Innovation, Infrastructure & Digital Learning

Innovation, Infrastructure and Digital Learning Kenneth C. Green • The Campus Computing Project INNOVATION, INFRASTRUCTURE & DIGITAL LEARNING

Kenneth C. Green Director, ACAO Digital Fellows Project THE CAMPUS COMPUTING PROJECT® campuscomputing.net • @DigitalTweed

Indiana Digital Learning Summit The Plater Institute Indiana University-Purdue University Indianapolis ® 8 February 2019 campuscomputing.net

Homage to Dwight D. Eisenhower

Five Star General, War Hero, 34th president of the US. . . and university president. “General, the faculty are the university!”

The Campus Computing Project

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Innovation, Infrastructure and Digital Learning Kenneth C. Green • The Campus Computing Project

Great Expectations

Both the processing and the uses of information are undergoing an unprecedented technological revolution. Not only are machines now able to deal with many kinds of information at high speed and in large quantities, but it is also possible to manipulate these quantities so as to benefit from them in new ways. This is perhaps nowhere truer than in the field of education. One can predict that in a few years, millions of schoolchildren will have access to what Philip of Macedon’s son Alexander enjoyed as a royal prerogative: the services of a tutor as well-informed and as responsive as Aristotle. Patrick Suppes Scientific American October, 1966

Great Expectations II

percentage who agree/strongly agree CAOs CIOs Faculty Digital curricular learning resources provide a richer and more personalized 87 87 35 learning experience than traditional CAOs and CIOs print materials. Digital curricular learning resources have great make learning more efficient and 87 88 27 expectations for effective for students. digital pedagogy; Faculty here strongly support the role of technology to enhance teaching and 85 88 -- faculty, not so instruction. much! The senior academic leadership at my institution understands the strategic value of investments in IT infra- 93 90 -- structure, institutional resources, and services.

Green, Provosts, Pedagogy & Digital Learning, 2017 (CAO data) SOURCES Green, Campus Computing, 2016 (CIO data) Green, Going Digital 2016 (faculty data) The Campus Computing Project

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Innovation, Infrastructure and Digital Learning Kenneth C. Green • The Campus Computing Project

What is Digital Learning?

Digital courseware is a solution with the potential to support The correct student-centered learning at scale in postsecondary education. emphasis is on While millions of students use digital courseware today in their student learning college courses, significant opportunity remains for effective digital courseware use to support new teaching and learning and course strategies, improve course accessibility, and drive improvements content, not in learning outcomes for postsecondary students. technology

Courseware in Context applications.

Digital courseware is instructional content that is scoped and sequenced to support delivery of an entire course through purpose-built software. It includes assessment to inform personalization of instruction and is equipped for adoption Your LMS is not across a range of institutional types and learning environments. an example of Online Learning Consortium digital learning!

What is Digital Learning?

I cannot define it, but I know it when I see it.

Justice Potter Stewart Jacobellis v. Ohio (1964)

The Campus Computing Project

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Innovation, Infrastructure and Digital Learning Kenneth C. Green • The Campus Computing Project Insanity

Doing the same Gateway courses are thing over and over American higher again and expecting education’s Bermuda Triangle into which a different outcome. thousands of students enter, never to be seen Incorrectly attributed again. to Albert Einstein

John W. Gardner John W. Gardner Institute for Excellence in Undergraduate Education

The Campus Computing Project

How many psychologists does it take to change Technology a light bulb? NONE! is a The light bulb must WANT to change. Conversation About Change

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Innovation, Infrastructure and Digital Learning Kenneth C. Green • The Campus Computing Project

Teaching is a “High Touch” Profession

Spangenberg: Aristotle’s School, 1880s

Learning is a Personal Experience

Wisdom from the Software Industry The Innovator’s Dilemma

God could create the world in seven days . . .

because there were no legacy systems

and there were no legacy users. What are the legacy systems in education?

The Campus Computing Project

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Innovation, Infrastructure and Digital Learning Kenneth C. Green • The Campus Computing Project

Technology is a Metaphor for Change Technology is also a metaphor for risk. Technology is a means of uncertainty reduction that is made possible by the cause-effect relationships upon which the technology is based . . . .

A technological innovation creates a kind of uncertainty (about its expected consequences) in the minds of potential adopters, as well as representing an opportunity for reduced uncertainty in another sense (reduced by the information base of the technology). . . . Thus, the innovation-decision process is essentially an information-seeking and information-processing activity in which the individual is motivated to reduce uncertainty about the advantages and disadvantages of the innovation. The Campus Computing Project

The Innovation Curve • INNOVATORS: Venturesome; cosmopolitan; they can cope with uncertainty; not always influential.

• EARLY ADOPTERS: Greatest degree of opinion leadership; respected; serve as role models for others; help off-set uncertainty among others.

• EARLY MAJORITY: Deliberate choices; longer decision cycle; links to late majority.

• LATE MAJORITY: Traditional, cautious and skeptical; may adopt out of necessity. Innovation must be safe.

• LAGGARDS: No roles as opinion leaders; they reference the past not the future. Must be certain that innovation will not fail.

Source: Everett M. Rogers, The Diffusion of Innovation The Campus Computing Project

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Innovation, Infrastructure and Digital Learning Kenneth C. Green • The Campus Computing Project

Technology is Disruptive

Issues & Impacts Response • Organizational • Denial practice & process • Anger

• Individual behaviors • Bargaining and preferences • Depression

• Visualization: can I • Acceptance see me/us doing that? On Death and Dying Elizabeth Kübler-Ross

The Campus Computing Project

CAOs and CIOs Rating the Effectiveness of Campus IT Investments

100 pct. reporting “very effective” (6/7); scale: 1=not effective; 7=very effective 90 • Disappointing 80 assessments CAOs CIOs 70 from both 60 CAOs and CIOs about the 50 effectiveness 40 of campus IT 30 investments 20 10 0 On-Campus Online Instruction ERP / Admin Analytics Instruction Info Systems

Sources: Green, Provosts, Pedagogy and Digital Learning, Fall 2017 The Campus Green, Campus Computing 2017 Computing Project

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Innovation, Infrastructure and Digital Learning Kenneth C. Green • The Campus Computing Project

The Key Campus Tech Issues are No Longer about IT

• IT is the “easy part” of technology on campus

• THE CHALLENGES: People, • Document the evidence of planning, policy, programs, impact priorities, silos, egos, and IT • Provide much-needed entitlements support, recognition, and reward for faculty The Instructional Challenge • Communicate the effective- How do we make Digital ness of and need for IT Learning compelling and resources

safe for the faculty? The Campus Computing Project

Visualization

Age Appropriate Underlying Issues Opaque Hose Age Can I do this? Why shouldMatching I do this? Appropriate Opaque Hose Red Matching Evidence of benefit?Shoes Shoes

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Innovation, Infrastructure and Digital Learning Kenneth C. Green • The Campus Computing Project

What Have We Learned Over Four Decades?

Technological plus ça change, Change The more things change, the more • Hardware things stay the same. • Software • Internet • Wireless • Mobile • Analytics • Social Media

The Campus Computing Project

The (Educational) Innovator’s Ecosystem

Innovation does not occur in a vacuum Successful (effective) innovation depends on an ecosystem Alliances that add value • Backend infrastructure

• Front-end user Backend COMPELLING User support Infra- INNOVATION Support structure • Alliances

• Supplanting current Supplement / practice Supplant Requirements The Campus Computing Project

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Innovation, Infrastructure and Digital Learning Kenneth C. Green • The Campus Computing Project

Mapping the Textbook Industry Textbooks: The Ecosystem Can Also Be a Fortress

Backend Alliances that Add Value User Support • High transition Infrastructure • Content • Cross-Licensing • Sales Reps costs • Authors • Distribution • Technology • Teacher’s • Editors providers Guides • Risk • Content • Student Designers Handbooks • Instructional • Reliability and • Test Sets Specialists Sustainability • Web Sites • Contracts • Conferences • Quality • Communities Supplement / Supplant • Call Centers Requirements • Accreditation • Curricular Sequences • Standards/Regs

The Campus Source: Green, Innovation and Infrastructure (2013) Computing Project

False Choices: High Tech vs. High Touch

Five Key Elements of an Whenever new technology is Effective Campus eLearning Plan introduced into society, there must be a counterbalancing • Realistic Definitions and Expectations human response that is high touch or the technology is • Faculty Recognition rejected. The more and Reward high tech, the more • Training and User Support high touch. • Evidence of Impact John Naisbitt Megatrends, 1982 • Sustained Support for IT, Innovation, and Infrastructure

The Campus Computing Project

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Innovation, Infrastructure and Digital Learning Kenneth C. Green • The Campus Computing Project

Tech Enabled High Touch

Baccalaureates Eliminating Math for Baristas the Graduation Accelerator Gap at Georgia State

• Launched in 2014 • Innovative use of analytics to eliminate the • Starbucks employees graduation gap between complete degrees at ASU • 600 computers in clusters minority and white at a redeveloped mall Online students: 1700 more • Counseling and support grads annually vs. five • Adaptive learning services are a critical years ago technology from McGraw supplement to the course Hill • “Monday morning emails experience. followed by 3,000 hours • Faculty on the floor of counseling and working directly with support services” students

The Campus Computing Project

Changing the Data Culture

Culture eats change CHANGE THE CULTURE OF DATA for breakfast. • OLD: What did YOU do wrong? • NEW: How do WE do better?

Peter Drucker Use data as a resource, not as a weapon

The Campus Computing Project

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Innovation, Infrastructure and Digital Learning Kenneth C. Green • The Campus Computing Project

Innovation and the Fear of Trying

If trustees, presidents, provosts, deans, and department chairs really want to address the fear of trying and foster innovation in instruction, then they have to recognize that infrastructure fosters innovation. And infrastructure, in the context of technology and instruction, involves more than just computer hardware, software, digital projectors in classrooms, learning management systems, and campus web sites. The technology is actually the easy part. The real challenges involve a commitment to research about the impact of innovation in instruction, plus recognition and reward for those faculty who would like to pursue innovation in their instructional activities and scholarship.

Kenneth C. Green • Digital Tweed / Inside Higher Ed • 13 July 2017

Guidelines for Machiavellian Change Agents

• Concentrate your efforts • Pick issues carefully; know when to fight • Know the history • Build coalitions • Set modest – and realistic – goals • Leverage the value of data • Anticipate personnel turnover • Set deadlines for decisions • Nothing is static – anticipate change

Source: J. Victor Baldridge, Rules for a Machiavellian Change Agent, 1983 The Campus Computing Project

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Innovation, Infrastructure and Digital Learning Kenneth C. Green • The Campus Computing Project

campuscomputing.net

Kenneth C. Green THE CAMPUS COMPUTING PROJECT ® [email protected] 818.990.2212 @digitaltweed ® campuscomputing.net

Kenneth C. Green is the founding director of The Campus Computing Project, the largest continuing study of the role of eLearning and information technology in American colleges and universities. Campus Computing is widely cited as a definitive source for data, information, and insight about IT planning and policy issues affecting higher education.

He also serves as the director of the Digital Fellows Project of the Association of Chief Academic Officers (ACAO) and as the moderator and co-producer of TO A DEGREE, the postsecondary success podcast of the Bill & Melinda Gates Foundation

Green is the author or editor of some 20 books and published research reports and more than 100 articles and commentaries that have appeared in academic journals and professional publications. His Digital Tweed blog is published by Inside Higher Ed.

In 2002 Green received the first EDUCAUSE Award for Leadership in Public Policy and Practice. The EDUCAUSE award cites his work in creating The Campus Computing Project and recognizes his “prominence in the arena of national and international technology agendas, and the linking of higher education to those agendas.”

A graduate of New College (FL), Green earned his Ph.D. in higher education and public policy at the University of California, Los Angeles.

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13 July 2017 Innovation and the Fear of Trying Kenneth C. Green

Last week in Inside Higher Ed, reporter David Concurrent with keyboarding was, of course, learning Matthews of The Times Higher Education characterized to use and master key computer applications such as “as a surprising conclusion” the work of Carnegie Mellon word processing, spreadsheets, and presentation University anthropologist Lauren Herckis that a major software. Understandably this was a time-intensive and barrier to instructional innovation and technology often frustrating task for many mid-career faculty some utilization in higher education is that faculty “are simply 25-35 years ago, despite the best efforts of their too afraid of looking stupid in front of their students to try institutions to provide faculty only training programs and something new.” one-on-one instruction (alas, often provided by tech- Alas, this is not new news. Nor is it a surprising savvy undergraduates). conclusion. The fear of trying among faculty, because of But even as faculty began to acquire these core tech the fear of looking awkward, foolish, or incompetent in skills, other fear of trying factors emerged. For example, front of their students, dates back to the arrival of the by the early/mid-1990s, I began hearing reports of what I first microcomputers (aka IBM PCs and Macs) in college would come to characterize as a new form of “Oedipal classrooms and campus computer labs in the mid-1980s. aggression in the classroom.” Beyond mocking professors Let us review the reasons why the fear of trying is for their academic demeanor or attire, students could neither new nor surprising. now chastise faculty for their discomfort with More than three decades ago the core technology technology: skills we now view as essential and today we hope are § One dimension of the tech discomfort was when ubiquitous among students and faculty typically were faculty would have to type (or rather, keyboard!) in not. Typing, transformed as keyboarding, emerged as a front of their students. When fumbling keyboarding critical skill. In the early-1980s, I attended an interesting efforts were projected onto a classroom screen, presentation on the future of office automation. The faculty often confronted the stage whispered speaker, a technology expert from Rand Corp., explained comment that “Oh look! Professor Jones can’t the semantic imperative for talking about keyboarding: type.” typing was a (low status) secretarial skill, while § Internet access to primarily sources – including keyboarding implied a higher status skill tied to faculty authors at other institutions – emerged as computers. The semantic rebranding of typing as the second dimension of public professorial keyboarding, the Rand expert explained, would make discomfort. If Prof. Jones was not available to learning keyboarding (aka typing) more acceptable to discuss an assigned reading, students could easily mid- and high-level managers and professionals. email their questions directly to Prof Wilson, author of the assigned article. And, in turn, Prof. Wilson might respond with more than just answers, perhaps asking to see the syllabus that included his or her work. § A third dimension of the public professorial discomfort emerged as classrooms went wireless, enabling students to easily fact check faculty in real time: “Prof. Green: your data are interesting, but dated. I’m looking at the most recent numbers from the same source you used, and things have changed a bit.” Beyond the public potential for embarrassment, one continuing factor in the conversation about innovation and the fear of trying has been the absence of compelling evidence that a new technology or innovative instructional technique really does make a difference in

https://www.insidehighered.com/blogs/digital-tweed/innovation-and-fear-trying

Digital Tweed: Innovation and the Fear of Trying page 2

student learning and outcomes. Four decades into the would like to pursue innovation in their instructional much discussed (and hyped!) “IT revolution” in higher activities. education, a good portion of the campus conversation If trustees, presidents, provosts, deans, and about innovation and technology remains driven by department chairs really want to address the fear of opinion and epiphany, rather than hard evidence trying and foster innovation in instruction, then they have documenting impacts and outcomes. Consequently, it is to recognize that infrastructure fosters innovation. And not surprising that many faculty would understandably be infrastructure, in the context of technology and ambivalent about “attempting to innovate” in their instruction, involves more than just computer hardware, instructional activities if there is no evidence that software, digital projectors in classrooms, learning “innovation” affects student learning and outcomes. management systems, and campus web sites. The Finally, there is the continuing absence of collegial, technology is actually the easy part. The real challenges departmental, and institutional recognition and reward involve a commitment to research about the impact of for innovation that affects the fear of trying. Data from innovation in instruction, plus recognition and reward for The Campus Computing Project confirm that the vast those faculty who would like to pursue innovation in their majority of the two-and four-year American colleges and instructional activities and scholarship. IHE universities have not expanded the algorithm for review and promotion to include faculty efforts at instructional innovation and technology. So despite the public Kenneth C. Green, the Digital Tweed blogger at INSIDE HIGHER ED, is the founding director of The Campus proclamations of presidents and provosts about the “key Computing Project, the largest continuing study of the role of innovative information technology resources here role of computing, eLearning, and information at Acme College,” review and promotion decisions reside technology in American higher education. in the hands of departmental colleagues and chairs who have largely been unwilling adopt an expanded notion of scholarship for their (often younger) colleagues who .

22 July 2018 The Babel Problem with Big Data in Higher Ed

Kenneth C. Green

Over the past decade Bring Data! has emerged as a new cri your unit failed) and not as a resource (how do we use data to de coeur across American colleges and universities. do better). Campus planning initiatives, policy discussions about student success (retention, advising, institutional outcomes, and value added), instructional interventions and innovations, operational effectiveness, and efforts to enhance the return on investment (ROI) are all data dependent. And because “Big Data” has emerged as a powerful engine of analysis and insight in the consumer and corporate sectors, it is no surprise that institutional leaders (along with patrons on campus boards and in state legislatures) have great expectations for the role of Big Data and Analytics across all sectors of American higher education. We can trace the origins of this movement, in part, to Margaret Spellings, currently president of the University of North Carolina System. As Secretary of Education under George W. Bush from 2005-2009, Secretary Spellings frequently cited the “Deming Dictum” – In God we trust; all others bring data – as a charming if disarming way to articulate the both the Finally, the core task of data integration and transparency K-12 and higher education policy priorities of the Bush remain a major challenge. Big data analytics depend on a rich Administration. And in her current position as the leader of the “gumbo” of data from a wide array of campus sources, often University of North Carolina System of Higher Education, generated by several different technology platforms and data President Spellings has also served notice about her “bring data” bases. As a technology rather than a campus culture issue, data priorities for planning, policy, and decision-making across for integration and transparency – creating the “data gumbo” – the 17-campus UNC system. should, in theory, be the easiest of these factors to resolve in a Yet great expectations (and great needs) notwithstanding, timely matter. In fact, it may be among the most difficult. bringing Big Data and Analytics into the operational DNA of The different, and often disparately structured and defined American higher education, might be appropriately data from Student Information Systems (SIS), campus financial characterized as a Sisyphean task. Why? systems, student financial aid systems, HR/personnel files, First, presidential and provost proclamations supporting ePortfolios, Learning Management Systems (LMS), data-informed campus decision-making notwithstanding, much alumni/development programs, and other databases large and of the decision-making in higher education is too often small that are scattered across campuses (and servers and now “informed” by opinion and epiphany, not data and also the Cloud) are the core resources that inform and fuel evidence. Recent national surveys of presidents, provosts, program assessments, institutional interventions, and CFOs, and CIOs reveal that the many (and in some surveys the outcomes analysis. The business intelligence and data mining majority) do not believe that their institutions do a good job of tools that, a more decade ago, allowed Wal-Mart to discover a using data to inform campus planning and decision-making. surprising run on beer in its Florida stores ahead of Hurricane Second, to paraphrase a long ago New Yorker cartoon, Frances in 2004 are the same tools that colleges and higher education has lots of information technology, but too universities now want deploy to gain information and insight many colleges and universities seem to have little timely and about how to improve student success efforts, and how to useful data and information to aid and inform campus planning assess the impact of campus programs and the efficiency of and decision-making. campus operations. Third, both CIOs and CAOs seem to agree that, in general, These same data resources and tools are essential for their institutional investments in data and analytics have not campus efforts to respond to the mandates – some new and been “very effective.” The data about the effectiveness of some ignored for years but now enforced – from accrediting institutional investments come from the fall 2017 ACAO Survey associations, government regulators, and various higher ed of CAOs and the 2016 Campus Computing Survey. sponsors that increasing demand hard data and real evidence Fourth, the “data culture” at too many institutions to document claims for program effectiveness and institutional continues to use as a data weapon (what you did wrong; how outcomes.

https://www.insidehighered.com/blogs/digital-tweed/oer-issues-apples-orchards-and-infrastructure

Digital Tweed • 22 July 2018

Framing the Data Babel Problem many of her CAO colleagues, remains understandably skeptical Over the past four decades higher education’s technology that future investments in analytics will provide the much- providers have repeatedly promised data integration and needed data, information, and insight for Acme State. transparency. Data Transparency and Integration Consider the hypothetical case of Susan Wilson, the provost Data transparency and integration issues might be at Acme State University, a mid-sized public comprehensive characterized, in part, as a “cross-platform integration institution of some 14,000 students. Susan Wilson began her challenge.” Critical data come from a variety of sources and academic career as a newly-hired assistant professor at Acme technology applications or platforms and then have to be State in 1985, as campuses across the US were grappling with coded or processed in a way that allows links to individual strategies to address enrollment problems created by the student records ahead of analysis. demographic downturn caused by the declining size of the high This involves more than simply linking student data via a school graduating cohort in her state and across the US. common ID number in several data files. Data structures, . In 1993, as a member of a Faculty Oversight Committee common data definitions, and other issues typically pose on Enrollment and Retention, Assistant Professor Wilson significant challenges that require time and money (and often heard the directors of university’s Administrative Computing outside expertise) to resolve linking and data integration and Institutional Research units cite the benefits Electronic problems. Data Interchange (EDI) as way to integrate data from various The analytical potential for the data linkage and “data campus sources which would facilitate analytic efforts to gumbo” is a gestalt where the “sum” (information and insight) enhance lagging recruitment and retention activities. from the analytical work should be more than the “parts” were . A decade later, as an Associate Professor and Associate the work is limited to just one or two sets of data from different Dean, Prof. Wilson sat through technical explanations about sources (e.g., background and transcript data from a Student the importance of Web Services and the emergence Information System and transactional data from a Learning of Enterprise Resource Planning (ERP) as a new strategy that Management System). would generate more and (much) better data, information, Unfortunately, rather having access to a “data gumbo” rich and analysis about academic programs and institutional with integrated data sources, most colleges and universities operations for her and her Acme State colleagues. continue to experience “data babel” – critical operational . And following her appointment as Provost in 2015, Susan challenges that involve applications, platforms, and data bases Wilson now feels she has been captive to way too many (often six to eight or more on some campuses) that do not presentations about data and analytics from both campus “talk” with one another and that are difficult to aggregate, personnel and technology providers. These presentations, integrate, analyze, and exploit. including those from “analytic specialists” with barely five Admittedly, cross platform issues (challenges) are common years of experience in the higher education market, typically to higher education. And yet some cross-platform strategies extoll the potential of Big Data and Cloud Services to and solutions do work. For example, the emergence of the ) generate insight and analysis to address a range of critical standards for learning management systems reflects efforts to campus issues, including timely student interventions to provide common standards across various LMS applications. reduce DWIF numbers, as well as informing efforts to The relatively fast and widespread adaption of the LTI standard increase retention and degree completion, and supporting a among higher ed’s LMS providers provides a model for how wide range of student success initiatives.'' tech providers might collaborate on standards that would facilitate data integration and cross-platform data migration. From Watching to Doing Some institutions have solved the “data babel” problem. Arizona State and Georgia State, among others, have each earned accolades and have become the envy of many of peers for their work with data and analytics in service to student learning and student success. Georgia State University reports its impressive gains in retention and graduation are fueled, in part by analytic work that monitors some 800 student risk factors, and also by a significant investment in support services. But ASU and Georgia State are analytic outliers in the landscape of American colleges and universities. So rather than continuing to talk about what Arizona State Despite the recurring promises during her rise through the and Georgia State have done with Big Data, the key question ranks over her decades at Acme State, Provost Wilson does not pressing most institutions is how can they do what Arizona believe that any of these “new technologies” have truly State and Georgia State have done – faster, easier, and at lower delivered on oft-offered (and much needed) data transparency, cost? data integration, and now, Big Data Analytics. Admittedly, she One component of the solution involves identifying the knows that the Student Success movement may have added operational and structural barriers that impede efforts to more elements to the data gumbo; however, she also knows integrate data across the key applications and platforms. that the underlying challenges of integrating the key data Addressing the structural barriers will require that the various elements remain significant. Consequently, Susan Wilson, like platform providers – ok, the harsher term is vendors – learn to Digital Tweed • 22 July 2018

play nice with one another and their campus clients when it Ultimately, it’s time for institutional leadership and comes to data access and integration. Simply “sending data technology providers to stop talking about the benefits of Big streams to the Cloud “ – the strategy I’ve heard in several Data and start working on ways to resolve the data babel and presentations in recent years, will not solve the data related issues that impede institutional efforts to exploit data compatibility and integration challenge presented by data to enhance our understanding of student learning, to help streams from multiple platforms. target interventions intended to increase retention and degree A second key component involves strategies to optimize the completion, and to inform decision-making about campus use of student data in digital learning, leveraging the data management and administration. IHE stream from adaptive courseware and other resources to improve prospects for course completion before course correction is no longer possible. Efforts to leverage digital Acknowledgement: My thanks to Karen Vignare, director of the learning resources and strategies must include operational Personalized Learning Consortium at the Association of Public and strategies that exploit data, provide information and insight, Land-Grant Universities, and James Ptaszynski, Vice President for and launch interventions faster and better to improve the Digital Learning at the University of North Carolina System Office, likelihood that more students succeed. for their very thoughtful comments on an early draft of this blog A third component of the solution must focus on faculty. post. Data-laden PowerPoint presentations and research papers typically lack the compelling narrative required to engage Kenneth C. Green, the Digital Tweed blogger at faculty – to help explain to faculty how analytic insights and the INSIDE HIGHER ED, is the founding director of The interventions that emerge from Big Data analytics can help Campus Computing Project, the largest continuing their students. Compelling evidence of impact, coupled with a study of the role of computing, eLearning, and change of the too-common campus culture of using data as a information technology in American higher education. weapon to using data as a resource are essential if campus officials hope to build faculty trust and foster faculty Follow me on Twitter: @digitaltweed engagement in student success initiatives. Beginning the Fourth Decade of the “IT Revolution” in Higher PlusEducation Ça Change Plus ça change, plus c’est la même chose. (“The more things change, the more things stay the same.”)

By Kenneth C. Green

anuary 24, 1984, will be remembered in the technology world and elsewhere as the day that Apple launched the Macintosh computer. In the crowded Flint Center at De Anza College, a community college across the street from the Apple campus in Cupertino, California, Steve Jobs pulled a beige computer out of a gray travel bag and formally introduced J 1 the Mac to the world.

Less known or remembered about the they are embarking on a Edison did not secure day is that concurrent with the Macintosh journey they can no lon- his fame or fortune launch at De Anza College, undergradu- ger delay.”2 by providing motion ates at Drexel University in Philadelphia pictures that would were the first college students anywhere Great Expectations supplant textbooks in in the world to see the Mac up close That technology jour- public schools. Suppes, and personal. In the run-up to the 1984 ney—our technology jour- however, went on to Macintosh launch, Apple negotiated ney—has taken all of us co-found the Computer agreements with some two dozen, pri- many places over the Curriculum Corpora- marily private, Ivy League or other highly past three decades. The A key tion in 1967, one of the selective colleges and universities to journey has been fueled, first companies to create sell Macs to college students for $1,000 in part, by great expecta- responsibility of instructional software (well below the retail price of $2,495). tions for the use of new and challenge for education. Drexel, then viewed by many as a blue- technologies in educa- for IT leaders A more sobering collar engineering school that lived in the tion—expectations that is to manage view of the challenges shadow of the University of Pennsylvania, were articulated well expectations involved in deploying was somewhat of an outlier in the group before the first PCs and and to technology resources of largely elite institutions that made Macs even arrived on in education was put up the Apple University Consortium college and university communicate forth in 1972 by George (AUC). Yet Drexel, under the technology campuses. For example, the effectiveness W. Bonham, found- leadership of Brian Hawkins (who would in a 1913 newspaper of IT investments. ing editor of Change become the founding president of EDU- interview, the prolific magazine, an influential CAUSE fourteen years later), was the first inventor Thomas Edison publication in higher college or university to sign a Macintosh proclaimed: “Books will education: purchase agreement with Apple, in the soon be obsolete in the public schools. spring of 1983; moreover, the Drexel Scholars will be instructed through the For better or worse, television today agreement, unlike most of the other AUC eye. It is possible to teach every branch dominates much of American life and contracts, provided a Mac to all first-year of human knowledge with the motion manners. . . . Part of [the] lackluster students. And so because Drexel made picture. Our school system will be com- record of the educational uses of televi- the early and significant commitment to pletely changed in ten years.”3 sion is of course due to the heretofore Apple, Drexel students were the first in A half century later, in a 1966 Scien- merciless economies of the medium. the nation to get Macs through campus- tific American article, Stanford Professor But fundamental pedagogic mistrust purchase programs. Patrick Suppes observed: “Both the of the medium remains also a fact Given the current ubiquity of tech- processing and the uses of information of life. The proof of the pudding lies nology on college and university cam- are undergoing an unprecedented tech- in the fact that on many campuses puses and in the consumer market, it can nological revolution.” Anticipating that today fancy television equipment . . . be difficult to understand the excitement technology could bring great benefits now lies idle and often unused. . . . Aca- and impact of the first generation of to education, Suppes added: “One can demic indifference to this enormously personal computers—IBM PCs and Apple predict that in a few more years millions powerful medium becomes doubly Macs—some thirty years ago. The arrival of schoolchildren will have access to incomprehensible when one remem- of these devices on campuses was the what Philip of Macedon’s son Alexander bers that the present college generation catalyst for what, in 1986, EDUCOM Vice enjoyed as a royal prerogative: the per- is also the first television generation.5 President Steve Gilbert and I called the sonal services of a tutor as well-informed “new computing” in higher education: and responsive as Aristotle.”4 Substitute the word technology for televi- “Thousands of faculty members and Note that Edison’s 1913 prediction for sion, and Bonham’s assessment speaks administrators have decided that 1986 the demise of books in public schools volumes about similar challenges inher- is the year that they will have a personal was offered eleven years before sound ent in current efforts with information relationship with computing. . . . Most came to motion pictures. Suppes’s 1966 technology: the high costs of creating academics now getting started on com- prediction of the digital Aristotle was useful and effective instructional con- puting are professionals who haven’t published just a year after IBM released tent, pedagogic distrust (or, at a mini- been computer users before and who the game-changing IBM 360 mainframe mum, ambivalence) about the impact will never think of themselves as com- computer and about a decade before and benefits of instructional content and puter experts. What they realize is that the arrival of the Apple II computer. technologies, and expensive equipment

42 EducausEreview SEPTEMBER/OCTOBER 2015 Beginning the Fourth Decade of the “IT Revolution” in Higher Education: Plus Ça Change

Figure 1. Rating the Effectiveness of Campus Investments in Information 2012. The three groups of survey par- Technology, 2012 ticipants—presidents, chief academic Percentage reporting “very effective” (6/7). officers, and chief information officers Scale: 1 = not effective; 7 = very effective. (CIOs) across all sectors of higher educa- 70 tion—were asked to assess eight separate Presidents (n=1002) Provosts (n=1081) CIOs (n=543) areas of technology investment: academic 60 support services; alumni services; on- 50 campus instruction; online instruction; libraries; management and operations; 40 research and scholarship; and student services. For the purposes of this discus- 30 sion, it is useful to look at the responses of presidents, provosts, and CIOs on the 20 “big four” issues: on-campus instruction; online instruction; management and 10 operations; and analytics. 0 As shown in figure 1, less than half (and often just 40 percent) of presidents On-Campus Online Courses ERP/ Analytics Instruction and Programs Administrative and provosts assessed campus IT invest- Information ments to be “very effective” (a 6 or 7). CIO Systems assessments were slightly different from Source: The 2012 Inside Higher Ed Survey of College and University Presidents; The 2011–12 Inside Higher Ed Survey of College & those of presidents and provosts, but not Kenneth C. Green, University Chief Academic Officers; Campus Computing Survey, 2012 by much—except for the over 60 percent of CIOs who assessed the institutional Figure 2. CIO Assessments of the Effectiveness of Campus Investments investment in “administrative informa- in Information Technology, 2012 vs. 2014 tion systems and operations” as “very effective.”6 Percentage reporting “very effective” (6/7). By 2014, the percentage of CIOs Scale: 1 = not effective; 7 = very effective. 70 reporting “very effective” on these same four metrics improved slightly (see fig- 2012 60 ure 2); comparable data for presidents 2014 and provosts is not available. 50 Unfortunately, the data provides clear evidence that the great expectations for 40 technology to aid and improve instruction, operations, and data analysis have 30 fallen short. Over the years, both technology providers and campus technology 20 advocates/evangelists may have contrib- 10 uted to unrealistic expectations about how quickly an investment in informa- 0 tion technology could deliver expected On-Campus Online/Distance Administrative Data Analysis and gains in instructional outcomes or insti- Teaching and Education Courses Information Managerial tutional performance and productivity. Instruction and Programs Systems and Analytics Operations A key responsibility of and challenge for

Source: Kenneth C. Green, Campus Computing Survey, 2012 & 2014 IT leaders is to manage expectations and to communicate the effectiveness of IT lying fallow on college and university Campus leaders’ perspectives on investments. We can—and must—do better. campuses. Moreover, the irony of Bon- the impact and effectiveness of the ham’s assessment is that the “present col- institutional investment in information Plus Ça Change lege generation” he referenced in 1972 is technology are reflected in three roughly The technologies that are pervasive and the middle-aged (and older) members of concurrent surveys that I conducted ubiquitous both on and off campus today today’s professorate! from December 2011 to September have changed dramatically since the arrival

44 EducausEreview SEPTEMBER/OCTOBER 2015 Beginning the Fourth Decade of the “IT Revolution” in Higher Education: Plus Ça Change

of the first PCs and Macs in the mid-1980s. Figure 3. Campus IT Priorities, Fall 2014 Without question, we have witnessed amazing technological change (the more Percentage reporting “very important” (6/7). things change) over the past three decades: Scale: 1 = not important; 7 = very important. hardware, software, the Internet, wireless Assisting faculty to integrate IT into instruction networks that foster mobility, digital con- Hiring/retaining qualified IT staff Providing adequate user support tent, social media, and “big data” analytics. Leveraging IT for student success However, at least in the higher education IT security arena, this change has been bounded by Mobile computing continuing challenges that often impede Supporting online education efforts to leverage and effectively exploit Learning & managerial analytics Professional development for IT staff the full value of technology investments Financing replacement of aging IT (the more they stay the same). These challenges Upgrading the campus network largely involve the enabling infrastructure: Supporting BYOD integrating technology into instruction; Migrating to the Cloud improving productivity; furthering online ERP upgrade/replacement Shared services/IT collaboration education; recognizing and rewarding fac- Upgrade/replace the LMS ulty who “do IT” in instruction; using data Leveraging social media to aid and inform decision making; and managing expectations about information 0 10 20 30 40 50 60 70 80 90 technology. service In many ways, colleges and universi- technology ties now lag behind where they once led. Consumer markets and corporations Source: Kenneth C. Green, Campus Computing Survey, 2014 move and adopt more quickly. Conse- quently, higher education’s concurrent continuing efforts with the instructional “providing adequate user support” and delay in implementation and effective integration of IT resources. That’s the “leveraging IT for student success” (both exploitation of many common consumer- clear message that emerges from more at 74 percent), and “IT security” (69 per- and corporate-sector technologies causes than a decade of data about IT priorities cent). And although the fall 2014 numbers many students, faculty, administrators, from the annual Campus Computing for top IT priorities vary slightly by sector board members, and other observers to Survey of CIOs and senior campus offi- (e.g., research universities vs. community ask: “Why can I do these things off cam- cials. For example, in the fall of 2014, 81 colleges), there is great consistency about pus but not on campus?” percent of the CIOs and senior IT officers the top three items across sectors: instruc- Three decades into the much- participating in the survey identified the tion, staffing, and user support.7 discussed and often overhyped technol- instructional integration of information How is higher education addressing ogy revolution in higher education, it is technology as a “very important” campus the top technology priority of instruc- increasingly clear that the major tech- IT priority over the next two to three tional integration of IT resources? Are nology challenges confronting higher years (see figure 3), followed by “hiring/ colleges and universities assessing their education are not about technology retaining qualified IT staff” (76 percent), work in this area? per se (i.e., the things we buy). Rather, as EDUCAUSE publications have often Table 1. Campuses with a Formal Program to Assess the Impact noted, the challenges involve how we of Information Technology on Instruction and Learning Outcomes deploy various technologies effectively (the things we do with technology). 2002 2005 2008 2011 2014 Percent Change And I would argue that perhaps most (%) (%) (%) (%) (%) 2002–2014 important, these challenges are about All Institutions 19.8 35.9 43.6 40.5 23.2 17.1 the people, program, policy, planning, Public Research Universities 29.2 39.5 52.0 46.7 26.2 -11.3 political, and budget issues that often Private Research Universities 34.2 35.4 52.3 50.0 26.2 -23.4 impede implementation efforts. Public BA/MA Institutions 18.7 42.3 45.3 40.9 29.8 43.3 Technology Priorities Private BA/MA Institutions 13.2 31. 0 35.6 32.8 15. 8 19.7 We should not be surprised that the Community Colleges 21.7 34.4 45.1 44.5 27.5 26.7 biggest technology challenges focus on Source: Kenneth C. Green, Campus Computing Survey (selected years)

46 EducausEreview SEPTEMBER/OCTOBER 2015 Unfortunately, most institutions over in things like inventory control, and the past decade or so have not invested showed up as much or more in non- in formal efforts to assess the impact of technology businesses like retail as in technology in instruction and learning; high-technology industries themselves. moreover, whereas some sectors have Nothing like that is happening now.”9 experienced modest gains, the numbers “Great headlines, but modest results” have declined between 2002 and 2014 (to paraphrase Krugman) might also for public research and private research be an appropriate characterization of universities (see table 1) and generally technology and productivity in higher remain lower overall: under 30 percent education. Admittedly, economists have across all sectors in the fall of 2014. With- a precise definition for productivity and out a commitment to evaluating invest- specific metrics for measuring produc- ments in innovation, we can’t know what tivity. However, you’ll need more than works. a good economics textbook to secure consensus on the meaning of “academic The Productivity Conundrum productivity” among faculty and admin- Recent reports suggesting that produc- istrators, with the latter recognizing the tivity in the United States has declined need to improve “academic productiv- should be a catalyst for the continuing ity” as a way to reduce (or at least contain) conversation about productivity and the rising cost of higher education. technology in higher education. Some Indeed, much as we struggle with productivity experts argue that the the meaning and attributes of qual- productivity-enhancing impacts of new ity in higher education, so too do we technologies (e.g., iPhones, social media) struggle with the meaning and attri- are less than the productivity gains that butes of productivity, particularly in the followed the emergence of the personal context of campus investments in and computer and the Inter- expenses for technology net three decades ago.8 in research, instruction, And then there is the operations and man- May 2015 assessment agement, and support by Paul Krugman, a services. Yet there is an Princeton University implied expectation that economics professor IT investments in higher and Nobel laureate: education should result “The whole digital area, in improved productiv- spanning more than ity. The questions then four decades, is looking become: How would we like a disappointment. know? What should we New technologies have measure? yielded great headlines, Most institutions Yes, we can prob- but modest economic ably measure produc- results.” Krugman over the past tivity in the research added: “New technol- decade or so have domain, and it is likely ogy is supposed to serve not invested in significant. Simula- businesses as well as formal efforts to tions and analysis that consumers, and should assess the impact once consumed huge be boosting the proof technology in computer resources for duction of traditional instruction and computation and stor- as well as new goods learning. age and that previously [and services]. The big required significant productivity gains of (and expensive) main- the period from 1995 frame time are now rou- to 2005 came largely tinely run, often quickly, www.educause.edu/er SEPTEMBER/OCTOBER 2015 Educausereview 47 Beginning the Fourth Decade of the “IT Revolution” in Higher Education: Plus Ça Change

on notebook, desktop, and lower-cost technology investments. plete the course, rather than just browse supercomputers. Yet despite the big gains in online or audit. For example, a recent HarvardX It is in the other domains—instruc- enrollments across all sectors and seg- report revealed that 22 percent of the tion, operations and management, and ments, it is not clear that “going online” “intend to complete” registrants across support services—that the conversa- has reduced costs and increased instruc- nine HarvardX courses earned a cer- tion about information technology and tional or institutional productivity. Part tificate.12 These numbers are better than productivity becomes problematic. To of the problem is that colleges and uni- the single-digit completion rates gener- be sure, many of higher education’s IT versities do not do a good job of dealing ally reported for the larger population investments over the past three decades with cost accounting—particularly cost of MOOC registrants, but something have made improvements in these accounting for instructional programs. is missing here. Where is an appropri- domains. The Internet has brought rich Instructional “costs” in online programs ate (indeed, necessary!) comparison digital content to students and faculty are often taken as the direct cost of fac- number for more “conventional” online across all sectors, segments, and dis- ulty time, with little acknowledgment courses or more traditional classroom- ciplines. Online registration and fee of any of the other costs involved in based courses? In the case of the latter payment have saved countless students developing and supporting online pro- courses—such as developmental math, countless hours previously spent stand- grams: course and content development, introductory psychology, organic chem- ing in long registration and payment the use of instructional support ser- istry, or Elizabethan sonnets—almost all lines at the beginning of each term. vices, the time provided by IT support those who register probably “intend to Analytic systems now automatically staff to assistant students and faculty, complete” the course. Would the fac- pour data into dashboards as part of the and allocating the true overhead costs ulty, department chairs, and deans who effort to aid and inform decision mak- involved in individual online courses supervise these courses be satisfied with ing. Monitoring systems send automatic and programs. a 22 percent or even a 35 percent comple- e-mails to students and faculty when The productivity pressures are tion rate? Unlikely. they detect signs that students may be reflected in the fawning endorsements In this context we might consider the falling behind in coursework. But in for MOOCs we witnessed several years data from a recent study—conducted by many ways, these examples reside at the ago. Part of the attraction of MOOCs was the Community College Research Cen- margin, not the core, of the productivity the potential for scale— ter at Teachers College, conundrum. Despite these gains, higher the opportunity to Columbia University—of education costs have not declined. The enroll 10,000 or 100,000 online and on-campus understanding that technology will students in a single courses at two commu- enhance productivity and consequently course. But completion nity colleges. Students reduce costs has not played out in most rates are dismal (in the who enrolled in (non- sectors of American higher education. single digits), user sup- MOOC) online courses Although campus IT budgets may port issues are signifi- were almost twice as ebb and flow somewhat during good cant, development costs likely to withdraw and bad economic times, institutions are high, and revenues from or fail the class continue to spend significant sums on (for the institutions that than were their coun- information technology—about 5 to 6 seek to make money terparts in face-to-face percent of the total institutional oper- from “free” courses”) (F2F) courses. But even ating budget—for instruction, opera- may be problematic. If campus here, the completion tions and management, and support Over the past two officials are truly rate was 70–80 percent services.10 Yet few would argue that years, the response to for students in online individual institutions, as well as higher the low-completion committed to courses and 80–90 per- education as an “industry,” are now more critique of MOOCs has advancing the role cent for students in F2F productive because of IT expenditures involved a revisionist of technology in courses.13 Also of inter- and investments. redefinition of “course instruction, then est may be a 2013 study completion” in the con- it is time for these of the completion rates MOOCS and Online Education text of student intention. leaders to stand up for some 5,700 students Online enrollments have exploded over That is, course comple- for and stand with enrolled in either (non- the past fifteen years.11 Online courses tion is calculated based the faculty who are MOOC) online or tradi- and programs would seem to be the one on those whose intent, doing this work. tional (F2F) courses at a higher education domain that could at the time of MOOC public comprehensive showcase the productivity gains from registration, was to com- university. Although

48 EducausEreview SEPTEMBER/OCTOBER 2015 Table 2. Campuses with a Formal Program to Recognize and Reward the Use of Information Technology as Part of the Routine Faculty Review and Promotion Process

1997 2002 2005 2008 2011 2014 Percent Change (%) (%) (%) (%) (%) (%) 1997–2014 All Institutions 12.2 17. 4 19.3 19.1 19.8 16.4 32.7 Public Research 7. 8 16.9 14.5 16.0 13.3 18.5 137. 2 Universities Private Research 10.0 5.9 10.4 9.1 7.1 9.5 -5.0 Universities Public BA/MA 12.2 22.3 25.0 21.7 25.8 16.9 38.5 Institutions Private BA/MA 14.4 15.4 20.1 16.4 19.0 12.3 -15.3 Institutions Community Colleges 12.1 18.2 19.8 24.6 25.5 23.9 97.5

Source: Kenneth C. Green, The Campus Computing Survey (selected years) there was a statistically significant differ- earned tenure. Indeed, the issue of rec- ence in the completion rates (higher for ognition and reward remains one of the F2F courses), both the online and F2F most daunting issues facing faculty of courses reported completion rates over all ranks—but especially junior (tenure- 93 percent.14 track) faculty—as they build their schol- The course completion numbers for arly portfolios. non-MOOC online and F2F courses Data from the fall 2014 Campus cited in these two reports are more than Computing Survey reveals that on aver- three to four times better than the 22 age, two-fifths of institutions support percent completion rate reported for instructional innovation with grants to the HarvardX students “who intend” to help faculty redesign courses or create complete MOOCS. To date, the experi- simulations, ranging from 26.9 percent ence with MOOCs suggests that they are in private BA/MA institutions to 56.9 one point on the continuum of online percent in public research universities.15 education options for students and insti- Yet too few institutions have expanded tutions. But at present, MOOCs may at the algorithm for faculty review and pro- best supplement, not supplant, more motion to include technology. Consider, conventional approaches to both online for example, the trend data on “review and F2F courses. and promotion” from the annual Cam- pus Computing Survey. The good news Faculty Recognition and Reward is that in many sectors, the proportion Across all sectors, most colleges and uni- of institutions that have expanded the versities loudly and proudly proclaim faculty review and promotion criteria their commitment to the innovative use to include technology has increased of technology in online and on-campus dramatically. The exceptions are private instruction. Concurrently, most institu- research universities and private BA/MA tions continue to ignore and, more often, institutions, where the numbers remain punish faculty members who would largely the same as they were in 1997. like to have their efforts in innovative But even with the gains, the numbers in and effective uses of IT resources in all sectors are still very low: less than 25 instruction considered in the review and percent in the fall of 2014 (see table 2). promotion process. The conventional The survey data cited above provides wisdom often offered to younger faculty clear evidence that despite the procla- is “don’t do tech” until after they have mations of presidents, provosts, and www.educause.edu/er SEPTEMBER/OCTOBER 2015 Educausereview 49 Beginning the Fourth Decade of the “IT Revolution” in Higher Education: Plus Ça Change

board chairs about how insight—to the critical vides benchmarking data on various their institutions have planning and policy IT metrics. Whereas the Campus Com- made significant invest- discussions about insti- puting Project focuses primarily on IT ments to leverage infor- tutional assessment and planning and policy issues, the CDS mation technology for outcomes that affect all provides detailed institutional data on instruction, the opera- sectors of U.S. higher IT financials, staffing, and services.18 tional decisions about education.”17 Yet in 2011, less than two-fifths of tenure and promotion Admittedly, we have presidents, provosts, and chief financial depend on the decisions made some impor- officers ranked their institutions’ use of of senior faculty and tant gains in the data data to aid and inform decisions as “very department chairs— domain. Technol- effective” (see figure 4). In 2012, only and on whether these ogy providers have 22.7 percent of CIOs, 37.7 percent of departmental leaders improved the range of provosts, and 39 percent of presidents will recognize and sup- the analytic resources rated their institutional investment in port the technology they now offer campus analytics as “very effective” (see figure efforts and activity of clients. Colleges and 1). By 2014, just 30 percent of CIOs rated faculty into and through universities are begin- investment in analytics at their institu- the review and promo- ning to leverage real- tions as “very effective” (see figure 2). tion process. time student data from Finally, in the effort to make better If campus officials— In the effort to the learning manage- use of data for decision making, we also including IT leaders— make better use of ment systems and other have to address the data culture in higher are truly committed to data for decision sources to send warning education. Too often, data is used as advancing the role of making, we also notices to both faculty a weapon (What was done wrong?) as technology in instruc- have to address and students regarding opposed to a resource (How can we do tion, then it is time for course progress. On a better? What’s the path to doing better?). these leaders to stand the data culture in broader scale, the EDU- The model here should be continuous up for and stand with higher education. CAUSE Core Data Ser- quality improvement: using data to aid the faculty who are vice (CDS), launched and inform decision making and to doing this work, affirm- by former EDUCAUSE help improve programs, resources, and ing the value of technology for those President Brian Hawkins in 2002, pro- services. who wish to affirm it as part of their scholarly portfolio. Figure 4. Using Data to Aid and Inform Campus Decision Making, 2011

The Data Challenge Percentage reporting “very effective” (6/7). The third “A” in the accessibility, afford- Scale: 1 = not effective; 7 = very effective. ability, and accountability mandates of 70 A Test of Leadership (the 2006 Spelling Commission report) was a clear direc- 60 tive to higher education and to college and university leaders to bring data to 50 address the “remarkable shortage of clear, accessible information about cru- 40 cial aspects of American colleges and 30 universities.”16 As I wrote in EDUCAUSE Review at 20 the time: “The issue before [the higher education IT community] in the wake 10 of the Spellings Commission report concerns when college and univer- 0 sity IT leaders will assume an active Presidents Provosts/Chief CFOs role, a leadership role, in these discus- Academic Officers

sions, bringing their IT resources and Source: The 2011 Inside Higher Ed Survey of College and University Presidents; The 2011–12 Inside Higher Ed Survey of College & expertise—bringing data, information, and University Chief Academic Officers; The 2011 Inside Higher Ed Survey of College and University Business Officers

50 EducausEreview SEPTEMBER/OCTOBER 2015 Priorities: What We these continuing efforts and invest- Must Do Better ments in teaching, learning, and edu- For the past several decades (and par- cational outcomes. ticularly over the past ten years), there n Productivity. Much as higher educa- has been much talk about the need for tion has struggled with the con- change in higher education. Concur- versation about quality, so too will rently, there has been much talk about academe continue to struggle with a technology as a catalyst for change. Yet candid discussion about productiv- as noted above, the key technology chal- ity. It is now time for academic lead- lenges that confront higher education are ers, including higher education’s IT not about technology per se; rather, they leadership, to have frank, candid, and are about our efforts to make effective public conversations about productiv- use of technology resources, and they ity: What are the appropriate metrics? are about the people, planning, program, What are reasonable expectations? policy, and budget issues that often What might technology contribute? impede these efforts. And what are the limits of informa- As we look ahead to the fourth decade tion technology in the discussion of the technology revolution in higher about productivity? education, campus and IT leaders must n Online Education. The large numbers focus on the enabling infrastructure that will of students enrolled in online courses allow (a) students of all ages and back- and the growing number of institu- grounds to realize their educational aspi- tions that offer online programs rations; (b) faculty across all disciplines speak to both student interest and and sectors to realize their instructional institutional aspirations and oppor- aspirations and scholarly goals; and (c) tunities. The data on the educational institutions across all sectors to do a better outcomes of (non-MOOC) online job of exploiting the power and poten- courses and programs remains tial of technology resources to enhance mixed. To do better, institutions must instruction, operations and management, commit to significant and sustained and support services. efforts to evaluate their online efforts, Accordingly, as we look beyond bits documenting what works and what and bytes, beyond hardware and soft- needs to improve. Concurrently, we ware, beyond network speeds and feeds, need a new candor about the true and beyond the ebb and flow of IT bud- costs of developing online programs, gets in good times and difficult times, the which includes full cost accounting priorities for information technology in for the people and the institutional higher education for the next decade are resources required to support online clear: programs and online students. n Recognition and Reward. We must move n User Support. Colleges and universi- to an expansive definition of schol- ties across all sectors must commit to arship in order to value the efforts major improvements in user training of faculty who commit to making and support, including support for technology and experiments with IT faculty who want to do more with resources part of their instructional IT resources in their instructional portfolios. It is time for the deans, activities. department chairs, and senior faculty n Assessment. Opinion and epiphany who populate review and promotion cannot be allowed to dominate the committees to stand up for and stand conversations about institutional IT with their colleagues who are inno- policy and planning. If colleges and vating with technology. universities are going to invest in n Data as a Resource. Higher education technology to support instruction, institutions must stop using data as they also need to assess the impact of a weapon against students, faculty, www.educause.edu/er SEPTEMBER/OCTOBER 2015 Educausereview 51 Beginning the Fourth Decade of the “IT Revolution” in Higher Education: Plus Ça Change

and programs. Rather, they should areas of content and services. However, Mirror, July 9, 1913, p. 24. See “Books Will Soon commit to using data as a resource our reach continues to exceed our grasp, Be Obsolete in the Schools,” Quote Investigator, February 15, 2012, http://quoteinvestigator.com/ that provides information and insight our aspirations continue to fall short of 2012/02/15/books-obsolete/. about the need for change and the our implementations, and the corporate 4. Patrick Suppes, “The Uses of Computers path toward that change. and consumer experience continues in Education,” Scientific American 215, no. 3 (September 1966), https://suppes-corpus n In to cast a shadow over campus efforts. The Value of Information Technology. .stanford.edu/article.html?id=67. general, IT leaders have not done a A renewed commitment to an enabling 5. George Bonham, “Television: The Unfulfilled good job of communicating the value infrastructure, as part of our ongoing Promise,” Change 4, no. 1 (February 1972), 11. of information technology to campus investment in IT resources, will help to 6. Kenneth C. Green, with Scott Jaschik and Doug Lederman, The 2012 Inside Higher Ed Survey audiences. Moreover, institutional ensure that the more things change, the of College and University Presidents; Kenneth C. leaders must do a better job of con- less they will stay the same. n Green, with Scott Jaschik and Doug Lederman, veying the value and impact of higher The 2011–12 Inside Higher Ed Survey of College & University Chief Academic Officers; Kenneth education’s investments in infor- Notes C. Green, Campus Computing, 2012 (Encino, mation technology to off-campus 1. Harry McCracken, “Exclusive: Watch Steve Jobs’ CA: The Campus Computing Project, 2012), audiences: board members, project First Demonstration of the Mac for the Public,” http://www.campuscomputing.net/item/ Time, January 25, 2014, http://time.com/1847/ campus-computing-2012-mixed-assessments-it- sponsors, patrons, alumni, and gov- steve-jobs-mac/. effectiveness. ernment officials. 2. Steven W. Gilbert and Kenneth C. Green, “The 7. The top Campus Computing Survey priorities New Computing in Higher Education,” Change, align with the number 1 and number 2 IT issues May/June 1986, p. 33. See also Kenneth C. Green, in the annual EDUCAUSE list for 2014: (1) Hiring Higher education has invested sig- “The New Computing Revisited,” EDUCAUSE and retaining qualified staff, and updating the nificant time and money in information Review, 38, no. 1(January/February 2003), https:// knowledge and skills of existing technology technology over the past three decades. net.educause.edu/ir/library/pdf/ERM0312.pdf. staff; (2) Optimizing the use of technology in 3. Frederick James Smith, “The Evolution of the teaching and learning in collaboration with And admittedly, academe has experi- Motion Picture: VI—Looking into the Future academic leadership, including understanding enced some significant benefits in the with Thomas A. Edison,” New York Dramatic the appropriate level of technology to use. The

52 EducausEreview SEPTEMBER/OCTOBER 2015 EDUCAUSE list of top IT issues is developed Higher Education, A Report of the Commission by a panel of experts and then voted on by the Appointed by Secretary of Education Margaret EDUCAUSE community. See Susan Grajek and Spellings (Washington, DC: U.S. Dept. of the 2014–2015 EDUCAUSE IT Issues Panel, “Top Education, 2006), 4, https://www2.ed.gov/about/ 10 IT Issues, 2015: Inflection Point,” EDUCAUSE bdscomm/list/hiedfuture/reports/final-report Review 50, no. 1 (January/February 2015), http:// .pdf. www.educause.edu/ero/article/top-10-it-issues- 17. Kenneth C. Green, “Bring Data: A New Role 2015-inflection-point. for Information Technology after the Spellings 8. Alan S. Blinder, “The Mystery of Declining Commission,” EDUCAUSE Review 41, no. 6 Productivity Growth,” Wall Street Journal, May 14, (November/December 2006), http://www 2015, http://www.wsj.com/articles/the-mystery- .educause.edu/ero/article/bring-data-new- of-declining-productivity-growth-1431645038. role-information-technology-after-spellings- 9. Paul Krugman, “The Big Meh,” New York Times, commission. May 25, 2015, http://www.nytimes.com/2015/05/ 18. Kenneth C. Green, David L. Smallen, Karen 25/opinion/paul-krugman-the-big-meh.html. L. Leach, and Brian L. Hawkins, “Data: Roads 10. Kenneth C. Green, Campus Computing, 2014 Traveled, Lessons Learned,” EDUCAUSE Review (Encino, CA: The Campus Computing Project, 40, no. 2 (March/April 2005), http://www 2014), http://www.campuscomputing.net/item/ .educause.edu/ero/article/data-roads-traveled- campus-computing-2014. lessons-learned; Leah Lang, “Benchmarking to 11. I. Elaine Allen and Jeff Seaman, Changing Course: Inform Planning: The EDUCAUSE Core Data Ten Years of Tracking Online Education in the United Service,” EDUCAUSE Review 50, no. 3 (May/June States (Boston: Babson Survey Research Group, 2015), http://www.educause.edu/ero/article/ 2013), http://www.onlinelearningsurvey.com/ benchmarking-inform-planning-educause-core- reports/changingcourse.pdf. See also Ashley data-service. A. Smith, “The Increasingly Digital Community College,” Inside Higher Ed, April 21, 2015, https:// © 2015 Kenneth C. Green. The text of this article is www.insidehighered.com/news/2015/04/21/ licensed under the Creative Commons Attribution- survey-shows-participation-online-courses- NonCommercial-NoDerivatives 4.0 International growing. License (http://creativecommons.org/licenses/by-nc- 12. Justin Reich, “MOOC Completion and Retention in the Context of Student Intent,” EDUCAUSE nd/4.0). Review, December 8, 2014, http://www.educause .edu/ero/article/mooc-completion-and- retention-context-student-intent. See also Kenneth C. Green (cgreen@ Daphne Koller, Andrew Ng, Chuong Do, and campuscomputing.net), Zhenghao Chen, “Retention and Intention the first recipient of the in Massive Open Online Courses: In Depth,” EDUCAUSE Review, June 3, 2013, http://www EDUCAUSE Leadership .educause.edu/ero/article/retention-and- Award for Public Policy intention-massive-open-online-courses- and Practice (2002), is the depth-0. 13. “What We Know about Online Course founding director of The Campus Computing Outcomes,” CCRC, Teachers College, Columbia Project (http://www.campuscomputing University, April 2013, http://ccrc.tc.columbia .net). Launched in 1990 and celebrating its .edu/media/k2/attachments/what-we-know- about-online-course-outcomes.pdf. 25th year in 2015, Campus Computing is 14. Wayne Atchley, Gary Wingenbach, and Cindy the largest continuing study of the role of Akers, “Comparison of Course Completion e-learning and IT planning and policy issues and Student Performance through Online and Traditional Courses,” International Review of in American higher education. Green’s Digital Research in Open and Distributed Learning, 14, no. 4 Tweed blog (http://www.insidehighered.com/ (September 2013), http://www.irrodl.org/index blogs/digital-tweed) is published by Inside .php/irrodl/article/view/1461/2627. 15. Green, Campus Computing 2014. Higher Ed. 16. A Test of Leadership: Charting the Future of U.S.