The Top 200 World Universities of 2009
October 9, 2009 | Leave a Comment
Top 10 or 100 lists of universities or colleges are the type of thing people in higher education love to hate and hate to love. Times Higher Education from the U.K. recently published its list of the Top 200 World Universities for 2009. Of course there is no shortage of American universities on the list and the usual cadre of big names are all comfortably near the top.
What always strikes me about these things is how they always vary from year to year, yet I wonder how much has changed at the institutions themselves during this time. Take the top 20 – most of which are from the U.S. and U.K. – is there really a major difference in the quality of the education between them? You have to wonder how much effort has been expended at some of these institutions in order to move one or two places on the list – just to edge out their rival.
Another striking aspect of these lists is how some institutions seem to swing wildly up or down by as many as 20 or 30 places in a given year. I have a hard time considering what changed at such an institution in so little time to warrant such a movement in ranking. In fact, I’d argue that the pace of change in higher education simply is not fast enough for the results of any change that may have been implemented to be felt in that short of a time.
To the extent that such lists spur both institutions and people to strive for excellence, than I suppose they are a good thing. However, I know quite a few institutions that do not make it onto such lists that nonetheless provide students with a myriad of opportunity and high-quality educations – including community colleges.
The top 20 of the Top 200 World Universities:
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| 1 | 1 | Harvard University | US | 100 | 100 | 98 | 100 | 85 | 78 | 100.0 |
| 2 | 3 | University of Cambridge | UK | 100 | 100 | 100 | 89 | 98 | 96 | 99.6 |
| 3 | 2 | Yale University | US | 100 | 99 | 100 | 94 | 85 | 77 | 99.1 |
| 4 | 7 | University College London | UK | 98 | 99 | 100 | 90 | 96 | 99 | 99.0 |
| 5= | 6 | Imperial College London | UK | 100 | 100 | 100 | 80 | 98 | 100 | 97.8 |
| 5= | 4 | University of Oxford | UK | 100 | 100 | 100 | 80 | 96 | 97 | 97.8 |
| 7 | 8 | University of Chicago | US | 100 | 99 | 97 | 88 | 77 | 83 | 96.8 |
| 8 | 12 | Princeton University | US | 100 | 96 | 82 | 100 | 89 | 81 | 96.6 |
| 9 | 9 | Massachusetts Institute of Technology | US | 100 | 100 | 89 | 100 | 31 | 95 | 96.1 |
| 10 | 5 | California Institute of Technology | US | 99 | 72 | 87 | 100 | 100 | 89 | 95.9 |
| 11 | 10 | Columbia University | US | 100 | 99 | 97 | 92 | 28 | 89 | 95.6 |
| 12 | 11 | University of Pennsylvania | US | 96 | 99 | 85 | 98 | 82 | 60 | 94.2 |
| 13 | 13= | Johns Hopkins University | US | 98 | 79 | 100 | 99 | 28 | 71 | 94.1 |
| 14 | 13= | Duke University | US | 95 | 97 | 100 | 93 | 29 | 62 | 92.9 |
| 15 | 15 | Cornell University | US | 100 | 99 | 85 | 94 | 28 | 73 | 92.5 |
| 16 | 17 | Stanford University | US | 100 | 100 | 71 | 100 | 25 | 96 | 92.2 |
| 17 | 16 | Australian National University | Australia | 100 | 91 | 75 | 74 | 99 | 92 | 90.5 |
| 18 | 20 | McGill University | Canada | 100 | 97 | 92 | 61 | 67 | 95 | 90.4 |
| 19 | 18 | University of Michigan | US | 99 | 99 | 85 | 81 | 57 | 52 | 89.9 |
| 20= | 23 | University of Edinburgh | UK | 97 | 99 | 84 | 65 | 93 | 86 | 89.3 |
| 20= | 24 | ETH Zurich (Swiss Federal Institute of Technology) | Switzerland | 97 | 80 | 55 | 99 | 100 | 94 | 89.3 |
ASR Analytics at the NACAC Conference in Baltimore
September 24, 2009 | Leave a Comment
Thank you for visiting ASR Analytics at the NACAC Conference in Baltimore. Many of you have asked for additional information to about our strategic reporting and predictive analytic solutions. We will update this page with a schedule of web demonstrations so that you may share with others on your campus.
Here are some quick links to more about our solutions:
Strategic Recruitment Reporting
The winner of the TomTom GPS will be announced here at the conclusion of the show.
User-Friendly Predictive Analytics Recruitment Solution Unveiled at NACAC Conference
September 23, 2009 | Leave a Comment
Baltimore, MD September 23, 2009 — While attending the National Association for College Admission Counselors (NACAC) 65th Annual Conference, ASR unveiled its new Predictive Analytics Solution for Recruitment and Admission. The new solution is designed to put the power of advanced analytics directly into the hands of enrollment management professionals for better evidence-based decision making. ASR’s Recruitment Analytic Models leverage institution specific data to estimate statistically valid forecasts of future enrollment, net tuition revenue, and even retention rates. These models provide enrollment managers with evidence based predictions for shaping the incoming class with the ‘ideal’ students for their institution.
ASR’s solution is different. Admissions professionals will be able to interact with the models to build various enrollment scenarios and change the model’s assumptions. This helps them understand the inevitable trade-offs that can happen when they simulate various policy ‘levers.’ ASR’s solution focuses on making these models accessible to non-technical admissions professionals. Most of the solutions on the market require an IT professional to extract data in a specific file format to provide to a third party that estimates an analytic model. The institution receives a static report to guide planning decisions, but it doesn’t let them simulate a variety of scenarios.
ASR’s new Predictive Analytics Solution for Recruitment and Admission will help the institution develop its recruitment strategy and at the same time enable better day-to-day tactical decision making. The solution will help institutions to:
- Identify causal factors for enrollment
- Analyze a prospect pool for more effective list purchases
- Simulate a multitude of enrollment scenarios
- Forecast enrollment on a daily basis throughout the recruitment lifecycle
- Perform decision impact analysis and assessment
There were three main goals in development of the framework:
- Provide a user-friendly way for busy enrollment management professionals to interact with predictive models to aid in institutional planning.
- Produce a solution that works with existing tools and technology already in use at the institution.
- Eliminate the need for clients to pay new recurring software license fees.
The secret to successfully meeting these goals lies in ASR’s ability to develop analytic solutions that help institutions integrate their people, process, and technology. “We think it’s critically important that advanced analytics are put directly into the hands of those that do the planning and make the decisions” said, Dr. Peter Arena, ASR’s founding principal and chief statistician for higher education. “Using simple, point-and-click interfaces – enrollment professionals can bring data and information to life. The result is a rich user experience that makes it easier to visualize data, simulate decisions before they are made, and ultimately optimize recruiting.”
To get people started with predictive analytics, ASR is offering a special NACAC conference rate for 50% off the company’s Prospect Scoring Report service. Attendees can visit the ASR booth for more details and to see a demonstration of the full solution.
To learn more about ASR’s solutions for higher education visit:
ASR’s Predictive Analytics Solution for Recruitment and Admissions
ASR’s solutions for Higher Education
ASR’s business intelligence blog
About ASR Analytics, LLC
ASR Analytics LLC (ASR) provides high-end business intelligence and analytic consulting services to clients in higher education. ASR aims to provide institutional decision makers with self-service decision support tools to help them be more effective in their recruitment, retention, and accountability initiatives. To learn more about our solutions visit: http://www.asranalytics.com/
Recruiting with Predictive Analytics in Uncertain Times
September 22, 2009 | Leave a Comment
It’s late September and the new academic year has settled upon us. Admission offices across the land are already busy recruiting the next set of classes, while at the same time conducting postmortems on the good and the bad of the most recent campaign. Some institutions have found the 2009 recruitment cycle to be quite unpredictable.
Recently, the Providence Journal ran a story about the difficulty some Rhode Island institutions had enrolling and managing the inflow of new students for the fall semester. This, despite the fact that nationally the number of graduating high school students remains at nearly record highs (although down slightly from the previous 2 or 3 record years). What was driving the instability? The economy, of course.
Private institutions in Rhode Island, according to the article, seem to have been hit the hardest and many were short of their goals. What struck me, however, were the tactics that were used by admission officers to stave off the potential declines. Seemingly, institutions broadly applied a combination of two tactics; either, 1) increase the amount of institutional gift aid to effectively discount the tuition, or, 2) lower admission standards to increase the eligible pool of applicants. Sometimes they did both.
While I may be over simplifying the true remedies attempted by these institutions, I can tell you from experience that the typical responses to shifts in the marketplace for many admission officers tend to be linear and broadly applied:
- When the demographic pool shrinks – buy more search names.
- If times are tough economically – increase the discount.
- When the numbers don’t come through as expected and the cause is not clear – lower admission standards.
The problem with these approaches are that they often conflict with other strategic goals of the institution (e.g. quality, diversity, etc.).
Enter predictive analytics. Although this may seem contradictory to the premise that 2009 was an unpredictable year, statistical modeling techniques are not only warranted in times like these, but even more necessary than during ‘normal’ recruitment cycles. You see, predictive modeling does more than forecast aggregate numbers. The idea is really to understand the key drivers of enrollment using mathematical models. Once you truly understand who and why students enroll at your institution, you can better segment or target the audience and more effectively recruit the students that are more likely to enroll.
As a result, you will be able to address the problems I’ve listed above with more surgical precision. So instead of giving a blanket discount to all accepted students, you will be able to identify the students that will benefit the most from the award through the analysis of a combination of driving factors.
“What-if scenario” decision support applications make it possible to interact with predictive models so that you can “war-game” various scenarios. So, for example, you could make the output of your model more conservative in an uncertain recruitment cycle by applying limits or weighing certain variables (like household income) more heavily in your enrollment propensity models.
The lesson is that there is almost always a way to use data to your advantage, even in turbulent times. Broadly applying tactics across all of the prospective student body should be a last resort.
IBM Jettisons U2 Database Division – Beginning of the End?
September 21, 2009 | Leave a Comment
As an IT professional, how do you know when it is time to not only consider migrating off a legacy technology but to quickly make plans to jump ship? How long before development and ultimately support come to an ignoble end? Nothing is certain in this industry as I have come to learn over the years, but the announcement last week by IBM of the sale of the U2 division offers some illustrative clues. The first is the remarkably short press release. I guess there really wasn’t much to say since U2 is not well known — even within IBM itself.
Perhaps a little context first. The U2 product line consists primarily of the Universe and UniData multi-value nested relational databases and related development tools. They can trace their heritage back into the 1960’s as an offshoot of the Pick architecture. In short, there’s a long history with numerous established commercial applications using it. Perhaps the best known is the DataStage ETL tool. IBM purchased the conglomeration of products in 2001 after a turbulent period of buyouts and mergers. At the time there was a large worldwide license base generating healthy recurring revenues coupled with a strong development and partner community. Those invested in the technology were relieved to be under the umbrella of a large, stable, recognized brand. It seemed credibility, new investments, and a future were assured. But that was before Microsoft entered the application database world with force and the shift toward low cost PC servers. You can see where this train is going.
Back to the writing on the wall. Whenever a large, established player in the software world like IBM sells assets to a global company that nobody has heard of, I see that as a red flag. Rocket Software? Let’s do a poll. That’s what I thought. Though, there are other companies like them specializing in niche markets like mainframe software. Ever heard of ASG? These companies don’t need marketing and name recognition because they aren’t trying to compete head to head with the current technologies of the day. They’re making a business selling into and supporting the products people invested in years ago.
Yet, one has to conclude the license growth and maintenance renewals hit a concrete ceiling and have come crashing down in recent years, especially as many of U2’s largest application VARs have provided alternative so-called “industry standard” database options like SQL Server for their applications. These have proved far more popular with new customers and existing customers alike. The U2 product line just can’t generate the numbers being demanded by a company like IBM. So out it goes. However, a company like Rocket with a very different corporate strategy and cost structure, probably can make a go of it for awhile. But, I wouldn’t expect to see much in new innovation. In the Higher Education ERP market this will have the greatest impact on Datatel customers and to lesser extent Jenzabar clients who will now need to accelerate their plans to move to alternate database platforms.
It seems U2 has been put into palliative care. No one can be certain if there will be significant changes in the near or medium term, but the ultimate prognosis is still the same. Technologies are moving forward and fast leaving U2 behind. The IBM press release trumpeting Rocket’s ability to “stimulate growth” is a veiled marketeering attempt to put life in a terminally ill product line.
Campus Technology: BI Project Success
February 10, 2009 | Leave a Comment
If you thought the implementation of your institution’s student information system was challenging, try deploying an institution wide business intelligence platform and data warehouse. While it doesn’t have to be hard, most institutions tend to approach business intelligence as a technology and not a strategy. This is a fundamental mistake that almost always ends in failure. Yet, if you are willing to think differently about business intelligence (the strategy) then business intelligence (the project) can go smoothly and most definitely will provide a greater return on investment than most any other enterprise project.
In the February 2009 issue of Campus Technology Magazine, Dr. James Riha from Oklahoma City Community College, and I wrote about the keys to BI Project Succes. The article focuses on the organizational aspects of managing a business intelligence implementation project. In the article we discuss the importance of:
- Developing a clear institutional vision for what business intelligence means to decision makers.
- Fostering the development of self-organizing teams.
- Rapid prototyping.
- End-user involvement, early and often.
- Planning to be ‘nimble.’
So give it a read and let us know what you think in the comments.
Private Institutions Steal Market Share from the Publics
January 21, 2009 | Leave a Comment
Total Enrollment and Market Share by Carnegie Classification
A couple of weeks ago I read an interesting article from Inside Higher Ed about College Affordability. This led me to investigate the work of the Delta Project, a non-profit organization examining the costs and accountability of higher education. While I am still digesting many of the reports on the Delta Project website, one table showing enrollment market share by Carnegie Classification (.pdf) jumped out at me.
The authors of the study note that there is a shift in market share toward private institutions during a five year period from 2002 – 2006. So why did such a shift take place?
According to the Delta Project:
- At public research universities, nearly all of the revenues from student tuition increases from 2002 to 2006 (92 percent) were used to offset revenue losses from other sources, primarily state appropriations. At public master’s institutions and community colleges, all of the revenues from increased tuition during this period replaced losses from other sources.
- At private colleges and universities, tuition increases fueled increased spending. Nearly three-quarters of educational spending increases at private research universities from 2002 to 2006 can be linked to increased tuition.
Thus, the argument is that private insitutions are providing greater value for the dollar as they increase services with revenues from tuition dollars, while public institutions are only modestly improving services as they are forced to use tuition revenues to offset declining state revenues.
While I have not yet read the entire study, I’m intrigued by this proposition. Yet, I can’t help but wonder if there are other forces at work that the authors may or may not have considered.
What are the macro-economic forces at work during this time? How do increases in adult enrollment factor in to the equation? Are helicopter parents more likely to send their children to private institutions than previous generations of parents?
These are some of the critical questions that I’ll have in the back of my mind as I read through the rest of the report. Meanwhile, if you are interested, you can find the full Trends in College Spending report here.
The Pitfalls of Tracking Year-Over-Year Enrollment Patterns
December 29, 2008 | Leave a Comment
All over the web people are writing about Amazon.com’s seemingly positive holiday sales numbers in contrast to the overall retail market. At least one blog took some time to do some analysis between this year’s results and last year’s. After reviewing the analysis, however, it is clear to me that the data is inconclusive. Take the example, of comparing the results of this year’s peak sales day to last year’s:
The busiest day of the year was pushed back to Dec. 15th from Dec. 10th in 2007, and Dec. 11th in 2006. It could be that customers were apprehensive about making purchases this year, and in that case, did not make purchases in the preceding days of the month, waiting until they ascertained their comfort level this holiday season. That would be a negative. However, it could also mean that customers became comfortable with shipping times and continued to shop until that date. That would be a positive. Or the shift in the peak date could just reflect the fact that there were 5 fewer shopping days this holiday season.
Naturally, as I read this my mind recalled countless similar conversations with our clients in higher education. In higher education, we too have “shopping days” before enrollment or the drop-add period. Yet, we often have about as much insight into why there are differences in enrollment patterns from year to year as is evident in the quotation above.
Why?
There are two main reasons for this; first, we often use far too few data points for a statistically meaningful comparison. One, two, or even three years of comparison data does not necessarily constitute a trend. Secondly, when there are many more years of data available, the passage of time often distorts the clarity of the information.
So before jumping to conclusions when analyzing year over year data, consider the following:
- Do you have five or more years worth of data to provide for meaningful trend analysis?
- Have there been any major events (e.g. weather, emergency outages) during these years that might trigger an outlier?
- Has the data been consistently captured across all of the years? Perhaps the system was down for an extended period of time or the data entry person was a week behind on entry.
- Have new technologies been introduced or promoted? For example, the introduction of web registration or a new online application could have a major impact on enrollment patterns.
Without considering these possibilities the conclusions you draw could adversely impact the decisions that you make. Having said that, I feel compelled to caution that if you aren’t sure what you would do differently with this information, any analysis conducted in this area is purely academic and may not actually help you run your institution more effectively. Ultimately you may be better served to focus analysis on more aggregate year-over-year numbers such as for the term or for a given month then on particular days or weeks during an enrollment cycle.
Press Release: ASR Analytics Puts Predictive Analytics Directly into the Hands of Enrollment Managers
September 29, 2008 | Leave a Comment
Seattle, WA, September 25, 2008: While attending the National Association for College Admission Counselors (NACAC) 64th Annual Conference, ASR unveiled its new Predictive Analytics Solution for Recruitment and Admission. The new solution is designed to put the power of advanced analytics directly into the hands of enrollment management professionals for better evidence-based decision making. ASR’s Recruitment Analytic Models leverage institution specific data to estimate statistically valid forecasts of future enrollment, net tuition revenue, and even retention rates. These models provide enrollment managers with evidence based predictions for shaping the incoming class with the ‘ideal’ students for their institution.
ASR’s solution is different. Admissions professionals will be able to interact with the models to build various enrollment scenarios and change the model’s assumptions. This helps them understand the inevitable trade-offs that can happen when they simulate various policy ‘levers.’ ASR’s solution focuses on making these models accessible to non-technical admissions professionals. Most of the solutions on the market require an IT professional to extract data in a specific file format to provide to a third party that estimates an analytic model. The institution receives a static report to guide planning decisions, but it doesn’t let them simulate a variety of scenarios.
ASR’s new Predictive Analytics Solution for Recruitment and Admission will help the institution develop its recruitment strategy and at the same time enable better day-to-day tactical decision making. The solution will help institutions to:
- Identify causal factors for enrollment
- Analyze a prospect pool for more effective list purchases
- Simulate a multitude of enrollment scenarios
- Forecast enrollment on a daily basis throughout the recruitment lifecycle
- Perform decision impact analysis and assessment
There were three main goals in development of the framework:
- Provide a user-friendly way for busy enrollment management professionals to interact with predictive models to aid in institutional planning.
- Produce a solution that works with existing tools and technology already in use at the institution.
- Eliminate the need for clients to pay new recurring software license fees.
The secret to successfully meeting these goals lies in ASR’s ability to develop analytic solutions that help institutions integrate their people, process, and technology. “We think it’s critically important that advanced analytics are put directly into the hands of those that do the planning and make the decisions” said, Dr. Peter Arena, ASR’s founding principal and chief statistician for higher education. “Using simple, point-and-click interfaces – enrollment professionals can bring data and information to life. The result is a rich user experience that makes it easier to visualize data, simulate decisions before they are made, and ultimately optimize recruiting.”
To learn more about ASR’s solutions for higher education visit:
ASR’s Predictive Analytics Solution for Recruitment and Admissions: http://www.asranalytics.com/solutions/education/recruitment-analytics/
ASR’s solutions for Higher Education: http://www.asranalytics.com/solutions/education/
ASR’s business intelligence blog: http://www.asranalytics.com/category/blog/
About ASR Analytics, LLC
ASR Analytics LLC (ASR) provides high-end business intelligence and analytic consulting services to clients in higher education. ASR aims to provide institutional decision makers with self-service decision support tools to help them be more effective in their recruitment, retention, and accountability initiatives. To learn more about our solutions visit: http://www.asranalytics.com/
Webinar – 6 Best Practices for Successful Institutional Intelligence
June 3, 2008 | Leave a Comment
On June 18th, ASR will be hosting a Webinar titled “6 Best Practices for Successful Institutional Intelligence.” The Webinar is an extension of an article written by ASR’s Director of Higher Education Services, Graham Tracey, which was published earlier this year in Campus Technology Magazine.
Since institutional intelligence initiatives require ownership across the college or university community, registrants are encouraged to convene in groups including institutional researchers, IT professionals, and decision makers from the major operational divisions.
Click here to read the full presentation description and to register.