Space travelers encountering new species should not necessarily think that stealing a dictionary and plying an informant with single-malt scotch will automatically produce a Rosetta stone.

The author is David Dailey. He’s a professor of computer science at Slippery Rock University in Pennsylvania. The article is Why is SVG going to be REALLY BIG?. Dailey presented the paper at the SVG Open 2009 conference held from October 2-4 in Mountain View, California.

Here’s a taste of the main message:

In short, the fundamental metaphor of SVG for the expression of human meaning is closer to the realm in which meaning exists (at least in some abstract mathematical sense). SVG has not suffered from the setbacks that millenia of bad assumptions about how to communicate thoughts have saddled onto speech, text, hypertext and, hence, HTML.

Those of you with philosophical inclinations will likely be engaged by the article. All of us can take heart from the hope that HTML5 and SVG may open new opportunities for deeper understanding of complex policy issues.

Instead of unemployment, I chose to examine whether family income influences students’ choices about enrollment in public versus private colleges and universities in the United States. Given the substantial gap in net price between public and private institutions, you would think that family finances might play an important role in the public vs private decision.

One caveat as always. Throughout these use cases, I’ve been less interested in the research question than in how xquery performs. I chose research questions that I thought would be interesting, but I was more concerned about the tool than the actual research.

In this post I won’t give away the story-line about the research. For that, please refer to the 4-page PDF that describes the project.

However, I will say once again that I enjoyed working with xquery. There are several implementations of the W3C standards. I used zorba.

This project did place stress on xquery performance at times, but I was able to concoct workarounds that I’ll be able to use in similar situations if they occur in the future.

Also, I’d like to highlight once again the very intricate nature of data analysis. I’m occasionally uncomfortable in this age of transparency and big data and semantic linked data that ease-of-use is the assumed norm and expectation. It has never been my experience that data analysis is easy or fast. It requires living in the data for considerable periods of time. It requires thought and play and more thought. It requires failure and more failure and finally small steps of success. I hope this will be evident in the PDF that describes this project.

I also tried again to provide links to all important programs I wrote as part of this project. It’s not exactly what some call open research, but it certainly is a step in that direction. If anyone feels motivated to duplicate what I did or, better yet, to modify and improve upon what I did, then I think you’ll find the source links useful. And I’d be happy to assist if you have questions.

With the conclusion of this project, I no longer feel the need to equivocate about xquery. I will definitely use it.

July 2009 came and went without a new annual report, but I still think the exercise is important. So I’m now calling these intermittent documents Status Reports and am issuing one today on the autumnal equinox.

Regular readers know the context for my work in this blog. But others may not, so I’ll restate the core reason I do what I do. It’s a simple but audacious idea. I want everyone everywhere to have unlimited and free cradle-to-grave learning. In short form, I usually just say free learning for everyone everywhere.

Ridiculous, isn’t it? It means changing the world.

Over this past year, I’ve solidified three areas where I think I can make a contribution.
1. Systems modeling, research, and theory. I don’t subscribe to a mechanistic and deterministic view of change. Rather I feel much more comfortable with a probabilistic and uncertain perspective. This means that I do not believe there are throttle points that can be adjusted to affect change. It means instead that I pay attention to constructs of larger systems (eg, economic, educational) to help identify possible pressure points, which then require lots of experimentation, failure, and learning before something promising unfolds. This experimentation occurs in research labs and in entrepreneurial markets.

This is probably too abstract. Here’s an example. Presently in the United States, price is a pressure point in higher education. For reasons that we don’t need to go into here, many colleges and universities in the U.S. have chosen to follow strategies that leave them vulnerable to lower-priced competitors. There are now a range of these competitors, from the well-established private for-profits to the considerable number of evolving online alternatives. Most of the latter will fail, but nonetheless it is creative experimentation.

It’s important to take a mile-high systems perspective, even if that perspective is necessarily dim, and to pay attention to pressure points and the experimental efforts trying to exploit pressure points. The knowledge gained from this attention might tell us quite a lot about system perturbation and change.

2. Systems feedback loops, reporting, and analysis. It’s common to find feedback loops in complex systems. For example, information flows feature prominently in negative loops used to monitor and keep system performance within certain acceptable ranges. Information can also affect positive feedback loops that may eventually drive systems to chaotic states or to collapse.

Under a variety of terms like transparency, openness, and Gov 2.0, we now have a remarkable opportunity to broaden and affect the information feedback loops evident in complex systems like education. We can even create new loops that did not exist previously.

It is easy to view higher education in the United States as a mess that’s beyond hope. I’ve certainly made contributions to this rhetoric. But if existing institutions voluntarily change, then we may make a softer transition into an uncertain future.

This is the reasoning behind the work I’m doing with XQuery, data API’s, and the rest of the technical stuff that creeps into this blog. I’m headed toward analysis and reporting that I hope will plug into existing or new feedback loops and thereby encourage change in higher education.

There is just a colossal opportunity here. But because it is so difficult to move the impervious, benefits relative to effort are not favorable. Still, it’s an effort I choose to make as a way of acknowledging debts to the past and the good that remains in higher education.

3. System design and development. I believe we’re tantalizingly close to being able to design and develop viable new components in education-cum-learning systems. Eventually these components will dazzle us with their obviousness. But right now they’re the provenance of dreamers, researchers, and entrepreneurs.

I made a previous contribution in this area with Imagining Tomorrow’s University, more as a demonstration of technique than a serious proposal. I’ve also continued to play with designs that may one day provide the basis for market-based experiments. And with some luck I should be able to share these notions in the next year. I have no intention of actually doing the development work, but I can contribute design ideas and early prototypes.

There are also many other people working in this area and making important contributions. It’s incredibly invigorating.

So … that’s basically where I am and where I’m headed in the near future. Three areas of complementary but distinct work.

Here’s a sample graph from the report. It shows the annual percentage change in enrollments from all U.S. degree-granting institutions of higher education from 1968 to 2007. The grayscale background shows unemployment rates and periods of recessions. The report is available in this pdf that includes better resolution graphics.

Here is an abbreviated version of the summary section from the report.

1. The new XQuery use cases included screen scraping enrollment data embedded in html, and the pipelining of enrollment and economic data through several staged transformations.
2. The evidence that higher education in the United States is countercyclical appears weak based on the exploratory analysis done here.
3. The evidence varies somewhat by institutional control and type, but mostly the general observation of a weak association between enrollment change and recession cycles holds true.
4. The tools used in this project, XQuery and R, are wonderful research tools but are not suitable for more general use without wrappers that mute their complexity.
5. The screen-scraping technique used in this project provides another access route to a vast amount of U.S. Department of Education data on the web.
6. None of the data sources used in this project included semantic or linked data markup. Whether XQuery can be used successfully with RDFa or microformats seems worthy of investigation.

Here are links to various pieces of this project:
a. Final report
b. Documentation for XQuery source programs
c. XQuery to generate enrollment data
d. XQuery to generate unemployment and recession data
e. XQuery to merge enrollment, unemployment, and recession data
f. R analysis history

I remain quite satisfied with XQuery.

I’m now ready to start work on another XQuery data integration, this time using the new FRED API from the Federal Reserve Bank of St. Louis. Not that I’m particularly interested in banking-related data, but FRED uses a REST web service architecture and will allow me to play more  thoroughly with Zorba’s REST capability. And the volume of the data will allow me to stress test the performance of Zorba’s XQuery.

The siren’s song, of course, is that the web may one day become a giant database of interrelated data.

I’ve now finished the XQuery/FRED project. You can see the details here (pdf), but I’ll touch on a few of the points.

The FRED database includes more than 20,000 economic time series. The folks at the Economic Research Division at the Federal Reserve Bank of St. Louis have done a nice job making this data available via an API. With so much data to choose from, however, it helps if you know at the outset what kind of data you’d like to retrieve. I didn’t.

So I set myself two tasks: 1) Build the XML documents to provide flexible overviews of the 20,000 time series; and 2) Choose two of these times series and conduct a small analysis.

I did the first job using Zorba XQuery and the second job using XQuery and the R statistics package. Again, the details are here (pdf).

Basically I came away feeling like I’d made some progress. The Zorba REST calls to the FRED API worked flawlessly. Some of the XQuery programs with joins to multiple XML just chugged away slowly, but then I  was using a creaky old server to run Zorba and serve up the XML files. Otherwise it was a wonderful opportunity to learn more about XQuery as a web query tool. I continue to like what I saw.

Since that’s a pretty positive statement for me, perhaps this is the place to say that I have no relationship with Zorba or any of the other XQuery implementations. I’m simply looking for a tool that will utilize some of my strengths in pursuit of the main reason I write this blog … which, if you don’t yet know, is to help nudge us closer to the day when learning will be free for everyone everywhere.

Next I plan to look at Zorba in a variety of other settings. Options include the XQuery in the Browser project (XQIB), XQuery scripting and Sausilito to construct web sites, and some serious performance tests of  XQuery  on EC2 and S3 (Amazon Web Services).

Below is a screen shot of the R analysis. It shows a not unexpected relationship between the unemployment rate in the United States over the last 55 years and the rate of change in outstanding household credit market debt.  The current recession seems somewhat atypical, however. Details and a legible graphic are also in the PDF, as is souce code for the XQuery programs I wrote.

A year ago I believed that higher education in the United States seemed ripe for a discontinuous jolt and that new technologies seemed a likely driver of that change.

I now find these beliefs no less compelling, but see them in a broader context of educational and economic change, technological and social innovation, human learning and intentionality, institutional incentives and constraints, and the public good.

To consolidate my thinking and to chronicle a year of learning, I tried to explain to myself what I think I know now that I didn’t know a year ago.

I call it an annual report and it’s located here (pdf). It’s mercifully brief in hopes that it will actually be read.

I encourage and welcome comments. I’m aware that there is more than a whiff of superficiality to some statements, but view that mostly as learning to crawl before I can walk. Any help would be most welcome.

Here I’m interested in two questions. What are the revenue and expense models in use by colleges and universities? And are there combinations of revenue and expense models that indicate some institutions are following uniquely different business strategies compared to their competitors?

The study includes a diverse group of 1,144 private not-for-profit institutions.

Findings suggest that colleges and universities do differentiate themselves markedly by the business models they employ. This further suggests that institutions also differ in their ability to adapt to such disruptive changes as low-cost competition. No real surprises here, but the details provide tantalizing stories.

The report is available here (pdf).

Earlier this month I decided to see how people in diverse content areas used the term institutional change. I started with economics and quickly uncovered what’s called the new institutional economics. My intention was to cast a wide net across multiple disciplines, but that will now have to wait for another time. I never proceeded past the niche where I started.

In December 1993, Douglass C. North shared the Nobel Prize in Economics for his work in institutional economics. His Nobel Lecture on “Economic Performance through Time” is wonderfully accessible to non-economists. When I read the lecture, I was struck by three things. First, North saw institutional change as clearly central to understanding how economies evolve over time. Second, North saw learning as clearly central to understanding how institutions change (e.g., “The most fundamental long run source of change is learning by individuals and entrepreneurs of organizations.”) And, third, North’s language was so explicit it seemed likely that a model could be constructed.

It was a wonderful find. Here was a Nobel laureate writing in a literate style about institutional change and learning, two things that most occupy my thinking now. So I prepared a concept map based on North’s 1993 Nobel Lecture. In doing so, I used Cmap Tools from the Institute of Human and Machine Cognition (IMHC). Cmap Tools is free for educational institutions, employees of the U.S. Federal government, and individuals using it for non-commercial purposes (download here).

You can access my concept map using either Cmap Tools or a browser. Cmap Tools provides various ways to interact with the map that are not possible with a browser (e.g., pop-ups with quotes from North when the cursor hovers over a concept). But you can also use a browser and view the quotes in this PDF. Links are listed below for the various ways to access the concept map.

I’m not going to describe North’s model in detail here. Read his lecture and view the concept map instead. Rather, I’ll concentrate on only a few notions that focus on the following questions: Does North’s model provide guidance on the location and nature of interventions most likely to affect how institutions and an economy change? In particular, what role does education and learning play?

North views institutions as a humanly devised matrix of formal and informal constraints and their enforcement mechanisms. Examples of formal constraints include laws and regulation; examples of informal constraints include belief structures and accepted norms of behavior. This matrix forms the “rules of the game” that is then contested by players (individuals, entrepreneurs, and organizations). They do so under conditions of uncertainty and with limited knowledge of what other players are doing. North’s view is based ultimately on the assumption that scarcity is the central economic problem, that competition derives from scarcity, and that markets provide one mechanism for distribution of scarce resources (although the markets are not efficient in the manner that neo-classical economists would assume).

Assumptions, of course, are always pivotal and open to critique and challenge. I don’t intend to go that route. I’d rather play with North’s model by granting him the assumptions. Model-building is always an iterative process and assumptions need not be sacrosanct if the model misses the mark.

I like North’s definition of institutions and their web-like fabric as incentives in a game. I also like the recursive nature of the game being played. Change happens because players of the game experiment with existing rules, finding opportunities, and chafing at constraints. As the players learn, their mental models about what is possible change and eventually their beliefs about what <em>should</em> be possible also change. Coincident are changes in the rules themselves. So you get this very rich and complex interaction of players learning and rules morphing or being created. The image doesn’t lend itself to easy understanding about where or how to intervene in the process. But it does ring true, that change is something always in the process of becoming, that economic or social or educational reality is anything but real, but rather is a work of art in constant creation.

North bases his learning concepts on cognitive science. This is one area where he seems on thin ice. I suspect that those working in the edublogging world today, with its theories of connective and networked learning, could add immeasurably to this part of the model.

At the time North delivered his lecture, the concept of technology largely existed external (i.e., exogenous) to his model. In 1994 North and John J. Wallis wrote an article that tried to integrate technology and institutional economics (Journal of Institutional and Theoretical Economics, 150/4). I’ve not yet read that article, nor have I read North’s 2005 book Understanding the Process of Economic Change. But clearly, in 1993 North seemed headed toward an integration of demography and technology into the model.

In building the concept map, the take-home lesson for me was the creative miasma consisting of players experimenting, reinventing, and constructing rules of the game just by looking in new ways at what exists and what is possible. It’s something that all of us can do. But I’d argue, as I’ve done elsewhere, that instantiating possibilities requires the hard glare of market survival – even though future organizational forms may be considerably different than today. As North might say, we need to play the game today for things to change tomorrow.

Originally posted 01-April-2008 by Gary M. Lewis on http://garymlewis.typepad.com/educational_imaginations.

Permalink: http://garymlewis.com/instchg/2008/04/28/institutional-change-and-learning/

Links

1. If you have Cmap Tools installed and opened on a computer, access the concept map from the Views – Cmap Tools window by migrating to Shared Cmaps in Places > IHMC Public Cmaps (3) > Users > garymlewis > Economic Change (North) and opening the map called North.

2. Otherwise you can access several browser versions of the concept map that vary by the level of detail. I recommend that you look at all three in order from least detail to most detail, and then use the most detailed map to coordinate with North’s quotes in the PDF. Use the following links in the order listed:

Least Detailed Map

Detailed Map

Most Detailed Map

My concern is with higher education, although frankly I no longer see higher education as distinct from education at other levels. For me all education has been subsumed into something I simply call learning. But for the purposes of making this posting more manageable, I’ll confine it to higher education … and to higher education in the United States.

Where, then, could we look to find organizations with nascent strategies that call to question the business of higher education? Research institutes generate many innovative ideas, but I’m looking for something farther along than incubation. I want to find organizations that are, for right or wrong, being judged by markets. It doesn’t matter to me how wonderful the idea or concept. If it can’t survive in the market, then it doesn’t have the potential for significant institutional and social change that I seek.

In the United States currently, we have an unprecedented situation where many private colleges and universities and even some public ones have priced themselves precariously. My guess is that the most innovative efforts to redefine higher education will occur in the cauldron of low-cost competitors. Many low-cost institutions will merely choose to skim profits made available by the exposure of higher-priced colleges and universities. But low-cost alternatives could actually be a major factor in rethinking what we do in higher education.

I’m now reading a very fun book written by Carlota Perez, titled Technological Revolutions and Financial Capital: The Dynamics of Bubbles and Golden Ages. In the book, Perez examines five technological revolutions that occurred in the last two hundred years. In each case, she found that the “socio-institutional framework” lagged by several decades the changes in technology and the economy, but that the full potential of the changes was not realized until social and institutional changes also occurred. The techno-economic transformations that unfold bring “a major shift in the relative price structure that guides economic agents toward the intensive use of the more powerful new inputs and technologies,” and this seems to ripple through the socio-institutional spheres and find expression.

It’s easy to argue that the full potential of current information technologies will not be reached until industries like higher education recreate themselves, presumably along dimensions consistent with the lower cost advantages of the new technologies and their economic consonances.

With that as background, I set about trying to locate colleges and universities with innovative business models that include a low-cost strategy. Here is the pdf that describes this preliminary research (figures are on pages 1 and 2, discussion is on page 3). The work describes summary findings from 3,899 U.S. private colleges and universities using publicly available data from the National Center for Education Statistics (NCES) and its Integrated Postsecondary Education Data Systems (IPEDS). The research uses instruction-related expenses per FTE during the 2004-05 academic year as a means of identifying low-cost institutions.

As shown in the document, there are low-cost outliers (77 total) among the eight categories of institutions considered in this research. Most, perhaps all, of the low-cost institutions will prove on further examination to be uninteresting. Certainly a substantial number will be one-year anomalies (e.g., data reporting problems) and can easily be dismissed.

But I’m only looking for the one or two examples that are not anomalies and not profit-skimmers, but rather are re-imagining higher education. I do have to say, however, that with this study I feel a bit like the man searching on the ground under a light pole, at night, for a lost item. When asked where he lost the item, he replied “over there.” When asked why, then, he was looking over here, he said “the light is better here.” Certainly the light is better with the IPEDS universe of existing colleges and universities.

It may very well be that searching the murky fringes of the existing world of higher education will uncover the energy, creativity, innovations, and attention to social impact that I seek. But for a start, I’m content to look under the lamp. I’ll let you know how it turns out.

Originally posted 25-February-2008 by Gary M. Lewis on http://garymlewis.typepad.com/educational_imaginations.

Permalink: http://garymlewis.com/instchg/2008/04/25/looking-under-light-poles/

Just to recap briefly in case you missed the drama or, like me, initially yawned it off as merely another illustration of the inanity of college pricing in the United States. On December 10^th, Harvard announced it had revised its financial aid policies with three new features designed to make Harvard more affordable for middle- and upper-middle-income families (see here). The three new initiatives are:

• Harvard will no longer consider home equity when determining a student’s need for financial assistance.
• Loans will no longer be a component in the mix of aid offered to students.
• And, most notably, the price of a Harvard education will be capped at 10% of family income for “families with incomes above $120,000 and below $180,000 and with assets typical for these income levels.” At present, the expected family contribution is zero for families with incomes of $60,000 or less. This policy will remain intact. Families with incomes between $60,000 and $120,000 will also benefit from a cap, but on a sliding scale between 0% and 10%.

According to Harvard, the impact of these changes on pricing will be substantial. Families affected by the new home equity policy should expect to see the price of a Harvard education decline by an average of $4,000. And the new zero to 10% standard should reduce Harvard’s cost by one-third to one-half. For example, Harvard estimates that a typical family with income of $180,000 would pay about $18,000 compared to more than $30,000 under the current awarding policy.

I’m not going to try to summarize the reactions to this announcement, because I want to move from this point in a different direction. But the reactions were entertaining, to say the least, and included: consternation that $180,000 could be considered upper-middle-income; concern that Harvard’s shift would increase the degree of family haggling over pricing at other institutions; claims that Harvard had masked, in an altruistic cloak, the awarding of merit financial aid to students whose financial need did not warrant it; and assertions that Harvard had taken preemptive action to thwart attempts now in Congress that would require non-profit colleges and universities, as a condition for maintaining their tax-exempt status, to annually distribute a minimum percentage of endowment to operations.

If you’d like to follow these reactions, here is a cross-section of responses (New York Times, Boston Globe 1, Boston Globe 2, and Inside Higher Education).

As I read these stories, I started to wonder just how much impact a $35 billion endowment could have on pricing. The answer is that Harvard could, if it so chose, redefine college pricing … which is probably a fearsome thought for presidents of colleges and universities who compete with Harvard for students and yet do not enjoy multi-billion dollar endowments.

That is a conclusion I reached after spending several days mired in the intricacies of Harvard’s Financial Report for Fiscal Year 2007 (here, pdf). I don’t mind telling you, this was not easy. The audited financial report includes 22 pages of notes that provide detail to the 4 pages of standard statements. Harvard is a complex institution and its financial statements reflect that fact.

I believe I have accurately interpreted the financial information, but there undoubtedly are nuances that I’ve missed. So you should probably view the story I’m about to describe as true but not exact. If anyone would like to pursue this summary in greater detail, I’ve also included page locations in Harvard’s financial report. The dollar amounts in my story are expressed in millions. So $3,247m is actually $3,247,000,000. And please note that we’re speaking about the whole university here and not just the undergraduate college, for example.

Harvard’s fiscal year runs from July 1 to June 30, so the financial statements I reviewed refer to the year from July 1, 2006 to June 30, 2007. During that time, revenues used for operations came from five sources (p16). These were student income (e.g., tuition, room, board); sponsored research (e.g., external funding from the federal government for research); gifts given for current use; investment income, including endowment income distributed for operations; and other operating income (e.g., rentals, royalties, publications, etc). About 20% of the $3,211m total operating revenues came from net student income (i.e., student sources after removing scholarship aid to students). A similar percentage (20%) came from sponsored research. Investment income provided about 38% of total operating revenue, while gifts for current use and other operating income contributed 7% and 15% respectively.

This, of course, is a revenue distribution that most private non-profit colleges and universities can only envy. A mere 20% of Harvard’s annual operating revenue is dependent on the vagaries of the student marketplace. Tuition-dependent institutions are much more the norm in the U.S.

As I read the reaction around the country to Harvard’s announcement, I kept returning to the same question. What would it take for Harvard to become totally independent of student income? Could Harvard afford to offer all students a free education?

The answer is yes. And the degree of financial pain it would inflict on Harvard is minimal. Here’s the rest of the story.

In fiscal year 2007, the largest single category of Harvard’s operating revenue came from endowment income ($1,044m or about one-third of total operating revenue; p16). Stated somewhat different, Harvard distributed over a billion dollars in one year from endowment to general operations. So we’re not talking small potatoes here.

But by distributing another $658m from endowment to operations (p16), Harvard could have eliminated the need for any student income. That is, no student in undergraduate, graduate, continuing education, or executive education study would have needed to pay tuition, room, board, or fees. Their education would have been free.

If solely based on financial considerations, Harvard could increase its endowment payout to operations by the $658m with minimal impact. Each fall, the Harvard Corporation approves an endowment distribution rate (p30). This rate is stated in an obscure way as dollars per unit of market value at the beginning of the fiscal year. For fiscal year 2007, the endowment distribution set by the Corporation was 4.3%. Meaning that about 4% of the market value of Harvard’s endowment at the start of the fiscal year could be available for operating revenue. The market value of the endowment was $29,219m on June 30, 2006 (p17).

As I mentioned previously, Harvard actually distributed $1,044m of endowment income for operations. This amount is 3.6% of the market value of the endowment at the start of the fiscal year. By increasing the endowment distribution to 5.8% (i.e., another $658m), Harvard could have provided a free education to its students during fiscal year 2007.

The value 5.8% is significantly greater than the target identified by the Corporation, but it is also near the range planned for the future. As stated in the financial report, “in an effort to accelerate progress on priority initiatives, the University recently increased its targeted aggregate spending rate range to between 5.0% and 5.5% of the endowment’s market value annually” (p5). The use of the word “aggregate” refers not just to the payout for operational use but also includes what Harvard calls a “strategic payout” to provide funding for key programs and goals.

Clearly the provision of free education is not one of Harvard’s goals, but the fact that the Corporation would consider a range of 5.0% to 5.5% means that finances alone would not prohibit Harvard from redefining its pricing structure to achieve institutional goals.

There’s no doubt that an additional $658m is a considerable amount of money, but it also needs to be put in context. The market value of Harvard’s endowment increased by $5.7 billion in fiscal year 2007 (from $29.2 billion to $34.9 billion; pp16-17). Endowment would still have been $34.2 billion after covering all income from student sources with income from endowment.

Harvard enjoys huge latitude for future price movement. Of course, some of this latitude is constrained by factors other than finances, so I don’t think we’ll see free and Harvard mentioned together any time soon. That much is clear.

But just as clearly, if college costs ever truly become a national concern, Harvard does not provide a healthy model for the rest of U.S. higher education to follow. The path to less costly higher education does not lie through multi-billion dollar endowments. Rather, it depends on educating students differently.

Originally posted 02-Jan-2008 by Gary M. Lewis on http://garymlewis.typepad.com/educational_imaginations.

Permalink: http://garymlewis.com/instchg/2008/04/24/college-pricing-and-harvard/