Posts tagged ‘economics’
The below-linked article by Jill Lepore is remarkable for its careful dissection of Christensen’s theory of “disruptive innovation.” (Thanks to Shriram Krishnamurthi for the link.) As Lepore points out, Christensen’s theories were referenced often by those promoting MOOCs. I know I was told many times (vehemently, ferociously) that my emphasis on learning, retention, diversity was old-fashioned, and that disrupting the university was important for its own sake, for the sake of innovation. As Lepore says in the quote below, there may be good arguments for MOOCs, but Christensen’s argument from a historical perspective just doesn’t work. (Ian Bogost shared this other critical analysis of Christensen’s theory.)
I just finished reading Michael Lewis’s The Big Short, and I see similarities between how Lepore describes reactions to Christensen’s theory of “disruptive innovation” and how Lewis describes the market around synthetic subprime mortgage bond-backed financial instruments. There’s a lot of groupthink going on (and the Wikipedia description is worth reading), with the party line saying, “This is all so great! This is a great way to get rich! We can’t imagine being wrong!” What Lewis points out (most often through the words of Dr. Michael Burry) is that markets work when there is a logic to them and real value underneath. Building financial instruments on top of loans that would never be repaid is ludicrous — it’s literally value-less. Lepore is saying something similar — innovation for its own sake is not necessarily valuable or a path to success, and companies that don’t disruptively innovate can still be valuable and successful.
I don’t know enough to critique either Lewis or Lepore, but I do see how the lesson of value over groupthink applies to higher-education. Moving education onto MOOCs just to be disruptive isn’t valuable. We can choose what value proposition for education we want to promote. If we’re choosing that we want to value reaching students who don’t normally get access higher education, that’s a reasonable goal — but if we’re not reaching that goal via MOOCs (as all the evidence suggests), then MOOCs offer no value. If we’re choosing that we want students to learn more, or to improve retention, or to get networking opportunities with fellow students (future leaders), or to provide remedial help to students without good preparation, those are all good value propositions, but MOOCs help with none of them.
Both Lewis and Lepore are telling us that Universities will only succeed if they are providing value. MOOCs can only disrupt them if they can provide that value better. No matter what the groupthink says, we should promote those models for higher-education that we can argue (logically and with evidence) support our value proposition.
In “The Innovative University,” written with Henry J. Eyring, who used to work at the Monitor Group, a consulting firm co-founded by Michael Porter, Christensen subjected Harvard, a college founded by seventeenth-century theocrats, to his case-study analysis. “Studying the university’s history,” Christensen and Eyring wrote, “will allow us to move beyond the forlorn language of crisis to hopeful and practical strategies for success.” … That doesn’t mean good arguments can’t be made for online education. But there’s nothing factually persuasive in this account of its historical urgency and even inevitability, which relies on a method well outside anything resembling plausible historical analysis.
According to the article linked below, there is a large effort to fill STEM worker jobs in Northern Virginia by getting kids interested in STEM (including computing) from 3rd grade on. The evidence for this need is that there will be 50K new jobs in the region between 2013 and 2018.
The third graders are 8 years old. If they can be effective STEM workers right out of high school, there’s another 10 years to wait before they can enter the workforce — 2024. If they need undergrad, 2028. If they need advanced degrees, early 2030’s. Is it even possible to predict workforce needs out over a decade?
Now, let’s consider the cost of keeping that pipeline going, just in terms of CS. Even in Northern Virginia, only about 12% of high schools offer CS today. So, we need a fourfold increase in CS teachers — but that’s just high school. The article says that we want these kids supported in CS from 3rd grade on. Most middle schools have no CS teachers. Few elementary schools do. We’re going to have to hire and train a LOT of teachers to fulfill that promise.
Making a jobs argument for teaching 3rd graders CS doesn’t make sense.
The demand is only projected to grow greater. The Washington area is poised to add 50,000 net new STEM jobs between 2013 and 2018, according to projections by Stephen S. Fuller, the director of the Center for Regional Analysis at George Mason University. And Fuller said that STEM jobs are crucial in that they typically pay about twice as much as the average job in the Washington area and they generate significantly more economic value.
It is against this backdrop that SySTEMic Solutions is working to build a pipeline of STEM workers for the state of Virginia, starting with elementary school children and working to keep them consistently interested in the subject matter until they finish school and enter the workforce.
I recently watched the documentary Why we fight, and was struck by the prescience of President Eisenhower’s warning. So many of our educational decisions are made because of the harsh economic realities of today. How many of these are guns-for-butter choices might we have made differently if education was considered? Here in Georgia, computer science curricular decisions are being made with a recognition that there will be little or no funding available for teacher professional development — certainly not enough for every high school CS teacher in the state. What percentage of the DoD budget would it cost to provide professional learning opportunities to every CS teacher in the country? It’s certainly in the single digits.
Every gun that is made, every warship launched, every rocket fired signifies, in the final sense, a theft from those who hunger and are not fed, those who are cold and are not clothed.
This world in arms in not spending money alone.
It is spending the sweat of its laborers, the genius of its scientists, the hopes of its children.
The cost of one modern heavy bomber is this: a modern brick school in more than 30 cities.
It is two electric power plants, each serving a town of 60,000 population.
It is two fine, fully equipped hospitals.
It is some 50 miles of concrete highway.
We pay for a single fighter with a half million bushels of wheat.
We pay for a single destroyer with new homes that could have housed more than 8,000 people.
This, I repeat, is the best way of life to be found on the road the world has been taking.
This is not a way of life at all, in any true sense. Under the cloud of threatening war, it is humanity hanging from a cross of iron.
via Cross of Iron Speech.
Wall Street Journal just ran an article (linked below) about people “flocking to coding classes.” The lead for the story (quoted below) is a common story, but concerning. If coding is all extra-curricular, with the (presumably expensive) once-a-week tutor, then how do the average kids get access? How do the middle and lower kids get access? Hadi Partovi and Jane Margolis talked about this on PRI’s Science Friday — CS education can’t be an afterschool activity, or we’ll keep making CS a privileged activity for white boys.
Like many 10-year-olds, Nick Wald takes private lessons. His once-a-week tutor isn’t helping him with piano scales or Spanish conjugations, but teaching him how to code.
“I always liked to get apps from the app store, and I always wanted to figure out how they worked and how I could develop it like that,” Nick says.
As the ability to code, or use programming languages to build sites and apps, becomes more in demand, technical skills are no longer just for IT professionals. Children as young as 7 can take online classes in Scratch programming, while 20-somethings are filling up coding boot camps that promise to make them marketable in the tech sector. Businesses such as American Express Co. AXP -0.57% send senior executives to programs about data and computational design not so they can build websites, but so they can better manage the employees who do.
SIGCSE Preview: Measuring Demographics and Performance in Computer Science Education at a Nationwide Scale Using AP CS Data
Barbara and I are speaking Thursday 3:45-5 (with Neil Brown on his Blackbox work) in Hanover DE on our AP CS analysis paper (also previewed at a GVU Brown Bag). The full paper is available here: http://bit.ly/SIGCSE14-APCS This is a different story than the AP CS 2013 analysis that Barbara has been getting such press for. This is a bit deeper analysis on the 2006-2012 results.
Here are a couple of the figures that I think are interesting. What’s fitting into these histograms are states, and it’s the same number of bins in each histogram, so that one can compare across.
Fitting this story into the six page SIGCSE format was really tough. I wanted to make the figures bigger, and I wanted to tell more stories about the regressions we explored. I focused on the path from state wealth to exam-takers because I hadn’t seen that story in CS Ed previously (though everyone would predict that it was there), but there’s a lot more to tell about these data.
Figure 1: Histograms describing (a) the number of schools passing the audit over the population (measured in 10K), (b) number of exam-takers over the population, and (c) percentage of exam-takers who passed.
Measuring Demographics and Performance in Computer Science Education at a Nationwide Scale Using AP CS Data
Abstract: Before we can reform or improve computing education, we need to know the current state. Data on computing education are difficult to come by, since it’s not tracked in US public education systems. Most of our data are survey-based or interview-based, or are limited to a region. By using a large and nationwide quantitative data source, we can gain new insights into who is participating in computing education, where the greatest need is, and what factors explain variance between states. We used data from the Advanced Placement Computer Science A (AP CS A) exam to get a detailed view of demographics of who is taking the exam across the United States and in each state, and how they are performing on the exam. We use economic and census data to develop a more detailed view of one slice (at the end of secondary school and before university) of computer science education nationwide. We find that minority group involvement is low in AP CS A, but the variance between states in terms of exam-takers is driven by minority group involvement. We find that wealth in a state has a significant impact on exam-taking.
We’ve heard about this problem before: Online courses don’t reach the low-income students who most need them, because they don’t have access to the technology on-ramp. This was an issue in the San Jose State experiment.
That’s because the technology required for online courses isn’t always easily accessible or affordable for these students. Although the course may be cheaper than classroom-based courses, the Campaign for the Future of Higher Education argues in a report released Wednesday low-income students might still have a harder time accessing it.
“We have to wrap our heads around the fact that we can’t make assumptions that this will be so simple because everyone will just fire up their computers and do the work,” says Lillian Taiz, a professor at California State University, Los Angeles, and president of the California Faculty Association.
Many students, Taiz says, don’t have computers at home, high-speed Internet access, smart phones, or other technologies necessary to access course content.
The US News article suggests Google Chromebooks as an answer — cheap and effective. The Indian government is trying an even cheaper tablet solution. Could you use one of these to access MOOCs?
The Indian government realized a few years ago that the technology industry had no motivation to cater to the needs of the poor. With low cost devices, the volume of shipments would surely increase, but margins would erode to the point that it wasn’t worthwhile for the big players. So, India decided to design its own low-cost computer. In July 2010, the government unveiled the prototype of a $35 handheld touch-screen tablet and offered to buy 100,000 units from any vendor that would manufacture them at this price. It promised to have these to market within a year and then purchase millions more for students.
An interesting experiment, with a deeply disturbing result.
The poor often behave in less capable ways, which can further perpetuate poverty. We hypothesize that poverty directly impedes cognitive function and present two studies that test this hypothesis. First, we experimentally induced thoughts about finances and found that this reduces cognitive performance among poor but not in well-off participants. Second, we examined the cognitive function of farmers over the planting cycle. We found that the same farmer shows diminished cognitive performance before harvest, when poor, as compared with after harvest, when rich. This cannot be explained by differences in time available, nutrition, or work effort. Nor can it be explained with stress: Although farmers do show more stress before harvest, that does not account for diminished cognitive performance. Instead, it appears that poverty itself reduces cognitive capacity. We suggest that this is because poverty-related concerns consume mental resources, leaving less for other tasks. These data provide a previously unexamined perspective and help explain a spectrum of behaviors among the poor. We discuss some implications for poverty policy.