• Facebook’s upcoming $100-billion IPO has users wondering why owners get all the money while users provide all the assets.
• Google’s revision of privacy policies has users thinking that something important has changed even though they don’t know what.
• Google has used a loophole in Apple’s browser to gather data about iPhone users.
• Apple has allowed app developers to download users’ address books.
• And over in one of EDUCAUSE’s online discussion groups, the offer of a free book has somehow led security officers to do linguistic analysis of the word “free” as part of a privacy argument.

Lurking under all, I think, are the unheralded and misunderstood resurgence of a sometimes triangular barter economy, confusion about different revenue models, and, yes, disagreement what the word “free” means.

Let’s approach the issue obliquely, starting, in the best academic tradition, with a small-scale research problem. Here’s the hypothetical question, which I might well have asked back when I was a scholar of student choice: Is there a relationship between selectivity and degree completion at 4-year colleges and universities?

As a faculty member in the late 1970s, I’d have gone to the library and used reference tools to locate articles or reports on the subject. If I were unaffiliated and living in Chicago (which I wasn’t back then), I might have gone to the Chicago Public Library, found in its catalog a 2004 report by Laura Horn, and have had that publication pulled from closed-stack storage so I could read it.

By starting with that baseline, of course, I’m merely reminiscing. These days I can obtain the data myself, and do some quick analysis. I know the relevant data are in the Integrated Postsecondary Education Data System (IPEDS). And those IPEDS data are available online, so I can

(a) download data on 2010 selectivity, undergraduate enrollment, and bachelor’s degrees awarded for the 2,971 US institutions that grant four-year degree and import those data into Excel,

(b) eliminate the 101 system offices and such missing relevant data, the 1,194 that granted fewer than 100 degrees, the 15 institutions reporting suspiciously high degree/enrollment rates, the one that reported no degrees awarded (Miami-Dade College, in case you’re interested), and the 220 that reported no admit rate, and then

(c) for the remaining 1,440 colleges and universities, create a graph of degree completion (somewhat normalized) as a function of selectivity (ditto).

The graph doesn’t tell me much–scatter plots rarely do for large datasets–but a quick regression analysis tells me there’s a modestly positive relationship: 1% higher selectivity (according to my constructed index) translates on average into 1.4% greater completion (ditto). The download, data cleaning, graphing, and analysis take me about 45 minutes all told.

Or I might just use a search engine. When I do that, using “degree completion by selectivity” as the search term, a highly-ranked Google result takes me to an excerpt from a College Board report.

Curiously, that report tells me that “…selectivity is highly correlated with graduation rates,” which is a rather different conclusion than IPEDS gave me. The footnotes help explain this: the College Board includes two-year institutions in its analysis, considers only full-time, first-time students, excludes returning students and transfers, and otherwise chooses its data in ways I didn’t.

The difference between my graph and the College Board’s conclusion is excellent fodder for a discussion of how to evaluate what one finds online — in the quote often (but perhaps mistakenly) attributed to Daniel Patrick Moynihan, “Everyone is entitled to his own opinion, but not his own facts.” Which gets me thinking about one of the high points in my graduate studies, a Harvard methodology seminar wherein Mike Smith, who was eventually to become US Undersecretary of Education, taught Moynihan what regression analysis is, which in turn reminds me of the closet full of Scotch at the Joint Center for Urban Studies kept full because Moynihan required that no meeting at the Joint go past 4pm without a bottle of Scotch on the table. But I digress.

Since I was logged in with my Google account when I did the search, some of the results might even have been tailored to what Google had learned about me from previous searches. At the very least, the information was tailored to previous searches from the computer I used here in my DC office.

Which brings me to the linguistic dispute among security officers.

A recent EDUCAUSE webinar presenter, during Data Privacy Month, was Matt Ivester, creator of JuicyCampus and author of lol…OMG!: What Every Student Needs to Know About Online Reputation Management, Digital Citizenship and Cyberbullying.

“In honor of Data Privacy Day,” the book’s website announced around the same time, “the full ebook of lol…OMG! (regularly $9.99) is being made available for FREE!” Since Ivester was going to be a guest presenter for EDUCAUSE, we encouraged webinar participants to avail themselves of this offer and to download the book.

One place we did that was in a discussion group we host for IT security professionals. A participant in that discussion group immediately took Ivester to task:

…you can’t download the free book without logging in to Amazon. And, near as I can tell, it’s Kindle- or Kindle-apps-only. In honor of Data Privacy Day. The irony, it drips.

“Pardon the rant,” another participant responded, “but what is the irony here?” Another elaborated:

I intend to download the book but, despite the fact that I can understand why free distribution is being done this way, I still find it ironic that I must disclose information in order to get something that’s being made available at no charge in honor of DPD.

The discussion grew lively, and eventually devolved into a discussion of the word “free”. If one must disclose personal information in order to download a book at no monetary cost, is the book “free”?

If words like “free”, “cost”, and “price” refer only to money, the answer is Yes. But money came into existence only to simplify barter economies. In a sense, today’s Internet economy involves a new form of barter that replaces money: If we disclose information about ourselves, then we receive something in return; conversely, vendors offer “free” products in order to obtain information about us.

In a recent post, Ed Bott presented graphs illustrating the different business models behind Microsoft, Apple, and Google. According to Bott, Microsoft is selling software, Apple is selling hardware, and Google is selling advertising.

More to the point here, Microsoft and Apple still focus on traditional binary transactions, confined to themselves and buyers of their products.

Google is different. Google’s triangle trade (which Facebook also follows) offers “free” services to individuals, collects information about those individuals in return, and then uses that information to tailor advertising that it then sells to vendors in return for money. In the triangle, the user of search results pays no money to Google, so in that limited sense it’s “free”. Thus the objection in the Security discussion group: if one directly exchanges something of value for the “free” information, then it’s not free.

Except for my own time, all three answers to my “How does selectivity relate to degree completion?” question were “free”, in the sense I paid no money explicitly for them. All of them cost someone something. But not all no-cost-to-the-user online data is funded through Google-like triangles.

In the case of the Chicago Public Library, my Chicago property taxes plus probably some federal and Illinois grants enabled the library to acquire, catalog, store, and retrieve the Horn report. They also built the spectacular Harold Washington Library where I’d go read it.

In the case of IPEDS, my federal tax dollars paid the bill.

In both cases, however, what I paid was unrelated to how much I used the resources, and involved almost no disclosure of my identity or other attributes.

In contrast, the “free” search Google provided involved my giving something of value to Google, namely something about my searches. The same was true for the Ivester fans who downloaded his “free” book from Amazon.

Not that there’s anything wrong with that, as Jerry Seinfeld might say: by allowing Google and Amazon to tailor what they show me based on what they know about me, I get search results or purchase suggestions that are more likely to interest me. That is, not only does Google get value from my disclosure; I also get value from what Google does with that information.

The problem–this is what takes us back to security–is twofold.

• First, an awful lot of users don’t understand how the disclosure-for-focus exchange works, in large part because the other party to the exchange isn’t terribly forthright about it. Sure, I can learn why Google is displaying those particular ads (that’s the “Why these ads?” link in tiny print atop the right column in search results), and if I do that I discover that I can tailor what information Google uses. But unless I make that effort the exchange happens automatically, and each search gets added to what Google will use to customize my future ads.
• Second, and much more problematic, the entities that collect information about us increasingly share what they know. This varies depending whether they’ve learned about us directly through things like credit applications or indirectly through what we search for on the Web, what we purchase from vendors like Amazon, or what we share using social media like Facebook or Twitter. Some companies take pains to assure us they don’t share what they know, but in many cases initial assurances get softened over time (or, as appears to have happened with Apple, are violated through technical or process failures). This is routinely true for Facebook, and many seem to believe it’s what’s behind the recent changes in Google’s privacy policy.

Indeed, companies like Acxiom are in the business of aggregating data about individuals and making them available. Data so collected can help banks combat identity theft by enabling them to test whether credit applicants are who they claim to be. If they fall into the wrong hands, however, the same data can enable subtle forms of redlining or even promote identity theft.

Vendors collecting data about us becomes a privacy issue whose substance depends on whether

• we know what’s going on,
• data are kept and/or shared, and
• we can opt out.

Once we agree to disclose in return for “free” goods, however, the exchange becomes a security issue, because the same data can enable impersonation. It becomes a policy issue because the same data can enable inappropriate or illegal activity.

The solution to all this isn’t turning back the clock — the new barter economy is here to stay. What we need are transparency, options, and broad-based educational campaigns to help people understand the deal and choose according to their preferences.

As either Stan Delaplane or Calvin Trillin once observed about “market price” listings on restaurant menus (or didn’t — I’m damned if I can find anything authoritative, or for that matter any mention whatsoever of this, but  know I read it), “When you learn for the first time that the lobster you just ate cost $50, the only reasonable response is to offer half”.

Unfortunately, in today’s barter economy we pay the price before we get the lobster…

Note to prospective readers: This post has evolved, through extensive revision and expansion and more careful citation, into a paper available at http://gjackson.us/it-he.pdf.

You might want to read that paper, which is much better and complete, instead of this post — unless you like the pictures here, which for the moment aren’t in the paper. Even if you read this to see the pictures, please go read the other.

“Which way to Millinocket?,” a traveler asks. “Well, you can go west to the next intersection…” the drawling down-east Mainer replies in the Dodge and Bryan story,

“…get onto the turnpike, go north through the toll gate at Augusta, ’til you come to that intersection…. well, no. You keep right on this tar road; it changes to dirt now and again. Just keep the river on your left. You’ll come to a crossroads and… let me see. Then again, you can take that scenic coastal route that the tourists use. And after you get to Bucksport… well, let me see now. Millinocket. Come to think of it, you can’t get there from here.”

PLATO and its programmed-instruction kin were supposed to transform higher education. So were the Apple II, and then the personal computer – PC and then Mac – and then the “3M” workstation (megapixel display, megabyte memory, megaflop speed) for which Project Athena was designed. So were simulated laboratories, so were BITNET and then the Internet, so were MUDs, so was Internet2, so was artificial intelligence, so was supercomputing.

Each of these most certainly has helped higher education grow, evolve, and gain efficiency and flexibility. But at its core, higher education remains very much unchanged. That may no longer suffice.

What about today’s technological changes and initiatives – social media, streaming video, multi-user virtual environments, mobile devices, the cloud? Are they to be evolutionary, or transformational? If higher education needs the latter, can we get there from here?

It’s important to start conversations about questions like these from a common understanding of information technologies that currently play a role in higher education, what that role is, and how technologies and their roles are progressing. That’s what prompted these musings.

Information Technology

For the most part, “information technology” means a tripartite array of hardware and software:

• end-user devices, which today range from large desktop workstations to small mobile phones, typically with some kind of display, some way to make choices and enter text, and various other capabilities variously enabled by hardware and software;
• servers, which comprise not just racks of processors, storage, and other hardware but rather are aggregations of hardware, software, applications, and data that provide services to multiple users (when the aggregation is elsewhere, it’s often called “the cloud” today); and
• networks, wireless or wired, which interlink local servers, remote server clouds, and end-user devices, and which typically comprise copper and glass cabling, routers and switches and optronics, and network operating system plus some authentication and logging capability.

Information technology tends to progress rapidly but unevenly, with progress or shortcomings in one domain driving or retarding progress in others.

Today, for example, the rapidly growing capability of small smartphones has taxed previously underused cellular networks. Earlier, excess capability in the wired Internet prompted innovation in major services like Google and YouTube. The success of Google and Amazon forced innovation in the design, management, and physical location of servers.

Perhaps the most striking aspects of technological progress have been its convergence and integration. Whereas once one could reasonably think separately about servers, networks, and end-user devices, today the three are not only tightly interconnected and interdependent, but increasingly their components are indistinguishable. Network switches are essentially servers, servers often comprise vast arrays of the same processors that drive end-user devices plus internal networks, and end-user devices readily tackle tasks – voice recognition, for example – that once required massive servers.

Access to Information Technology

Progress, convergence, and integration in information technology have driven dramatic and fundamental change in the information technologies faculty, students, colleges, and universities have. That progress is likely to continue.

Here, as a result, are some assumptions we can reasonably make today:

• Households have some level of broadband access to the Internet, and at least one computer capable of using that broadband access to view and interact with Web pages, handle email and other messaging, listen to audio, and view videos of at least YouTube quality .
• Teenagers and most adults have some kind of mobile phone, and that phone usually has the capability to handle routine Internet tasks like viewing Web pages and reading email.
• Colleges and universities have building and campus networks operating at broadband speeds of at least 10Mb/sec, and most have wireless networks operating at 802.11b (11Mb/sec) or greater speed.
• Server capacity has become quite inexpensive, largely because “cloud” providers have figured out how to gain and then sell economy of scale.
• Everyone – or at least everyone between the ages of, say, 12 and 65 – has at least one authenticated online identity, including email and other online service accounts; Facebook, Twitter, Google, or other social-media accounts; online banking, financial, or credit-card access; or network credentials from a school, college or university, or employer.
• Everyone knows how to search on the Internet for material using Google, Bing, or other search engines.
• Most people have a digital camera, perhaps integrated into their phone and capable of both still photos and videos, and they know how to send them to others or offload their photos onto their computers or an online service.
• Most college and university course materials are in electronic form, and so is a large fraction of library and reference material used by the typical student.
• Most colleges and universities have readily available facilities for creating video from lectures and similarly didactic events, whether in classrooms or in other venues, and for streaming or otherwise making that video available online.

It’s striking how many of these assumptions were invalid even as recently as five years ago. Most of the assumptions were invalid a decade before that (and it’s sobering to remember that the “3M” workstation was a lofty goal as recently as 1980 and cost nearly $10,000 in the mid-1980s, yet today’s iPhone almost exceeds the 3M spec).

Looking a bit into the future, here are some further assumptions that probably will be safe:

• Typical home networking and computers will have improved to the point they can handle streamed video and simple two-way video interactions (which means that at least one home computer will have an add-on or built-in camera).
• Most people will know how to communicate with individuals or small groups online through synchronous social media or messaging environments, in many cases involving video.
• Authentication and monitoring technologies will exist to enable colleges and universities to reasonably ensure that their testing and assessment of student progress is protected from fraud.
• Pretty much everyone will have the devices and accounts necessary for ubiquitous connectivity with anybody else and to use services from almost any college, university, or other educational provider.

Technology, Teaching, and Learning

In colleges and universities, as in other organizations, information technology can promote progress by enabling administrative processes to become more efficient and by creating diverse, flexible pathways for communication and collaboration within and across different entities. That’s organizational technology, and although it’s very important, it affects higher education much the way it affects other organizations of comparable size.

Somewhat more distinctively, information technology can become learning technology, an integral part of the teaching and learning process. Learning technology sometimes replaces traditional pedagogies and learning environments, but more often it enhances and expands them.

The basic technology and middleware infrastructure necessary to enable colleges and universities to reach, teach, and assess students appears to exist already, or will before long. This brings us to the next question: What applications turn information technology into learning technology?

To answer this, it’s useful to think about four overlapping functions of learning technology.

Amplify and Extend Traditional Pedagogies, Mechanisms, and Resources

For example, by storing and distributing materials electronically, by enabling lectures and other events to be streamed or recorded, and by providing a medium for one-to-one or collective interactions among faculty and students, IT potentially expedites and extends traditional roles and transactions. Similarly, search engines and network-accessible library and reference materials vastly increase faculty and students access. The effect, although profound, nevertheless falls short of transformational. Chairs outside faculty doors give way to “learning management systems” like Blackboard or Sakai or Moodle, wearing one’s PJs to 8am lectures gives way to watching lectures from one’s room over breakfast, and library schools become information-science schools. But the enterprise remains recognizable. Even when these mechanisms go a step further, enabling true distance education whereby students never set foot on campus (in 2011, 3.7% of all students took all their coursework through distance education), the resulting services remain recognizable. Indeed, they are often simply extensions of existing institutions’ campus programs.

Make Educational Events and Materials Available Outside the Original Context

For example, the Open Courseware initiative (OCW) started as publicly accessible repository of lecture notes, problem sets, and other material from MIT classes. It since has grown to include similar material from scores of other institutions worldwide. Similarly, the newer Khan Academy has collected a broad array of instructional videos on diverse topics, some from classes and some prepared especially for Khan, and made those available for anyone interested in learning the material. OCW, Khan, and initiatives like them provide instructional material in pure form, rather than as part of curricula or degree programs.

Enable Experience-Based Learning 

This most productively involves experience that otherwise might have been unaffordable, dangerous, or otherwise infeasible. Simulated chemistry laboratories and factories were an early example – students could learn to synthesize acetylene by trial and error without blowing up the laboratory, or to fine-tune just-in-time production processes without bankrupting real manufacturers. As computers have become more powerful, so have simulations become more complex and realistic. As simulations have moved to cloud-based servers, multi-user virtual environments have emerged, which go beyond simulation to replicate complex environments. Experiences like these were impossible to provide before the advent of powerful, inexpensive server clouds, ubiquitous networking, and graphically capable end-user devices.

Replace the Didactic Classroom Experience

This is the most controversial application of learning technology – “Why do we need faculty to teach calculus on thousands of different campuses, when it can be taught online by a computer?” – but also one that drives most discussion of how technology might transform higher education. It has emerged especially for disciplines and topics where instructors convey what they know to students through classroom lectures, readings, and tutorials. PLATO (Programmed Logic for Automated Teaching Operations) emerged from the University of Illinois in the 1960s as the first major example of computers replacing teachers, and has been followed by myriad attempts, some more successful than others, to create technology-based teaching mechanisms that tailor their instruction to how quickly students master material. (PLATO’s other major innovation was partnership with a commercial vendor, the now defunct Control Data Corporation.)

Higher Education

We now come to the $64 question: what role might trends in higher-education learning technology play in the potential transformation of higher education?

The transformational goal for higher education is to carry out its social and economic roles with greater efficiency and within the resource constraints. Many believe that such transformation requires a very different structure for future higher education. What might that structure be, and what role might information technologies play in its development?

The fundamental purpose of higher education is to advance society, polity, and the economy by increasing the social, political, and economic skills and knowledge of students – what economists call “human capital“. At the postsecondary level, education potentially augments students’ human capital four ways:

• admission, which is to say declaring that a student has been chosen as somehow better qualified or more adaptable in some sense than other prospective students (this is part of Lester Thurow‘s “job queue” idea);
• instruction, including core and disciplinary curricula, the essentially unidirectional transmission of concrete knowledge through lectures, readings, and like, and also the explication and amplification of that through classroom, tutorial, and extracurricular guidance and discussion (this is what we often mean by the narrow term “teaching”);
• certification, specifically the measuring of knowledge and skill through testing and other forms of assessment; and
• socialization, specifically learning how to become an effective member of society independently of one’s origin family, through interaction with faculty and especially with other students.

Sometimes a student gets all four together. For example, MIT marked me even before I enrolled as someone likely to play a role in technology (admission), taught me a great deal about science and engineering generally, electrical engineering in particular, and their social and economic context (instruction), documented through grades based on exams, lab work, and classroom participation that I had mastered (or failed to master) what I’d been taught (certification), and immersed me in an environment wherein data-based argument and rhetoric guided and advanced organizational life, and thereby helped me understand how to work effectively within organizations, groups, and society (socialization).

Most students attend college whose admissions processes amount to open admission, or involve simple norms rather than competition.  That is, anyone who meets certain standards, such as high-school completion with a given GPA or test score, is admitted. In 2010, almost half of all institutions reporting having no admissions criteria, and barely 11% accepted fewer than 1/4 of their applicants. Moreover, most students do not live on campus — in 2007-08, only 14% of undergraduates lived in college-owned housing. This means that most of higher education has limited admission and socialization effects. Therefore, for the most part higher education affects human capital through instruction and certification.

Instruction is an especially fertile domain for technological progress. This is because three trends converge around it:

• ubiquitous connectivity, especially from students’ homes;
• the rapidly growing corpus of coursework offered online, either as formal credit-bearing classes or as freestanding materials from entities like OCW or Khan; and
• perhaps more speculative) the growing willingness of institutions to grant credit and allow students to satisfy requirements through classes taken at other institutions or through some kind of testing or assessment.

Indeed, we can imagine a future where it becomes commonplace for students to satisfy one institution’s degree requirements with coursework from many other institutions. Further down this road, we can imagine there might be institutions that admit students, prescribe curriculum, certify progress, and grant degrees – but have no instructional faculty and do not offer courses. This, in turn, might spawn purely instructional institutions.

One problem with such a future is that socialization, a key function of higher education, gets lost. This points the way to one major technology challenge for the future: Developing online mechanisms, for students who are scattered across the nation or the world, that provide something akin to rich classroom and campus interaction. Such interaction is central to the success of, for example, elite liberal-arts colleges and major residential universities. Many advocates of distance education believe that social media such as Facebook groups can provide this socialization, but that potential has yet to be realized.

A second problem with such a future is that robust, flexible methods for assessing student learning at a distance remain either expensive or insufficient. For example, ProctorU and Kryterion are two of several commercial entities that provide remote exam proctoring, but they do so through somewhat intensive use of video observation, and that only works for rather traditional written exams. For another example, in the aftermath of 9/11 many universities figured out how to conduct doctoral thesis defenses using high-bandwidth videoconferencing facilities rather than flying in faculty from other institutions, but this simply reduced travel expense rather than changed the basic idea that several faculty members would examine one student at a time.

Millinocket

If learning technologies are to transform higher education, we must exploit opportunities and address problems. At the same time, transformed higher education cannot neglect important dimensions of human capital. In that respect, our goal should be not only to make higher education more efficient than it is today, but also better.

Drivers headed for Millinocket rarely pull over any more to ask directions of drawling downeasters. Instead, they rely on the geographic position and information systems built into their cars or phones or computers, which in turn rely on network connectivity to keep maps and traffic reports up to date. To be sure, reliance on GPS and GIS tends to insulate drivers from interaction with the diversity they pass along the road, much as Interstate highways standardized cross-country travel. So the gain from those applications is not without cost.

The same is true for learning technology: it will yield both gains and losses. Effective progress will result only if we explore and understand the technologies and their applications, decide how these relate to the structure and goals of higher education, identify obstacles and remedies, and figure out how to get there from here.

In oversimplified caricature, many colleges and universities have traditionally staffed the line, management, and leadership layers of their IT enterprise thus:

Students with some affinity for technology (perhaps their major, perhaps work-study, perhaps just a side interest) have approached graduation not quite sure what they should do next. They’ve had some contact with the institution’s IT organizations, perhaps having worked for some part of them or perhaps having criticized their services. Whatever the reason, working for an institutional IT organization has seemed a useful way to pay the rent while figuring out what to do next, and it’s been a good deal for the IT organizations because recent graduates are usually pretty clever, know the institution well, learn fast, and are willing to work hard for relatively meager pay.

Moreover, and partly compensating for low pay, the technologies being used and considered in higher education often have been more advanced than those out in business, so sticking around has been a good way to be at the cutting edge technologically, and college and universities have tended to value and reward autonomy, curiosity, and creativity.

Within four or five years of graduation, most staff who come straight into the IT organization have figured out that it’s time to move on. Sometimes a romantic relationship has turned their attention to life plans and long-term earnings, sometimes ambition has taken more focused shape and so they seek a steeper career path, sometimes their interests have sharpened and readied them for graduate school — but in any case, they have left the campus IT organization for other pastures after a few good, productive years, and have been replaced by a new crop of recent graduates.

But a few individuals have found that working in higher education suits their particular hierarchy of needs (to adapt and somewhat distort Maslow). For them, IT work in higher education has yielded several desiderata (remember I’m still caricaturing here): there’s been job security, a stimulating academic environment, a relatively flat organization that offers considerable responsibility and flexibility, and an opportunity to work with and across state-of-the-art (and sometimes even more advanced) technologies. Benefits have been pretty good, even though pay hasn’t and there have been no stock options. Individuals to whom this mix appeals have stayed in campus IT, rising to middle-management levels, sometimes getting degrees in the process, and sometimes, as they have moved into #3 or #2 positions, even moving to other campuses as opportunities present themselves.

Higher-education IT leaders — that is, CIOs, the heads of major decentralized IT organizations, and in some cases the #2s within large central organizations — typically have come from one of two sources. Some have come from within higher-education IT organizations, sometimes the institution’s own but more typically, since a given institution usually has more leadership-ready middle managers than it has available leadership positions, another institution’s. (Whereas insiders once tended to be heavy-metal computer-center directors,  more recently they have come from academic technologies or networking.) Other leaders have come from faculty ranks, often (but not exclusively) in computer science or other technically-oriented disciplines. Occasionally some come from other sources, such as consulting firms or even technology vendors, or even from administration elsewhere in higher education.

The traditional approach staffs IT organizations with well educated, generally clever individuals highly attuned to the institution’s culture and needs. They are willing and able to tackle complex IT projects involving messy integration among different technologies. Those individuals also cost less that comparable ones would if hired from outside. Expected turnover among line staff notwithstanding, they are loyal to the institution even in the face of financial and management challenges.

But the traditional model also tilts IT organizations toward idiosyncrasy and patchwork rather than coherent architecture and efficiency-driven implementation. It often works against the adoption of effective management techniques, and it can promote hostility toward businesslike approaches to procurement and integration and indeed the entire commercial IT marketplace. All of this has been known, but in general institutions have continued to believe that the advantages of the traditional model outweigh its shortcomings.

I saw Moneyball in early October. I liked it mostly because it’s highly entertaining, it’s a good story, it’s well written, acted, directed, and produced, and it involves both applied statistical analysis (which is my training) and baseball (my son’s passion, and mine when the Red Sox are in the playoffs). I also liked it because its focus — dramatic change in how one staffs baseball teams — led me to think about college and university IT staffing. (And yes, I know my principles list says that “all sports analogies mislead”, but never mind.)

In one early scene, the Oakland A’s scouting staff explains to Brad Pitt’s character, Billy Beane, that choosing players depends on intuition honed by decades of experience with how the game is played, and that the approach Beane is proposing — choosing them based on how games are won rather than on intuition — is dangerous and foolhardy. Later, Arliss Howard’s character, the Red Sox owner John Henry, explains that whenever one goes against long tradition all hell breaks loose, and whoever pioneers or even advocates that change is likely to get bloodied.

So now I’ll move from oversimplification and caricature to speculation. To believe in the continued validity of the traditional staffing model may be to emulate the scouts in Moneyball. But to abandon the model is risky, since it’s not clear how higher-education IT can maintain its viability in a more “businesslike” model based on externally defined architectures, service models, and metrics. After all, Billy Beane’s Oakland A’s still haven’t won the World Series.

The Beane-like critique of the traditional model isn’t that the advantage/shortcoming balance has shifted, but rather that it depends on several key assumptions whose future validity is questionable. To cite four interrelated ones:

• With the increasing sophistication of mobile devices and cloud-based services, the locus of technological innovation has shifted away from colleges and universities. Recent graduates who want to be in the thick of things while figuring out their life plans have much better options than staying on campus — they can intern at big technology firms, or join startups, or even start their own small businesses. In short, there is now competition for young graduates interested IT but unsure of their long-term plans.
• As campuses have outsourced or standardized much of their IT, jobs that once included development and integration responsibility have evolved into operations, support, and maintenance — which are important, but not very interesting intellectually, and which provide little career development.  Increased outsourcing has exacerbated this, and so has increased reliance on business-based metrics for things like user support and business-based architectures for things like authentication and systems integration.
• College and university IT departments could once offset this intellectual narrowing because technology prices were dropping faster than available funds, and the resulting financial cushion could be dedicated to providing staff with resources and flexibility to go beyond their specific jobs (okay, maybe what I mean is letting staff buy gadgets and play with them). But tightened attention to productivity and resource constraints have large eliminated the offsetting toys and flexibility. So IT jobs in colleges and universities have lost much of their nonpecuniary attractiveness, without any commensurate increase in compensation. Because of this, line staff are less likely to choose careers in college or university IT, and without this source of replenishment the higher-education IT management layer is aging.
• As IT has become pervasively important to higher education, so responsibility for its strategic direction has broadened. As strategic direction has broadened, so senior leadership jobs, including the CIO’s, have evolved away from hierarchical control and toward collaboration and influence. (I’ve written about this elsewhere.) At the same time, increasing attention to business-like norms and metrics has required that IT leaders possess a somewhat different skillset than usually emerges from gradual promotion within college and university IT organizations or faculty experience. This has disrupted the supply chain for college and university IT leadership, as a highly fragmented group of headhunter firms competes to identify and recruit nontraditional candidates.

I think we’re already seeing dramatic change resulting from all this. The most obvious change is rapid standardization around commercial standards to enable outsourcing — which is appealing not only intrinsically, but because it reduces dependence on an institution’s own staff. (On the minus side, it also tends to emphasize proprietary commercial rather than open-source or open-standards approaches.) I also sense much greater interest in hiring from outside higher education, both at the line and management levels, and a concomitant reappraisal of compensation levels. That, combined with flat or shrinking resources, is eliminating positions, and the elimination of positions is promoting even more rapid standardization and outsourcing.

On the plus side, this is making college and university IT departments much more efficient and businesslike. On the minus side, higher education IT organizations may be losing their ability to innovate. This is yet another instance of the difficult choice facing us in higher-education IT: Is IT simply an important, central element of educational, research, and administrative infrastructure, or is IT also the vehicle for fundamental change in how higher education works? (In Moneyball, the choice is between player recruitment as a mechanism for generating runs, and as a mechanism for exciting fans. Sure, Red Sox fans want to win. But were they more avid before or after the Curse ended with Bill James’s help?)

If it’s the latter, we need to make sure we’re equipped to enable that — something that neither the traditional model nor the evolving “businesslike” model really does.

“There are two possible solutions,” Hercule Poirot says to the assembled suspects in Murder on the Orient Express (that’s p. 304 in the Kindle edition, but the 1974 movie starring Albert Finney is way better than the book, and it and the book are both much better than the abominable 2011 PBS version with David Suchet). “I shall put them both before you,” Poirot continues, “…to judge which solution is the right one.”

So it is for the future role, organization, and leadership of higher-education IT. There are two possible solutions. There’s a reasonably straightforward projection how the role of IT in higher education will evolve into the mid-range future, but there’s also a more complicated one. The first assumes institutional continuity and evolutionary change. The second doesn’t.

IT Domains

How does IT serve higher education? Let me count the ways:

1. Infrastructure for the transfer and storage of pedagogical, bibliographic, research, operational, and administrative information, in close synergy with other physical infrastructure such as plumbing, wiring, buildings, sensors, controls, roads, and vehicles. This includes not only hardware such as processors, storage, networking, and end-user devices, but also basic functionality such as database management and hosting (or virtualizing) servers.
2. Administrative systems that manage, analyze, and display the information students, faculty, and staff need to manage their own work and that of their departments. This includes identity management, authentication, and other so-called “middleware” through which institutions define their communities.
3. Pedagogical applications students and faculty need to enable teaching and learning, including tools for data analysis, bibliography, simulation, writing, multimedia, presentations, discussion, and guidance.
4. Research tools faculty and students need to advance knowledge, including some tools that also serve pedagogy plus a broad array of devices and systems to measure, gather, simulate, manage, share, distill, analyze, display, and otherwise bring data to bear on scholarly questions.
5. Community services to support interaction and collaboration, including systems for messaging, collaboration, broadcasting, and socialization both within campuses and across their boundaries.

“…A Suit of Wagon Lit Uniform…and a Pass Key…”

The straightforward projection, analogous to Poirot’s simpler solution (an unknown stranger committed the crime, and escaped undetected), stems from projections how institutions themselves might address each of the IT domains as new services and devices become available, especially cloud-based services and consumer-based end-user devices. The core assumptions are that the important loci of decisions are intra-institutional, and that institutions make their own choices to maximize local benefit (or, in the economic terms I mentioned in an earlier post, to maximize their individual utility.)

Most current thinking in this vein goes something like this:

• We will outsource generic services, platforms, and storage, and perhaps
• consolidate and standardize support for core applications and
• leave users on their own insofar as commercial devices such as phones and tablets are concerned, but
• we must for the foreseeable future continue to have administrative systems securely dedicated and configured for our unique institutional needs, and similarly
• we must maintain control over our pedagogical applications and research tools since they help distinguish us from the competition.

Evolution based on this thinking entails dramatic shrinkage in data-center facilities, as virtualized servers housed in or provided by commercial or collective entities replace campus-based hosting of major systems. It entails several key administrative and community-service systems being replaced by standard commercial offerings — for example, the replacement of expense-reimbursement systems by commercial products such as Concur, of dedicated payroll systems by commercial services such as ADP, and of campus messaging, calendaring, and even document-management systems by more general services such as Google’s or Microsoft’s. Finally, thinking like this typically drives consolidation and standardization of user support, bringing departmental support entities into alignment if not under the authority of central IT, and standardizing requirements and services to reduce response times and staff costs.

How might higher-education IT evolve if this is how things go? In particular, what effects would it have on IT organization, and leadership?

One clear consequence of such straightforward evolution is a continuing need for central guidance and management across essentially the current array of IT domains. As I tried to suggest in a recent article, the nature of that guidance and management would change, in that control would give way to collaboration and influence. But institutions would retain responsibility for IT functions, and it would remain important for important systems to be managed or procured centrally for the general good. Although the skills required of the “chief information officer” would be different, CIOs would still be necessary, and most cross-institutional efforts would be mediated through them. Many of those cross-institutional efforts would involve coordinated action of various kinds, ranging from similar approaches to vendors through collective procurement to joint development.

We’d still need Brads.

“Say What You Like, Trial by Jury is a Sound System…”

If we think about the future unconventionally (as Poirot does in his second solution — spoiler in the last section below!), a somewhat more radical, extra-institutional projection emerges. What if Accenture, McKinsey, and Bain are right, and IT contributes very little to the distinctiveness of institutions — in which case colleges and universities have no business doing IT idiosyncratically or even individually?

In that case,

• we will outsource almost all IT infrastructure, applications, services, and support, either to collective enterprises or to commercial providers, and therefore
• we will not need data centers or staff, including server administrators, programmers, and administrative-systems technical staff, so that
• the role of institutional IT will be largely to provide only highly tailored support for research and instruction, which means that
• in most cases means there will be little to be gained from centralizing IT,
• it will make sense for academic departments to do their own IT, and
• we can rely on individual business units to negotiate appropriate administrative systems and services, and so
• the balance will shift from centralized to decentralized IT organization and staffing.

What if we’re right that mobility, broadband, cloud services, and distance learning are maturing to the point where they can transform education, so that we have simultaneous and similarly radical change on the academic front?

Despite changes in technology and economics, and some organizational evolution, higher education remains largely hierarchical. Vertically-organized colleges and universities grant degrees based on curricula largely determined internally, curricula largely comprise courses offered by the institution, institutions hire their own faculty to teach their own courses, and students enroll as degree candidates in a particular institution to take the courses that institution offers and thereby earn degrees. As Jim March used to point out, higher education today (well, okay, twenty years ago, when I worked with him at Stanford) is pretty similar to its origins: groups sitting around on rocks talking about books they’ve read.

It’s never been that simple, of course. Most students take some of their coursework from other institutions, some transfer from one to another, and since the 1960s there have been examples of network-based teaching. But the model has been remarkably robust across time and borders. It depends critically on the metaphor of the “campus”, the idea that students will be in one place for their studies.

Mobility, broadband, and the cloud redefine “campus” in ways that call the entire model into question, and thereby may transform higher education. A series of challenges lies ahead on this path. If we tackle and overcome these challenges, higher education, perhaps even including its role in research, could change in very fundamental ways.

The first challenge, which is already being widely addressed in colleges, universities, and other entities, is distance education: how to deliver instruction and promote learning effectively at a distance. Some efforts to address this challenge involve extrapolating from current models (many community colleges, “laptop colleges”, and for-profit institutions are examples of this), some involve recycling existing materials (Open CourseWare, and to a large extent the Khan Academy), and some involve experimenting with radically different approaches such as game-based simulation. There has already been considerable success with effective distance education, and more seems likely in the near future.

As it becomes feasible to teach and learn at a distance, so that students can be “located” on several “campuses” at once, students will have no reason to take all their coursework from a single institution. A question arises: If coursework comes from different “campuses”, who defines curriculum? Standardizing curriculum, as is already done in some professional graduate programs, is one way to achieve address this problem — that is, we may define curriculum extra-institutionally, “above the campus”. Such standardization requires cross-institutional collaboration, oversight from professional associations or guilds, and/or government regulation. None of this works very well today, in part because such standardization threatens institutional autonomy and distinctiveness. But effective distance teaching and learning may impel change.

As courses relate to curricula without depending on a particular institution, it becomes possible to imagine divorcing the offering of courses from the awarding of degrees. In this radical, no-longer-vertical future, some institutions might simply sell instruction and other learning resources, while others might concentrate on admitting students to candidacy, vetting their choices of and progress through coursework offered by other institutions, and awarding degrees. (Of course, some might try to continue both instructing and certifying.) To manage all this, it will clearly be necessary to gather, hold, and appraise student records in some shared or central fashion.

To the extent this projection is valid, not only does the role of IT within institutions change, but the very role of institutions in higher education changes. It remains important that local support be available to support the IT components of distinctive coursework, and of course to support research, but almost everything else — administrative and community services, infrastructure, general support — becomes either so standardized and/or outsourced as to require no institutional support, or becomes an activity for higher education generally rather than colleges or universities individually. In the extreme case, the typical institution really doesn’t need a central IT organization.

In this scenario, individual colleges and universities don’t need Brads.

“…What Should We Tell the Yugo-Slavian Police?”

Poirot’s second solution to the Ratchett murder (everyone including the butler did it) requires astonishing and improbable synchronicity among a large number of widely dispersed individuals. That’s fine for a mystery novel, but rarely works out in real life.

I therefore don’t suggest that the radical scenario I sketched above will come to pass. As many scholars of higher education have pointed out, colleges and universities are organized and designed to resist change. So long as society entrusts higher education to colleges and universities and other entities like them, we are likely to see evolutionary rather than radical change. So my extreme scenario, perhaps absurd on its face, seeks to only to suggest that we would do well to think well beyond institutional boundaries as we promote IT in higher education and consider its transformative potential.

And more: if we’re serious about the potentially transformative role of mobility, broadband, and the cloud in higher education, we need to consider not only what IT might change but also what effects that change will have on IT itself — and especially on its role within colleges and universities and across higher education.

Back in 1977, Ken Arrow, having won the Nobel Prize five years earlier, wondered about the internal functioning of firms. “To what extent is it necessary for the efficiency of a corporation,” he wrote, “that its decisions be made at a high level where a wide degree of information is, or can be made, available? How much, on the other hand, is gained by leaving a great deal of latitude to individual departments which are closer to the situations with which they deal, even though there may be some loss due to imperfect coordination?” The answer depends somewhat on whether the firm has one goal or several, on the correlation among multiple goals, and the degree to which different departments contribute to different goals.

In general, though, the answer is sobering for advocates of decentralization. The severally optimal choices of departments rarely combine to yield the jointly optimal choice for the overall enterprise. That’s not to say that centralization is wrong, of course. It merely means that one must balance the healthy and interesting diversity that results from decentralization against the overall inefficiency it can cause.

If we shift focus from the firm to enterprises within an economic sector, the same observations hold. To the extent enterprises pursue diverse goals primarily for their own benefit rather than for the efficiency of the entire sector, that sector will be both diverse and inefficient — perhaps to the extremes of idiosyncrasy and counterproductivity. Put differently, if the actors within a sector value individuality, they will sacrifice sector-wide efficiency; if they value sector-wide efficiency, they must sacrifice individuality.

Higher education traditionally has placed a high value on institutional individuality. Some years back a Harvard faculty colleague of mine, Harold “Doc” Howe II (who had been US Commissioner of Education under Lyndon Johnson), observed how peculiar it was that mergers and acquisitions were so rarely contemplated, let alone achieved, in higher education, even though by any rational analysis there were myriad opportunities for interesting, effective mergers. (Does the United States really need almost 4,000 nonprofit, degree-granting postsecondary institutions, not to mention 14,000 public school districts?) Among research universities, for example, Case Western Reserve University and Carnegie-Mellon University were two of the few successful mergers, there were some instances of acquisitions and subordinations (I’m not counting Brown/Pembroke, Columbia/Barnard, Tufts/Jackson, or their kin), and several prominent failures — for example, the failed attempts to merge the Cambridge anchors Harvard and MIT. (Wikipedia’s page on college mergers lists fewer than 100 mergers of any kind.)

If higher education isn’t going to gain efficiency through institutional aggregation, then its only option is to do so through institutional collaboration. There are lots of good examples where this has happened: I’d include athletic leagues, part of whose purpose is to negotiate effectively with networks; library collaborations, such as OCLC, that seek to reduce redundant effort; research collaborations, such as Fermilab, through which institutions share expensive facilities; and IT collaborations, such as Internet2.

That last is a bit different from the others, in that involves a group of institutions joining forces to buy services together. Why is joint procurement like that so rare in US higher education? I think there are two tightly connected reasons:

• US higher education has valued institutional individuality far more highly than collective efficiency — that is, it assigns less importance to collective utility (that’s a microeconomics term for the value an actor expects) than to individual utility.
• At the same time, it has failed to make the critical distinction between what Ryan Oakes, of Accenture‘s higher-education practice, recently called “differentiating” activities (those on which institutions reasonably compete) and generic “non-differentiating” activities (those where differences among peers are irrelevant to success). As a result, institutions have behaved competitively in all but a few contexts, even in those non-differentiating areas where collaboration is the right answer.

Although it’s a bit of a caricature, the situation somewhat resembles the scenario for the Rand Corporation‘s 1950s-era game-theory test, The Prisoner’s Dilemma. Here’s a version from Wikipedia:

Two suspects are arrested by the police. The police have insufficient evidence for a conviction, and, having separated the prisoners, visit each of them to offer the same deal. If one testifies for the prosecution against the other (defects) and the other remains silent (cooperates), the defector goes free and the silent accomplice receives the full one-year sentence. If both remain silent, both prisoners are sentenced to only one month in jail for a minor charge. If each betrays the other, each receives a three-month sentence. Each prisoner must choose to betray the other or to remain silent. Each one is assured that the other would not know about the betrayal before the end of the investigation. How should the prisoners act?

The dilemma is this:

• The optimal individual choice for each prisoner is to rat out the other — that is, to “defect” — since this guarantees him or her a sentence of no more than three months, with a shot at freedom if the other prisoner remains silent. Individuals seeking to maximize their own success (to make a “utility-maximizing rational choice”, in microeconomic terms) thus choose to defect. In decision-analytic terms, since prisoner A has no idea what prisoner B will do, A assigns a probability of .5 to each possible choice B might make. A multiplies those probabilities by the consequences to obtain the expected values of his or her two options: (3)(.5)+(0)(.5) = 1.5 months for defecting, and (12)(.5)+(1)(.5) = 6.5 months for cooperating. A chooses to defect. B does the same calculation, and also chooses to defect. Since both choose to defect, each gets a three-month sentence, and they serve a total of six months in jail.
• The optimal choice for the two prisoners together, as measured by the total of their two sentences, is for both to remain silent, that is, to cooperate. This yields a total sentence of one month for each prisoner, or a total of two months total. In contrast, defect/cooperate and cooperate/defect each yield twelve months (one year for one prisoner, freedom for the other) and defect/defect yields six months (three months for each). So the best joint choice is for A and B both to remain silent.

So each prisoner acting in his or her own self interest yields more individual and total prison time than each acting for their joint good — each would serve three months rather than one. But since A cannot know that B will cooperate and vice versa, each of them chooses self interest, and both end up worse off.

The situation isn’t quite the same for several colleges that might negotiate together for a good deal from a vendor, mostly because no one will get anything for free. But a problem like the prisoner’s dilemma arises when one or more members of the group conclude that they can get a better deal from the vendor by themselves than what they think the group would obtain. If those members try to cut side deals, the incentive for the vendor to deal with the other members shrinks, especially if the defecting members’ deals consume a substantial fraction of the vendor’s price flexibility. The vendor prefers doing a couple of side deals to the overall deal so long as the side deals require less total discount than the group deal would. Members have every incentive to cut side deals, vendors prefer a small number of side deals to a blanket deal, and so unless all the colleges behave altruistically a joint deal is unlikely.

And so the $64 question: What would break this cycle? The answer is simple: sharing information, and committing to joint action. If the prisoners could communicate before deciding whether to defect or cooperate, their rational choice would be to cooperate. If colleges shared information about their plans and their deals, the likelihood of effective joint action would increase sharply. That would be good for the colleges and not so good for the vendor. From this perspective, it’s clear why non-disclosure clauses are so common in vendor contracts.

In the end, the only path to effective joint action is a priori collaboration — that is, agreeing to pool resources, including clout and information, and work together for the common good. So long as colleges and universities hold back from collaboration (for example, saying, as about 15% of respondents did in a recent EDUCAUSE survey, that their institutions would wait to see what others achieved before committing to collaboration), successful joint action will remain difficult.