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Origins, Part V: Exploring Institutional Research

The kinds of challenges I came up against while thinking through the issues I describe in Origins posts III (quality in distance learning) and IV (inputs versus outputs) have led me over the last year to an interest in the world of Institutional Research (click to read Wikipedia entry on I.R.).

I recently applied to the graduate certificate program in I.R. at Penn State University. I hope to complete the program online as a way of further exploring the field and finding out if it's what will hold my interest for the rest of my career in Higher Education. I'm delaying my plans for a doctoral program right now, hoping that the certificate program would give me a chance to hone in on some ideas for my dissertation, assuming I end up doing something related to I.R...

Perhaps the best thing for this post would be to simply include here the personal statement I wrote for my application to Penn State. This is the long form--I discovered after I wrote it that I had to limit it to one page, so the version I submitted is a bit shorter. In any case, this provides some good background related to why I'm interested in I.R. (and repeats some of what I've said in earlier "orgins" posts):

I never would have predicted that I’d become interested in data. In my positions of increasing responsibility related to technology in education, though, I have faced continued pressure to justify the use of technological tools for teaching and learning. I’ve been asked to demonstrate the effectiveness of these tools in achieving intended outcomes, and have often struggled to put together the data to provide such illustrations. My search for the right information, and enough of it, has gradually led me to a fascination with data and its implications for educational decision-making.

This search also raised for me a lot of questions about data and effectiveness. As I began to undertake the job of illustrating technology’s value, I discovered that data alone didn’t tell the whole story. In fact, I often found data meaningless in the absence of other important information: What are the goals and intended outcomes? What are the specific strategies and applications we’ve deployed toward reaching these outcomes? How do we know which inputs are supporting, or creating barriers to, our success? What about information and data from other institutions—can this tell us something about ourselves? These questions have often been harder to answer than I thought they would be, but I learned that the real value of the data was not only in what it could tell you, but also in what it made you question.

One example of learning the value of data—specifically its relationship to goals and outcomes—comes from Marlboro College in Vermont, where I led curriculum development for a graduate program focused on technology in education. Marlboro College was at the time a school decidedly non-technological and more than a bit resistant to technology, but I feel that we succeeded in communicating the value of technology in education. We did this by asking what the core institutional values and goals were. A student-centered curriculum is a fundamental part of the college’s mission, and we were able to show how technology increases the institution’s and instructors’ ability to be student-centered. Since many of our students in this program were teachers themselves, we also focused the curriculum on developing goal setting and problem solving skills. Students were asked to develop technology-based solutions to real educational problems and eschew using technology for its own sake. I had learned that data must be viewed within a context of institutional goals, and applied this insight to my work.

As director of distance education at Southern New Hampshire University, I continued to work on tying my efforts to institutional objectives. I also began to find that data could be tremendously useful in plotting strategies for working toward objectives. Enrollment, student satisfaction, and student success (drop/withdrawal/failure rates and grade distributions) all became important indicators to me, but their utility went beyond providing evidence for a match with the strategic plan. What these data started to do was to tell me stories of the experiences that our students and faculty were having. I was able to use the stories that the data were telling to look at our inputs—the technology we were deploying, the marketing strategies, student support services, and faculty development. I learned that the data could give us hints about how to design the right combination of inputs to grow the programs.

At North Shore Community College, I am on the senior staff of the college’s Academic Affairs component. I continue to employ the lessons I’ve learned about data and asking the right questions. My job is largely about setting the strategic direction for educational technology at the institution, and connecting that strategy to other areas in Academic Affairs, Student and Enrollment Services, Administrative Affairs, and Institutional Advancement. I’m a little closer to a 33,000-foot view of the planning process now, and I’ve discovered that information about goals and inputs, while valuable, is not all that’s needed to make decisions. Also necessary is the ability to see that although these things drive the institution’s planning efforts, strategy requires more than a simple laundry list of goals, inputs, and outcomes. A process must be applied for all this data to be turned into strategy.

In an effort to advance my strategic efforts, I’m looking outward. I want to know what the data are “out there.” What have other institutions set as their objectives? What are their inputs? How well are they doing in reaching their outcomes? As an advisory board member of the National University Telecommunications Network (NUTN), I’ve been contributing my ideas to a distance learning benchmarking initiative. The initiative seeks to scaffold individual institutional strategies by providing results data, in the aggregate, from a wide variety of participating schools. Through my participation in the effort, I’m trying to remind the developers that results can’t be “unbundled” from the best practices. Both the outcomes data and the inputs must be a part of the planned benchmarking tool because either one of these alone loses meaning.

All too often, I’ve witnessed institutional leaders, policy makers and legislators confuse data with real outcomes. The statistics provide an important part of the story, but they are only a representation, and the real story includes so much more. While in graduate school, I took a course at MIT in system dynamics. After we spent a semester building complex models and simulating the behavior of economic systems over time, our instructor included a lecture on “Truth and Beauty.” The simulations, he warned us, may have been beautiful, and they may even have told us a powerful story. However, they shouldn’t be confused with reality—which is far more complex than we’d ever be able to model.

I worry that this kind of confusion is sometimes at the heart of educational decision-making. In numerous recent speaking engagements, United States Department of Education Secretary Margaret Spellings has said “In Texas we like to joke ‘In God we trust. All others, bring data.’” As it turns out, that phrase can be attributed to the late W. Edwards Deming, father of Total Quality Management (TQM) and continuous improvement—a pioneer in scientific management. W. Edwards Deming has also been reported to say, “Data will provide you with three percent of what you really need to know.”

My experience confirms that, in an age of accountability in education, it’s worth trying to find and record the data. The ability to search for the other 97% “…of what you really need to know,” however, must be part of the academy’s institutional research and effectiveness capacity. I hope to be able to explore these ideas, and translate them into practice, as part of Penn State’s graduate certificate program in Institutional Research.

Origins, Part IV: Inputs versus Outputs, versus Process

As I've learned the ways that the academy is viewed, it seems that a lot if boils down to inputs and outputs--inputs being the "stuff" that makes up a college, from buildings to curriculum to faculty and support staff; outputs being student success rates, research, etc.

It appears to me that in this age of accountabilty in education that there is some confusion about which of these things we ought to be paying most attention to. What really matters about a college? If you look at what the accreditors have looked at for many years, you'd probably say that what matters are the inputs. Accreditors have traditionally looked primarily at things like how well the curriculum is structured, how governance works, how many volumes there are in the library, how much faculty and administrators are paid, and other things related to the composition of the institution.

More and more, however, we're starting to see a trend toward looking more closely at the outputs. Especially for public institutions, there is an increasing pressure to track and report student success and other outcome measures. Public and private institutions alike are finding that both regional and national/professional accreditors are putting more and more outcomes assessment into their accreditation standards.

I think that both of these views on what makes for a good institution come up short. For me, it feels that we need to understand that the sustainable success of an institution lies neither in its inputs or its outcomes. Instead, it is the capacity of the institution to really understand the connection between its inputs and outputs that matters, in my view. An institution that's doing poorly on outcomes needs to be able to assess why it's doing poorly--which of its strategies or tactics (inputs) are acting as barriers to success? An institution that's doing well needs to know how to sustain success by understanding which inputs are bolstering it.

In either situation, an institution has to have the capacity to understand that over time there are likely to be shifts in what sustains or blocks success, and they need good planning capacity to lay out evolving strategies and tactics.

Maybe this capacity--the ability to assess and to plan--is itself an input. But I think that we should look at the institutional research and assessment capacity as the third leg of a tripod that includes inputs, outcomes and this process of analyzing and planning.

Origins, Part III: Demonstrating Quality in Distance Learning

Part of what's been going on in my professional life that has led me to this interest in and exploration of Institutional Research and Effectiveness has been an increasing pressure (to some extent self-imposed) to demonstrate quality and effectiveness of distance learning initiatives. Since online teaching and learning is still so new, there seems to be quite a lot of scrutiny. Questions about whether online courses are effective as traditional classroom learning abound.

As a manager of distance learning initiatives, I've struggled to find ways to demonstrate the quality and effectiveness of these programs. I know they're high quality, and I know that the faculty and students involved in them generally find them even more effective than their traditional courses. But how to demonstrate this to the skeptics?

It seems that one direction this has led folks in the field is toward creating frameworks for quality measurement in distance learning. It's interesting to me that rather than doing extensive studies and data gathering about teaching and learning outcomes we've instead decided to create models that help us evaluate the quality of the inputs to distance learning courses. But then, it seems that the history of assessment in higher education is about this balance between evaluating inputs and looking at the outcomes.

More on this in Origins: Part IV.

Origins, Part II: Why "Truth and Beauty"?

Why call this blog "Truth and Beauty"?

When I was pursuing my Masters at the Harvard Graduate School of Education, I cross-registered for a course in System Dynamics at MIT's Sloan School of Management. I had worked with Peter Senge while at PBS and was intrigued by his idea of systems thinking. I thought that the course would help me better understand this concept and the whole idea of organizational learning.

It did indeed give me insights into Senge's ideas. What I didn't know, though, when registering for the course, was that it was going to be highly technical. We would use specialized software (STELLA and iThink) to build complex business models and run simulations of these models to observe how they behaved over time. There was a lot of equation writing and playing with different data inputs. We were tweaking the models to see what it would take to get them to behave in just the way we wanted them to. It was a lot of fun, and an incredible learning experience.

Toward the end of the semester, our instructor included a lecture on "Truth and Beauty." The models that we'd labored over, he warned, were not to be confused with the truth. They may have been beautiful, telling interesting stories and even giving us some insight into decision-making. There were not, however, reality--something far more complex than could ever be modeled.

I'm fascinated by this paradox about data and decision-making. We often treat data as gospel. But usually there are more questions than answers, when we really look close. So are the data pointing us to the truth, or just beautiful possibilities? Or are these the same?

I guess this is what Keats meant when he said "'Beauty is truth, truth beauty,' - that is all ye know on earth, and all ye need to know." (Ode on a Grecian Urn).

All ye need to know, indeed. There's way more I gotta know, and I'm going to keep looking.

Origins, Part I: Why a Blog for Effectiveness?

Recently, a confluence of events in my professional life has led me to explore the worlds of educational assessment and institutional effectiveness. I'll describe more about these events in follow-up "Origins..." posts.

Basically, though, I'm hoping this blog can become a resource for folks working in postsecondary education—with responsibility for or just an interest in institutional research/effectiveness, policy analysis, planning, and evaluation/assessment. There are a wide variety of resources out there on these topics, but I haven't yet come across a commentary-type editorial resource. This is not to say I'm the first to be blogging or providing commentary on activities in this world—only that I haven't yet discovered these kinds of resources.

As my disclaimer under "About This Blog" says, I am not by any means an expert in the field. I'm just an academic technology administrator with a great deal of interest in institutional research. Seems like these days, it behooves us all in the field of education to have such an interest... I hope that my naive perspective on all this can be a helpful resource.

I've simply been doing a lot of thinking about the related issues lately (for reasons upon which I'll soon elaborate), and I thought it might be best to do some of this thinking out loud and share it with others.

Welcome to "Truth and Beauty." I hope you find it interesting and engaging.