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Jan 28, 2019

Innovation and the ego

In education, both K-12 and higher, too many innovations are driven by leaders’ egos, and not by the needs of their respective organizations. How it works is obvious: to jump to the next career level, or to maintain good graces of one’s employer, one needs to show something new. On a larger scale, any institution want to project a public image of innovation, not something stable and stale. The public is not interested in funding something that is good but boring; educators need a story to captivate the public collective imagination. In America, and much of the world, the idea of progress and innovation has firmly taken root. The pressure is really coming from outside, and it percolates through the system, from presidents to department chairs – everyone wants to distinguish oneself as an innovator, with a line on one’s resume to prove it.

This would not be bad, if we collectively did not spend significant resources on ego-driven innovations. The cost could be direct, or indirect, but it can be significant. Among other things, ego-innovations contribute to the so-called “mission creep” phenomenon, where a university adds more and more projects, structures, units to its core mission, depleting the resources, and muddying the latter. Time and resources spent on frivolous innovations are not spent on university’s core mission – improving its programs and ensuring student success.

Examples are abound. The relatively recent craze for MOOCs prompted many most prestigious universities to join either Coursera or EdX, and pour many millions of dollars in developing these free public resources. They were supposed to revolutionize higher education, but didn’t. I have seen cases where massive restructurings, reshuffling faculty and programs into different configurations were sold as innovation. Those can be very costly in terms of time, but people are rarely more productive in new administrative structures. Neither they ever cut the actual cost. Unfortunately, most of educational technologies have been a flop. While computer-connected projectors were probably worth it (although they are just a tiny step up from transparencies), none of the smart boards and similar interactive technologies have been very effective. The various data analytics software platforms are still an open question. I keep my mind open, but right now would not bet a $100 that they will significantly affect academic outcomes.

William Baumol identified one of the reason for so many flops in 1967: All economic activities can be divided “into two types: technologically progressive activities in which innovations, capital accumulation, and economies of large scale all make for a cumulative rise in output per man hour and activities which, by their very nature, permit only sporadic increases in productivity.” [i] In other words, education is much more relational than it is informational. We do not yet have any significant breakthroughs in terms of relational technologies, and it is not clear whether they are possible or desirable. Education is not like manufacturing or transportation; its core activity remains heavily manual, and not automatable.

Innovation is good when it answers to clearly identified organizational challenges, and has reasonable chances of success. Online teaching is not cheaper than its f2f equivalent, but it allowed to significantly expand the student population by reaching out to remote, less mobile, and working populations. That is an economic as well as technological innovation that is still not fully realized. Universities and schools have a lot to gain from reducing transactional costs by digitizing their organizational transactions. Things like purchasing, hiring, student registration, troubleshooting, petitions, advising, etc. – those represent a great innovation field that has been barely touched. It is not something sexy you can sell to the public, but simplifying and automating the bureaucratic routines still has a large potential for cost reduction and making everyone a little happier. Many innovations are possible in designing programs that reflect today’s and tomorrow’s labor landscape.

Innovation in education is a risky game, which is why it cannot be played by leaders’ egos. We need a very critical approach to expensive innovations.

[i] William J. Baumol, "Macroeconomics of unbalanced growth: the anatomy of urban crisis." The American economic review 57, no. 3 (1967): 415-416.

Jan 22, 2019

What happens in two years

It has been two years since I came to Sacramento. An experienced nomad, I can tell you what normally happens at about two years mark. The honeymoon is over, and my colleagues can see my weaknesses as clearly as I can see theirs, and we learn to work around each other’s edges. In general, we only get to know someone when we can see one’s shortcomings. Some people feel disappointment when they discover that so and so is imperfect. I feel relief – humanity is disclosed through weakness, not through strengths. A person starts being an enigma when she or he becomes relatable, and we can relate to those who do not wear an impenetrable shell of perfection. Vulnerability is really the only way to connection.

I start to get inside jokes. Relationships need shared history, which becomes a set of references to be used not just for humor, but also for rich reasoning. If you are considering doing B, you can say, “Oh, well, I don’t want it to become another A,” and everyone involved will understand what that means. This is not only shorthand, but hopefully allows to make fewer mistakes. And yes, one needs about two years to build that common vocabulary and a common set of references.

Every organizations can be imagined as a complicated maze, with some hard walls that constitute its limits and boundaries, and a number of openings that allows things to be done. It takes at least a year or two to figure it out, mostly by trial and error. There is no user manual for Sac State, because 90% of rules are not written down. People just know – these things are easy, these things are hard, but possible, and these are completely impossible. It also takes a couple of years to figure it out” one year for basic outlines of the maze, two or more for the ability to find passages.

I am here to report that all those things did happen, I am happy here, and feeling energetic and productive. We have so much more to do. Thanks to all for being so kind, so talented, and so passionate about our mission.

Jan 14, 2019

The Sidorkin’s Law: Why is institutional data always wrong?

Institutional data has come a long way. I still remember the hard copies of IR annual books; they were the only way to get any kind of stats about your programs. They were also mostly wrong, by the way. Now we have many sophisticated real-time data portals, showing you everything you need to know about your organization. I used to think that certain number of errors in the reporting was just a temporary thing, something we will eventually overcome. However, I do not believe this anymore. The errors are built in, and they are not cause by data technology. The errors come from human-data interaction.

There are at least two different causes of the intrinsic errors. One has to do with Campbell’s Law, an under-appreciated phenomenon; I wrote about it a few years ago. If consequences of a quantitative indicator are high, the measurement tends to corrupt the very practice it intends to measure. For example, if in my College FTEF/FTES ratio looks high, I will be thinking of ways to record both indicators to make us look better. Trust me there are ways.

However, there is something Campbell did not know, because in 1979, there were no user-input databases at every organization. Let us call this the Sidorkin’s Law: Categorical scarcity leads to work-arounds that will corrupt data input. When designing a database, one cannot use a very large number of fields/categories. That would make the database unwieldy and unmanageable. It would be impossible to produce a useable report. The whole point of database is simplification, standardization of information. If you want to compare , say, two colleges, you have to make a call: let’s call student teaching something similar to nurses’ clinical practice, for example “supervised field experiences.” One can see how they are similar, but they are also vastly different. The need to measure and compare brings about emphasizing the similarities and ignoring the differences. However, life is always just a bit more complicated than any set of categories, and people learn to operate within the limits presented by the database designers. Considerations of convenience force us enter data in the fields and categories that were not initially intended.

For example, one of our program areas has struck a deal several years ago that when they teach larger classes, they can get an extra workload credit. However, the workload database did not have a specific category to reflect such an arrangement. Moreover, there was initially a rule that synchronized student credit hours with faculty workload, a very sensible one, by the way. Therefore, someone came up with a work-around: We record these units as Discipline-Based Research. No one remembers what it was intended for, but it was a convenient way to solve the data input problem. As a result, our overall instructional workload units looks smaller than they actually are. This is just one small example; I can name a dozen or so work-around fixes like this. Cumulatively, they can significantly affect the reports that come out in the end. This is why I always prefer to see the raw data, understand the sources and the formulas used in a report, and never trust pre-packaged reports.

On top of these two data corrupting influences, there is a whole thing about what the report data means. It deserves a special blog one day. For example, 4-year graduation rates can be dramatically different among various programs. Does it mean that one is doing a better job than the other is? Obviously not: programs that are more selective will have higher graduation rates, no matter how hard you work on student success.

There are many threats to data reliability, and even more to their pragmatic validity. We cannot afford to ignore the data, because it is better to see your world through some distorted funhouse mirrors than see nothing at all. It just helps to remember the mirrors are not that accurate, not because of imperfection, but by their very nature.

Jan 7, 2019

The lecture trap

Sometimes university instructors, especially the novices, get into the lecture trap. They somehow assume that lecturing is the “real” teaching, and the rest of the stuff is supplemental. Why do they assume so is easy to explain: First, this is how they were taught in college and even in graduate school, and second, how else do you cover so much material? It feels good to tell something to students. The magic act of telling transfers responsibility from the instructor to the students – I told you, and if you still don’t know, it is no longer my fault. If you have read the same thing, and failed to learn, it feels more like mine. Regardless of the reasoning, such instructors invest enormous amount of time and creativity into designing the right kinds of power point slides, and devote a lot of class time lecturing.

The problem with that – the genre is becoming increasingly difficult. There is a very small minority of instructors who do lectures really well. It comes naturally to them; they are quick, funny, charming, entertaining, can read the audience very well. These people can collect and release attention, they can build an almost magical rhythm, where fascinating problems are interrupted by jokes, and striking visuals. And then there is the rest of us. You have to be good enough to compete with thousands of professionally produced lectures put out there by the hundreds of top universities with huge budgets and professional film production assistance. Just look through the free MOOCs on either Coursera or EdX, or just browse YouTube. Can you beat that? Again, BOTH charisma and resources can make a lecture sing. Let us admit, we are not that well-resourced, and not that entertaining. Students are becoming more and more discerning, because the great lecturers are available to them at a click of a button. They will doze off at your merely adequate lectures, and blame you for it; not entirely without a cause.

It is much easier to engage students with other types of in-class activities. Consider games, simulations, quests, demos, structured discussions, short written exercises, case studies, skits, brainstorming sessions, problem analysis, debates, etc. I know what you are afraid of – that these are high-engagement, but low information-density activities. In other words, you are afraid the kids will get entertained, but learn very little. Well, you are a researcher, so do an experiment – lecture one section, and use methods that are more active in another, and show me material difference in either knowledge or skills. I bet you won’t find much, or your actively engaged students will do better. Of course, you still will have students read, and find a way to engage their readings. In addition, find those great lectures you cannot do yourself online, and ask them to watch those at home. You can even watch them in class together, where you can answer questions, and explain the difficult parts.

Yes, of course, one can screw up all those other methods, too. They all require preparation, and careful tuning from semester to semester. However, they are much harder to screw up and require less or the same prep time as lectures. Think of a semester-long course as a series of activities, where students do something in class every time. Insert mini-lecturers: what is about to happen, and what you all will learn or practice today. Then again, here is what you just learned, and a couple of things you still seem to be unsure about. Those will go a long way in establishing your expert authority – actually more than a long lecture which you are likely to mess up somewhere.

I don’t want to extol the virtues of hands-on instruction. I am just saying it is much less risky, and just as effective, or more effective than lectures. Students will still need you – your expertise and your wisdom, in shorter, more focused doses that is.