The Long View

In "On Design," I gave three versions of designing for an outcome: soft, forward, and inverse, in increasing degree of difficulty. The question "how difficult is inverse design?" is of utmost importance when we consider complex systems. As a real example, consider how the government of the United States is "designed." By this I mean, the way laws and policies are created and enforced. Because of conflicting goals of different constituents and the inherent difficulty of the project, there is no complete empirical language to describe a state of affairs, let alone do a forward simulation to see the status of the nation in, say, three years. And even if we did have such a language, we would be limited to simulation and prediction of only the "easiest" parameters. And even these would subject to the whims of entropy, a subject I'll take up later.

I submit that individual governments, as well as companies, universities, and militaries use soft design with bits and pieces of forward and inverse design thrown in (for example trying to forecast near term economic conditions to help determine monetary policy).

Government in the general sense has been "designed" by the process of being tossed into the blender of fate, to be tested by real events. See the following video of the history of Europe to see what I mean.

Natural selection would seem to be be at work here, weeding out the worst designs. But it's not Darwinian  because the countries change rapidly over time with the population and minds of leaders. One truly spectacular bad idea (like invading Russia, apparently) can bring down a whole nation. So what we are left with is a very temporary list of "least bad designs." Of course, many other factors are important, such as geography, natural resources, and so on. Even so, the people who live there still have to make use of such advantages. If Switzerland abandoned its natural mountain fortress and invaded Russia, it likely wouldn't end well.

Darwinian evolution is different from this national evolution. In the former, good solutions can be remembered and reused through genes or any other information passed from generation to generation. Diversity is created through recombination, mutation, population isolation, and so on. Darwinian evolution comes with an empirical language that we partly understand. To make a metaphor of it, "programs" are written in phenotypes and these "are computed by" the laws of physics and chemistry using the design and environment as "inputs." The fact that scientists can discover this language and use it to make predictions should be appreciated for the miracle that it is: we are witnesses to a dynamic but understandable problem-solving machine of enormous scope that has worked spectacularly well at producing viable designs with only an empirical language. Evolution does not use predictive techniques (that is anticipating that a critter will need wings and therefore building them--for a dramatic example of this, see this video). But there do exist creatures who do use forward and inverse design to plan their day. If you throw a ball at target, you're predicting. If you go to the fridge to get food, you're using the inverse technique: starting with the outcome (get food) and working back to the solution. But this still isn't good enough.

Here's the rub: forward design isn't enough to guarantee any more than short term outcomes, and our ability to do inverse thinking is very limited. Let me pose a problem:

What present actions will lead to [insert subject]  existing in a healthy state 100 years from now? 1000 years from now? 10,000 years from now?

The question is posed as an inverse design problem, starting with the goal and asking what needs to be done to achieve that goal. If we had a good language with which to describe the states, we could at least imagine an evolutionary approach, shown in the diagram below, with the red dot being the desired goal.

We're asking "where do we need to be NOW in order to end up where we want to be AFTER?" The forward design approach is to simulate lots of "befores" and see where then end up "after," and choose the best solution we can find. If we are lucky, we can use a Darwinian approach, combining partially successful solutions or tweaking "near misses" to home in on the best solution we can find. This depends on the sort of problem we're trying to solve, and specifically whether or not it is continuous in the right way. Sometimes being close isn't any good--those "a miss is as good as a mile" problem. All of this highlights the importance of the empirical language, which must have rules precise and reliable enough to allow this kind of prediction and analysis. Clearly in the case of governments, companies, universities, or even our own selves, this is not possible.

With only forward design techniques and lacking a good empirical language, we can still solve the problem with massive brute force: by actually creating a host of alternatives and seeing what happens in real time. A computer game company could do this, for example. Rather than spending a lot of money to find the bugs in its game, it could just begin selling it with the knowledge that the game will be reproduced on many different systems and display many kinds of problems. With this data in hand, it can begin to debug. This seems to be a real strategy. This sort of solution obviously won't work for a government, although in a democracy we have a non-parallel version: swapping out one set of leadership for another routinely, to see what works best (in theory at least).

Paying it Backward. What would it look like if we had all the tools to solve the inverse problem? We could pose it precisely, work forward simulations like the one pictured above, and we would have a huge advantage, shown in the graphic below.

Again, the red dot is where we've decided we want to be after a while. The inverse solver shows us all of our possible starting places in the now. There are multiple ones if we have not completely specified the eventual outcome, which tells us what opportunities we have now to optimize other things than the one we were thinking about when we posed the problem.

Example: An artillery officer is given the task of attacking a distant building. The locations of his guns and the target are fixed. Using ballistics equations we can work backwards to show all the possible solutions: e.g., a high arcing shell like a mortar, or a flat trajectory. This decision will determine how much powder is used.

The inverse solver illuminates "free" parameters and allows us to customize our solution.

Conclusions. For the real-world messy problems we face in complex human organizations, it's fair to say that we are not very good at long-term planning. In some cases it may be impossible because the forward simulators simply don't exist--there are no reliable cause and effect mechanisms. Trying to predict the stock market might fall into that category. In other areas, short-term goals are given preference over long term goals. This is reasonable for at least two reasons. First, the longer out the prediction is, the more likely it is wrong. Second, as individual humans our ability to affect events is a narrow window (e.g. a term for a legislator), and most of us want positive feedback now, not 1000 years from now.

As a very real example of our collective difficulty with long-term planning, consider the issue of human-caused climate change. With the terminology given in these two posts, it's easy to dissect the arguments:

• Empirical language. The basic facts about temperature change and CO2 levels are accepted by the scientific community, but still debated as a political matter. 
• Forward design: Cause and effects are challenged in the political discourse, computer simulations are therefore called into question. Unaccounted-for causes are conjured to explain away data that is (to some extent) agreed on.
• Inverse design: Prescriptions about what to do now to affect the future climate are attacked as being too detrimental to the present, and/or useless.

The question of what happens to the climate is scientific--this is the arena where we are best at design. If we can't understand and act on such threats intelligently, it's very hard to make the argument that we have any long-term planning ability collectively. Note that I'm not neutral on this particular question. The evidence is overwhelming that the risk to our decedents is very high. But go read the experts at RealClimate.org. 

Higher Education. This isn't a climate change blog; what does this analysis have to do with your day to day job in the academy? Everything. From the oracle at Delphi: "Know Thyself." What functional aspects of your institution are understood? How many are understood well enough to make predictions? How many are understood well enough to make inverse predictions?

We work backwards all the time. Suppose budgetary concerns have pushed up freshman enrollment targets, so the admissions office is tasked with bringing in 1000 new students for the fall. This is posed as an inverse problem, and with the standard empirical language of admissions we can build basic predictors. This is the "admissions funnel" prospects->applicants->accepted->enrolled (this is the simplified version). We usually have historical data on the conversion rates between these stages. If the universe is kind, we can use Darwinian methods--keep what works, assuming what works last time will work again, tweak to see if we can make it better. If there's a significant discontinuity (suppose state grants suddenly dried up, or we have a new competitor) the old solutions may not work anymore.

We can and should work hard to create an empirical language and use it to simulate our futures, trying to end up in a good one. This isn't enough.

Short term optimization leads to long-term optimization sometimes, but not as a rule. Think of it as a maze you're trying to escape. If at every junction you choose a path that takes you closer to the opposite wall of the maze, you may make smart moves in the short term only to discover there's no exit there: a long-term failure.

(original image by Tiger Pixel ) 

Even though we can't solve the inverse problem entirely, we may find that we have enough empirical vocabulary to make some important decisions. What do we want the demographics of the student body to look like in 10 years? Answering this question about the future puts constraints on how we operate now, even if they are fuzzy and inexact. Forget about solving the problem exactly--that's impossible--and think about what constraints are imposed in the big picture. Do we want a strong research program, or a large endowment, a national reputation for X? More idealistically, what do we want to be able to say about our alumni in a decade or two? How much does their success matter, and what sort of success are we talking about? Work backwards. I'll finish with an example.

Example: Student Success. If we focus on a goal for our ultimate accomplishment: the education of students, what does this reveal? How exactly do we want our students to benefit from their education? Some possibilities:

• Happiness with life
• Being good citizens
• Being successful financially
• Being loyal to their alma mater
• Getting a job right out of school

The overwhelming narrative in the public discourse is that we want students to be "globally competitive" and "get well-paying jobs." But that's a means to some end. What is the ultimate aim? On a national scale, the answer might be better national security and a stronger economy. For an institution, the answer might be that we want our products strongly identified with our brand, or that we want them to donate lots of money in the annual campaign. I think it's important to start there and ask "why do we care?" You could follow that up with lots of activities, like telling the students themselves why you care, if that's appropriate.  

Suppose that we care because we want the institution to be able to rely on an international body of alumni who will contribute back in money, connections, expertise, and other intangibles. This will enable the university to grow by presenting global options that may not be apparent now, but also establish an exponentially-growing revenue stream through an expanding endowment as successful alumni give large gifts back to the institution. Working backwards, we might identify several tracks to success:

• The state department track--prepare students for high-level international government positions
• The military track--ditto for the miltary
• The global entrepreneur track--help them achieve independence
• The big corporate track--give students the skills to compete and succeed within vast multi-nationals
• The wildcard track--for those students who don't fit the mold, are intelligent and creative, but don't want to be entrepreneurs or work in a cubical. This could include scientists, philosophers, and artists of all stripes.

If we keep working backwards, even without exact solutions, we can make some good guesses as to the curriculum each track needs, and the type of faculty mentors we need. This neatly sidesteps the drift toward vocational education that the public narrative implies, and gives the institution a raison d'être. There's nothing wrong with telling students "we want you to succeed so that you'll help us succeed." This sort of pseudo-altruism is what keeps the population going, after all. Thanks, mom and dad.

Think Backwards. Short term forward planning is like beer: it's obviously a good idea at the time, but watch out for the hangover.

Suggestions for an Outcomes Assessment Strategy

Introduction
This outline is based on my own idiosyncratic view of assessment as a practitioner. Some of it clashes with what you will read elsewhere. Please read this as ideas to consider, not a comprehensive plan for success. It’s important to get other perspectives and begin to network with others in the business. The ASSESS-L listserv and conferences like the IUPUI Assessment Institute and the Atlantic Assessment Conference are good places to start. The SACS annual meeting is good, but can be frightening because of the panic pheromones in the air. Don’t miss the roundtable discussions at the conferences—get there very early because the assessment tables fill up first. There are also many books on the subject. [Note: edited 9/12/10]

I. Initialization of the Plan

• The president and academic vice president have to visibly be behind the project. Describe to the board what you are planning to do. Just as important is that the executives trust the implementation team to do the job, and not be tempted to micromanage.
• What to expect:
• Successful SACS accreditation
• Better faculty engagement with teaching, leading to improvements
• Cultural change that incorporates language of your stated outcomes (includes administration, faculty, students, prospective students if you want)
• Understandable reports that include what was learned from observations and what actions were taken because of it.
• A long slow evolutionary process of improvement.
• What not to expect:
• Learning measurements that can be used for determining faculty or program performance.
• One-off solution. It requires sustained attention, like growing an orchid. It’s just as finicky. Celebrate every blossom and nurse the brown bits.
• Unequivocal proof of learning achievement
• A “microwaved” solution to learning outcomes assessment. The garden analogy is a good one: this stage is about picking the right seeds and location.
  
  
• Gain the faculty’s trust. Without them all is lost.
• Form a small group of some faculty who are receptive or already doing assessment. Include at least one open-minded skeptic to avoid group-think.
• The leader can be a faculty member with release time or a full-time admin, but this person should teach at least one class a year. It has to be someone the faculty respects, and someone who’s not yet sold on assessment may be a good bet.
• Make sure each program has a program coordinator, per SACS, and consider making each of these responsible for making sure assessment gets done. Or maybe it’s department chairs, but someone has to be responsible for activities at the program level. I’ll just assume it’s coordinators for simplicity.

• Figure out how you’re going to organize documentation. An institutional archive is a wonderful thing. Librarians can be of great help here.
• Keep reports organized, at a minimum by year and program.
• Accumulate and save as much original student work as you can. A dropbox works wonders. More complex are ePortfolios and learning management systems. There’s nothing like authentic work to analyze when you need it. Note that the student work is much more valuable when associated with the assignment, so figure out how to save these too. This can be an IT/ library project.
  
  
• Coordinate with your SACS liaison and other accreditation processes. Make sure timelines and requirements are covered. Set some milestones.
  
  
• Consider re-visiting how faculty teaching evaluation is done. Linking assessment directly to evaluation is a bad idea (because there is incentive to cheat), but letting the faculty themselves use assessment results and improvements in their presentations for merit can be powerful. In other words, bottom-up rather than top-down use will work best (e.g teaching portfolios).
  
  
• Build a small library of assessment books and links, and read up on what other people do. Read critically, though, and don’t believe that things are as easy or simple as authors sometimes make them out to be.

II. Implementation of the Plan
This is mostly an assignment for your assessment working group, with appropriate input from the VP level (e.g. #2 below).

• You probably already have a list of learning objectives for general education, the institutional mission, programs, etc. Do an inventory of these and organize them. There are different types of goals, including:
• Skills like communication or thinking
• Content: facts, methods, ways of doing things in a discipline
• Non-cognitives: self-evaluation, attitudes, etc.
• Exposure: e.g. “every student will study abroad”
• Beliefs: could be religious or secular, depending on your mission
  
  
• One of the tenants of the TQM, which is what the SACS effectiveness model is based on, is that lower level goals should feed up to higher goals. In practice, trying to make this fit with learning outcomes can be a distraction. It creates a whole bunch of bureaucracy and reporting of dubious value. But you can attempt it if you want; it will just make the rest of the work twice as hard.
  
  
• If you can simplify your goals list by eliminating some, that will help focus the effort. In any event, don’t expect all goals to be evaluated all the time. You can simply “park” some goals for now if you want. If you don’t have many learning outcomes, this is great because you don’t have to deal with an existing mess. Figure out what’s the most important goals to assess at the current time.
  
  
• Help programs develop or reboot their plans:
  
  
• Stay away from “critical thinking” like the plague. Thinking skills are great, but pick ones that faculty can agree on, like (probably) deductive reasoning, inductive reasoning, evaluating information for credibility, and even creativity. If you’re already stuck with critical thinking as a goal, consider defining it as two or three more specific types of thinking that are easier to deal with.
  
  
• Provide structure for programs so that they are parallel. Here’s a sample list:
• Content-related outcomes for the discipline
• Non-cognitive outcomes for the discipline (e.g. confidence)
• General education outcomes as they relate to the major
• A technology-related outcome (to help with the SACS standard)
• Applicable QEP outcomes.
  
  
• Have meetings with programs or related groups of programs to discover their most important goals in the categories from (b) above. This can be a lot of fun, and should immediately be seen as a productive exercise. It takes a little practice for the facilitator to learn how to move the conversation along without getting hung up on technicalities. It will take at least three meetings with a good group to accomplish the following (in order):
• A list of broad goals important to the faculty for students to accomplish. It’s best to start from scratch unless they have a good assessment program already up and running. Get them to tell you what they believe in so you don’t have to convince them later.
• A map showing where these things appear in the present curriculum. (This is where ideas for change will already start appearing—document it.)
• A description of how we know if the students accomplish the goals. This includes assessment and documentation. Avoid temptation to extract promises for assessment to happen all the time, as it won’t happen.
• Develop assessments that are so integral to courses that they are natural, and perhaps already even happening. Assessments should be learning activities if at all possible.
• Share and cross-pollinate ideas across areas, for example by inviting “outsiders” to sit in on some of these sessions. That way you can develop others to run similar sessions. It’s too much for one person to do it all.
  
  
• Develop or find (e.g. from AAC&U) rubrics as it makes sense. Don’t go crazy with rubrics—they can be as harmful as helpful, just like electricity. Rubrics have scales to show accomplishment. It’s essential to get this right. If you can, tie the rubric accomplishment levels to the career of your students. For example, the scale might be “developmental,” “first-year,” “second-year,” “graduate,” for a two-year degree. That would be suitable for writing effectiveness, for example. Faculty find this natural—they have an implicit understanding of what to expect out of students of relative maturity. Wouldn’t you like to know how many of your graduates were still doing pre-college level work when they walked? [Note: you will find lots of people who don’t see it this way, and prefer scales like “underperform” to “overperform.” The problem for me with these is that you often can’t see progress. A good student may always overperform, and yet still progress to a higher level of performance.]
  
  It’s not always suitable to use such a scale, of course. For example, I assess how hard students work (a non-cognitive), and faculty rate on a scale like “minimal” to “very hard.”
  
  
• If your faculty wants to use external standardized tests, make sure that the content on the test matches the curriculum. Especially for small programs, these tests are better for making external reviewers happy that actually being of use. Remember that if it’s not a learning experience, it’s probably a poor assessment. Generally, try to avoid external tests if you can. They are expensive, very hard to admininster, generally not learning experiences, the faculty don’t “own” the content, and the results are often not detailed to know with any precision what to do to make improvements.
  
  
• Make sure everyone knows your plan for keeping documentation, how archiving of reports, student work, assignments, etc. is to be done. Set reasonable expectations, but think about what you will want to have on hand when the next SACS team visits, and (more important) what kinds of information you may want to look at retrospectively in five years. Retrospective analysis is very powerful for finding predictors of success. For example, suppose you use the CLAQWA writing aid one year and then stop. Two years later you might want to regress this on graduation (logistic regression) to see if those students’ success rates are statistically linked to the writing treatment. Okay, this is a little far-fetched, but the truth is that you never know what that original, authentic data will be useful for, so save it and organize it if you can.
  
  
• Avoid big standardized tests of gen ed skills like the aforementioned plague. They will only lead to heartbreak. The only exception to this is if you want to cover your bases for an accreditation report by using one of these. Some reviewers will see this is as a meaningful effort, so it might help with SACS section 3.5.1.
  
  
• Faculty may want you to talk about validity and reliability or other technicalities, generally as an obstruction. Read about this stuff to be acquainted, but don’t let it rule your life. It’s a common mistake to say “the XYZ test is proven valid.” No test is valid. Only propositions can be valid, like “Tatiana’s score of 78 means she can conjugate Spanish verbs with some proficiency.” As such, validity is very much a local concern. If assessments stay close to authentic classroom work, the faculty will believe appropriate statements valid, and faculty belief is at a premium in this venture. Other objections may come in the form of “has assessment been proven to improve learning?” This is very difficult to answer if you take the scientific approach of trying to prove things. Selling an evolutionary “assessment is teaching while paying attention” is easier. For example, you can ask what improvements such-and-such department has made in the last year. These are always going on: curricular change, new labs or experiences, etc. Then work backwards from the change with a line of questioning: why did you make the change? What made you notice there was an opportunity for improvement? What is the ultimate goal?” This is just the assessment/improvement process in reverse. All we’re trying to do is organize and document what already happens naturally, and intentionally use this powerful force for good, which otherwise is more of a random walk. A more serious objection to consider is “where am I going to get the time to do this?” Here, the administration can help in a variety of ways by carving out strategic release time, summer stipends, or elimination of other committees or bureaucracy to give the assessment effort priority. These efforts will help send the message that the administration takes the effort seriously.
  
  
• Don’t try to make the assessments too scientific. If you document part of the learning process (the most authentic assessment), it’s likely to be messy even with rubrics and whatnot. Don’t try to reduce everything to numbers. See the section on reporting for more on that. One very effective way of assessing is to get faculty together to look at raw results and discuss their experiences in this context. What worked? What didn’t? What problems are evident? This is very rich with possibilities for action. Somebody just needs to write down what happens during this meeting and archive it.

III. Development of the Plan

• Once everything is up and running, the assessment group’s most important function is to review and give feedback on plans and results. Without regular feedback and encouragement, professional development opportunities, and recognition, the process will peter out. Set a calendar of events for assessment and reporting.
  
  
• Make opportunities to give visibility to learning goals and results. Senior administrators should talk about it publically, praising successful efforts, describing the big picture, committing support. A magical thing happens when you start referring to your learning outcomes directly. Example: “Across the board we have seen efforts to increase student abilities in writing effectiveness and effective speaking.” By using the vocabulary, it becomes a natural part of the culture and seeps into the way people think. Get the goals written into syllabi so students see it and hear it talked about.
  
  
• Depending on your execution, you may have individual student outcomes reported (e.g with the Faculty Assessment of Core Skills, below). Consider whether or not you want advisors to have access to this information to use with their advisees. For example, I noticed one time that a senior art student was getting ratings of “remedial” in creativity. It’s important that individual instructor ratings not be revealed unless it’s part of the learning experience in a current course. This shields the ratings from political pressures (“why did you give me a remedial score?”).
  
  
• Make non-compliance a matter for administrative enforcement only as a last resort. SACS is a big stick, and you have to use it sometimes, but you don’t want a “because SACS says” culture. The odds are that if you create a process the faculty believe in, the SACS review will be fine. There are never guarantees of anything because of the randomness of peer-review, but this should improve the odds:
  
  
• Make sure there are no majors in non-compliance. Even if there are no students in the major, do something that looks like assessment. I know it’s absurd, but go read the SACS-L listserv.
  
  
• Assessment is only the appetizer. The test for reviewers is 1. is there a regular process that shows at least a couple years’ comprehensive effort, and 2. are there results—actions taken, changes made because of what the results were.
  
  
• Don’t accept “actions” like “we are continuing to monitor” or simply more plans do something. Any real process will produce evolutionary change in the form of curricular proposals, classroom experiences, teaching methods, testing, technology, etc. Extracting the reports that document this is always a battle, both for quantity and quality, but it has to be fought. Think like a SACS reviewer when you look at them. Ask your SACS VP to be a reviewer once you have some confidence.
  
  
• Don’t expect to be able to scientifically show that learning is improving because of changes. There are innumerable tests and data management products, “value-added” statistics, and other nonsense that will only frustrate you if you buy into it. It’s more important that faculty believe that learning is improving, that the changes are (probably) for the better. This is no excuse, of course, not to look at education literature for best practices and studies that do highlight some processes as better than others. Take them with a grain of salt, but try them out.
  
  
• For any goals that are institution-wide, consider using a “Faculty Assessment of Core Skills” approach. You can read more about that at http://www.coker.edu/assessment/elephant.pdf.

IV. Reporting Outcomes

• You can have the IR office help with report statistics if you want, but make sure they know the game plan first, or faculty may get two different messages. Generally with reporting, keep it simple and to the point. Here’s a sample list of headings for the sections:
• Learning Outcome: (a statement of it)
• Assessment Method (a statement of how it’s done with attached forms, if any)
• Results and analysis (what did faculty observe or glean through analysis or focused discussion?)
• Actions and Improvements (what did they do?)
  
  
• Avoid condensing data down more than you have to. Averages are often too abstract. It’s like taking a ripe strawberry and boiling it before you eat it. Presenting averages to an external body like SACS is fine—they expect it. All the better if the graphs go up. But internally, where you want to know meaning, don’t average unless it’s the only logical thing to do.
  
  
• Instead of averages, report out frequencies when you can. For example, rather than saying the average test score was 3.4 this year as opposed to 3.3 last year, say the percentage of students reaching the “acceptable” level went from 60% to 65% or whatever. This is a great technique that will instantly improve the usefulness of reports.
  
  
• Remember that assessment reports can’t be used punish programs or individuals administratively. The reason is simple: the instant you start doing that, every report thereafter will be suspect. The whole system relies on trust to work, and that goes in both directions. Instead, use results administratively as a reason for a conversation, especially around budget time.
  
  
• If you standardized goals by category as suggested earlier, it makes it easy to present the whole mass of reports to SACS in an organized and well-formatted form. Without some structure, you’ll have a pile of disjointed formats and styles that will immediately turn off whoever has to look at them.