Posts tagged ‘economics’
The Washington Post series on “The Tuition is Too Damn High” has been fascinating, filled with interesting data, useful insights, and economic theory that I hadn’t met previously. The article linked below is about “Baumol’s cost disease” which suggests an explanation for why wages might increase when productivity does not. It’s an explanation that some have used to explain the rise in tuition, which Post blogger Dylan Matthews rejects based on the data (in short: faculty salaries aren’t really rising — the increase in tuition is due to other factors).
But I actually had a concern about an earlier stage in his argument. It’s absolutely true that our labor intensive methods do not lead to an increase in productivity in terms of number of students, while MOOCs and similar other methods can. However, we can gain productivity in terms of quality of learning and retention. We absolutely have teaching methods, well-supported with research, that lead to better learning and more retention — we can get students to complete more classes with better understanding. In the end, isn’t THAT what we should be measuring as productivity of an educational enterprise, not “millions of customers served” (even if they don’t complete and don’t learn)?
Performing a string quartet will always require two violinists, a violist and a cellist. You can’t magically produce the same piece with just two people. Higher education, for at least the past few millennia, has seemed to fall in this category as well. “What just happened in my classroom is not very different from what happened in Plato’s academy,” quips Archibald. We’ve gotten better at auditorium-building, perhaps, but lecturers generally haven’t gotten more productive.
I’m interested in the discussions about corporate involvement in higher education, but am still trying to understand all the issues (e.g., who has a bigger stake and greater responsibility for higher education, industry or government). The point made below is one that I have definite opinions about. If we’re trying to improve higher education, why not try to make it more effective rather than just lower cost? I disagree with the below that we have to have 16:1 student:teacher ratios to have effective learning. We can increase those student numbers, with good pedagogy, to still get good learning — if we really do focus on good learning. Why is all the focus on getting rid of the faculty? Reducing the labor costs by simply removing the labor is unlikely to produce a good product.
There is a lot wrong in this apples to oranges comparison, but the point is obvious—cutting labor costs is the path to “education reform,” not research and improved pedagogy. This is “reform” we need to reject when applied to public education. I say this without reservation: when it comes to education, you pay for what is most effective. Period. If small class sizes produce better teaching and learning, then that’s what you support when appropriate. Whatever your approach, stop conflating economic restructuring and education reform; it’s dishonest.
Semester Online sounded like a nice idea — getting liberal arts focused institutions to share their online course offerings. The pushback is interesting and reflects some of the issues that have been raised about sustainability of online education as a replacement for face-to-face learning or even as an additional resource.
While Dr. Lange saw the consortium as expanding the courses available to Duke students, some faculty members worried that the long-term effect might be for the university to offer fewer courses — and hire fewer professors. Others said there had been inadequate consultation with the faculty.
When 2U, the online education platform that would host the classes, announced Semester Online last year, it named 10 participants, including Duke, the University of Rochester, Vanderbilt and Wake Forest — none of which will be offering courses this fall. “Schools had to go through their processes to determine how they were going to participate,” said Chance Patterson, a 2U spokesman, “and some decided to wait or go in another direction.”
My thinking on computing education has been significantly influenced by a podcast about hand-washing and financial illiteracy. I suspect that education is an ineffective strategy for achieving the goal of Computing Literacy for Everyone. I have a greater appreciation for work like Alan Kay’s on STEPS, Andy Ko’s work on tools for end-user programming, and the work on Racket.
On Hand-Washing and Financial Illiteracy
I have been listening to Freakonomics podcasts on long drives. Last month, I listened to “What do hand-washing and financial illiteracy have in common?” I listened to it again over the next few days, and started digging into the literature they cited.
At hospitals, hand-washing is far less common than our knowledge of germ theory says it ought to be. What’s most surprising is that doctors, the ones with the most education in the hospital, are the least likely to wash their hands often enough. The podcast describes how one hospital was able to improve their hand-washing rates through other behavioral methods, like shaming those who didn’t wash their hands and providing evidence that their hands were likely to be filled with bacteria. More education doesn’t necessarily lead to behavioral change.
Much more important was the segment on financial illiteracy. First, they present the work of Annamuria Lusardia who has directly measured the amazing financial illiteracy in our country. There is evidence that much of the Great Recession was caused by poor financial decisions by individuals. Less than a third of the over-50-year-old Americans that Lusardia studied could correctly answer the question, “If you put $100 in a savings account with 2% annual interest, at the end of five years you will have (a) less than $102, (b) exactly $102, or (c) more than $102?” More mathematics background did lead to more success on her questions, but she calls for a much more concerted effort in financial education. Her arguments are supported by some pretty influential officials, like Fed Reserve Chair Ben Bernanke and former Secretary of the Treasury Paul O’Neill. It makes sense: If people lack knowledge, we should teach them.
Lauren Willis strongly disagrees, and she’s got the data to back up her argument. She has a 2008 paper with the shocking title, Against Financial Literacy Education that I highly recommend. She presents evidence that financial literacy education has not worked — not that it couldn’t work, but it isn’t working. She cited several studies that showed negative effects of financial education. For example, high school students who participated in the Jump$start program become much more confident about their ability to make financial decisions, and yet made worse decisions than those students who did not participate in the program.
The problem is that financial decisions are just too complicated, and education (especially universal education) is expensive to do well (though Willis doesn’t offer an estimated cost). Educational curricula (even if tested successful) is not always implemented well. The gap between education in teen years and making decisions in your 40’s and 50’s is huge. Instead of education, we should try to prevent damage from ignorance. Willis suggests that we should create a cadre professional of financial advisors and make them available to everyone (for some “pro bono”), and that we should increase regulation of financial markets so that there are fewer riskier investments for the general public. It costs the entire society enormously when large numbers of people make poor financial decisions, and it’s even more expensive to provide enough education to prevent the cost of all that ignorance.
This was a radical idea for me. Education is not free, and sometimes it’s cheaper to prevent the damage of ignorance than correcting the ignorance.
Implications for Computing Literacy Education
I share the vision of Andy DiSessa and others of computing as a kind of literacy, and a goal of “Computing for All” where everyone has the knowledge and facility to build programs (for modeling, simulations, data analyses, etc.) for their needs. Let’s call that a goal of “Universal Computing Literacy,” and we can consider the costs of using education to reach that goal, e.g., “Universal Computing Education to achieve Universal Computing Literacy.”
The challenge of computing literacy may be even greater than the challenge of financial literacy. People know even less about computing than they do about finance. We don’t know the costs of that ignorance, but we do know that it has been difficult and expensive to provide enough education to correct that ignorance.
Computing may be even more complicated than finance. Willis talks about the myriad terms that people need to know to make good financial decisions (like “adjustable rate mortgages”), but they are at least compounds of English words! I attended a student talk this week, where terms like “D3” and “GreaseMonkey” were bandied about like they were common knowledge. We invent so much language all the time.
The problem is that education is often inefficient and ineffective. Jeremy Roschelle pointed out that education improvements rarely impact economic outputs. Greg Wilson shared a great paper with me in response to some tweets I sent about these ideas. Americans have always turned to education to solve a wide variety of ills, but surprisingly, without much evidence of efficacy. We can teach kids all about healthy eating, but we still have a lot of obesity. Smokers often know lots of details about how bad smoking is for them. Education does not guarantee a change of behavior. This doesn’t mean that education could not be made more effective and more efficient. But it might be even more expensive to fix education than to deal with ignorance.
Universal education is always going to be expensive, and some things are worth it. Text illiteracy and innumeracy are very expensive for our society. We need to address those, and we’re not doing a great job at that yet. Computing education to achieve real literacy is just not as important.
I am no longer convinced that providing computing education to everyone is going to be effective to reach the goal of making everyone computing literate, and I am quite convinced that it will be very expensive. Requiring computing education for STEM professionals at undergraduate level may still be cost-effective, because those are the professionals most likely to see the value of computing in their careers, which reduces the costs and makes the education more likely to be effective.
Barb sees a benefit in Universal Computing Education, but not to achieve Universal Computing Literacy. We need to make computing education available everywhere for broadening participation in computing. To get computing into every school, Barb argues that we have to make it required for everyone. Without the requirement, schools won’t go to the effort of including it. Without a requirement, female and URM students who might not see themselves in computing, would never even give it a chance. In response to my argument about cost, she argues that the computing education for everyone doesn’t have to be effective. We don’t have to achieve lifelong literacy for everyone. Merely, it has to give everyone exposure, to give everyone the opportunity to discover a love for computing. Those that find that love will educate themselves and/or will pursue more educational opportunities later. I heard Mike Eisenberg give a talk once many years ago, where he said something that still sticks with me: that the point of school is to give everyone the opportunity to find out what they’re passionate about. For that reason, we have to give everyone the chance to discover computing, and requiring it may be the only way to reach that goal.
It’s always possible that we’ll figure out to educate more effectively at lower cost. For example, integrating computing literacy education into mathematics and science classes may be cheaper — students will be using it in context, teachers in STEM are better prepared to learn and teach computing, and we may improve mathematics and science teaching along the way. My argument about being too expensive is based on what we know now how to do. Economic arguments are often changed by improved science (see Malthus).
As Willis suggests for financial literacy, we in computing literacy are probably going to be more successful for less cost by focusing on the demand side of the equation. We need to make computing easier, and make tools and languages that are more accessible, as Alan Kay, Andy Ko, and the Racket folks are doing. We have to figure out how to change computing so that it’s possible to learn and use it over an entire career, without a PhD in Computer Science. We have to figure out how to get these tools into use so that students see use of such tools as authentic and not a “toy.”
“Computing for All” is an important goal. “Access to Computing Education for All” is critical. “Universal Computing Education to achieve Universal Computing Literacy” is likely to be ineffective and will be very expensive. On the other hand, requiring computing education may be the only way to broaden participation in computing.
It nice to see someone with a background in management making this argument, that the costs of MOOCs may be greater than the benefits.
Give-away pricing in education, Mr. Cusumano warns, may well be a comparable misstep. The damage would occur, he writes in the article, “if increasing numbers of universities and colleges joined the free online education movement and set a new threshold price for the industry — zero — which becomes commonly accepted and difficult to undo.”
In our conversation, I offered the obvious counterargument. Why should education necessarily be immune from this digital, Darwinian wave, when other industries are not? Isn’t this just further evidence of the march of disruptive progress that ultimately benefits society?
Mr. Cusumano has heard this reasoning before, and he is unconvinced. In the article, he explains, “I am mostly concerned about second- and third-tier universities and colleges, and community colleges, many of which play critical roles for education and economic development in their local regions and communities.”
“In education,” Mr. Cusumano adds, “‘free’ in the long run may actually reduce variety and opportunities for learning as well as lessen our stocks of knowledge.”
How much does it cost the American economy that most American workers are not computer literate? How much would be saved if all students were taught computer science?
These questions occurred to me when trying to explain why we need ubiquitous computing education. I am not an economist, so I do not know how to measure the costs of lost productivity. I imagine that the methods would be similar to those used in measuring the Productivity Paradox.
We do have evidence that there are costs associated with people not understanding computing:
- Erika Poole documented participants failing at simple tasks (like editing Wikipedia pages) because they didn’t understand basic computing ideas like IP addresses. Her participants gave up on tasks and rebooted their computer, because they were afraid that someone would record their IP address. How much time is lost because users take action out of ignorance of basic computing concepts?
We typically argue for “Computing for All” as part of a jobs argument. That’s what Code.org is arguing, when they talk about the huge gap between those who are majoring in computing and the vast number of jobs that need people who know computing. It’s part of the Computing in the Core argument, too. It’s a good argument, and a strong case, but it’s missing a bigger issue. Everyday people need computing knowledge, even if they are not professional software developers. What is the cost for not having that knowledge?
Consider this a call to economics researchers: How do we measure the lost productivity from computing illiteracy?
This class sounds cool and similar to our “Computational Freakonomics” course, but at the data analysis stage rather than the statistics stage. I found that Allen Downey has taught another, also similar course “Think Stats” which dives into the algorithms behind the statistics. It’s an interesting set of classes that focus on relevance and introducing computing through a real-world data context.
The most unique feature of our class is that every assignment (after the first, which introduces Python basics) uses real-world data: DNA files straight out of a sequencer, measurements of ocean characteristics (salinity, chemical concentrations) and plankton biodiversity, social networking connections and messages, election returns, economic reports, etc. Whereas many classes explain that programming will be useful in the real world or give simplistic problems with a flavor of scientific analysis, we are not aware of other classes taught from a computer science perspective that use real-world datasets. (But, perhaps such exist; we would be happy to learn about them.)