Higher Education in 2026?

1915 women graduates - University of Toronto - via Wikipedia

The Chronicle of Higher Education has a new report, "2026 The Decade Ahead," it has recently published. I haven't read it and I probably won't read it. My involvement in higher education is less involved these days, but more so, I'm not going to spend $149 for the digital version ($199 for paper) of the report. There are always predictions of where we are headed in technology, education and in general. Many are free and I don't know that the differences in accuracy between free and paid versions is significant.

People do pay for these reports, and there are companies built on the job of predicting. Today, predictive analytics is a whole field and industry that seems to do quite well crunching numbers. In this election year, that is certainly a popular game, though one I rarely find interesting or instructive. Usually, I find these predictions to be wrong, but it is rare for people to go back and check on them. Idea for (someone else's) blog post in 2026: Check back on this report. Set a calendar reminder. So, what changes are in store for higher education over the next 10 years? The Chronicle's teaser says that "evolutionary shifts in three critical areas will have significant consequences for students and institutions as a whole."

1. Tomorrow’s students will be significantly more diverse and demand lower tuition costs.

2. Faculty tenure policies will be reexamined as deep-seated Boomers retire.

3. How colleges are preparing students to succeed in an evolving global economy will be intensely scrutinized. 

My immediate observation is that all three of those shifts have been evolving for at least the past decade - if not for several decades and possibly for a century or two in some ways. Of course, the answers are hopefully in the details that come in the full report.  Did you read it? How about a comment for those of us without an expense account or purchase order?



Visualizing Data

This is a nice example of data visualization showing bird migration in motion on a map.

The data is from millions of bird observations from participants in eBird and the Great Backyard Bird Count. (That count ran this year from February 12-15).

Scientists at the Cornell Lab used that data to generate an animated map showing the annual journeys of 118 bird species.  You can see how the routes change in spring and fall as birds ride seasonal winds to their international destinations.

If you want to know which species is which, you can switch to another version of the map showing species represented by a number. It is fast moving and a bit hard to interpret at a glance but you can start by looking for species like the Black-throated Blue Warbler (#16) passing by me in New Jersey or look for the Bobolink (#20), Solitary Sandpiper (#88), Prothonotary Warbler (#76), Lazuli Bunting (#55), Purple Sandpiper (#78) and Yellow-bellied Sapsucker (#114).

This post originally appeared on Endangered New Jersey

Cognizant Computing in Your Pocket (or on your wrist)

Two years ago, I wrote about the prediction that your ever-smarter phone will be smarter than you by 2017. We are half way there and I still feel superior to my phone - though I admit that it remembers things that I can't seem to retain, like my appointments, phone numbers, birthdays and such.

The image I used on that post was a watch/phone from The Jetsons TV show which today might make you think of the Apple watch which is connected to that ever smarter phone.

But the idea of cognizant computing is more about a device having knowledge of or being aware of your personal experiences and using that in its calculations. Smartphones will soon be able to predict a consumer’s next move, their next purchase or interpret actions based on what it knows, according to Gartner, Inc.

This insight will be performed based on an individual’s data gathered using cognizant computing — "the next step in personal cloud computing.

“Smartphones are becoming smarter, and will be smarter than you by 2017,” said Carolina Milanesi, Research Vice President at Gartner. “If there is heavy traffic, it will wake you up early for a meeting with your boss, or simply send an apology if it is a meeting with your colleague."

The device will gather contextual information from your calendar, its sensors, your location and all the personal data  you allow it to gather. You may not even be aware of some of that data it is gathering. And that's what scares some people.

watchWhen your phone became less important for making phone calls and added apps, a camera, locations and sensors, the lines between utility, social, knowledge, entertainment and productivity got very blurry.

But does it have anything to do with learning?

Researchers at Pennsylvania State University already announced plans to test out the usefulness in the classroom of eight Apple Watches this summer.

Back in the 1980s, there was much talk about Artificial Intelligence (AI). Researchers were going to figure out how we (well, really how "experts") do what they do and reduce those tasks to a set of rules that a computer could follow. The computer could be that expert. The machine would be able to diagnose disease, translate languages, even figure out what we wanted but didn’t know we wanted. 

AI got lots of  VC dollars thrown at it. But it was not much of a success.

Part of the (partial) failure can be attributed to a lack of computer processing power at the right price to accomplish those ambitious goals. The increase in power, drop in prices and the emergence of the cloud may have made the time for AI closer.

Still, I am not excited when I hear that this next phase will allow "services and advertising to be automatically tailored to consumer demands."

Gartner released a newer report on cognizant computing that continues that idea of it being "the strongest forces in consumer-focused IT" in the next few years.

Mobile devices, mobile apps, wearables, networking, services and the cloud is going to change educational use too, though I don't think anyone has any clear predictions. 

Does more data make things smarter? Sometimes.

Will the Internet of Things and big data converge with analytics and make things smarter? Yes.

Is smarter better? When I started in education 40 years ago, I would have quickly answered "yes," but my answer is less certain these days.


Predictive, Descriptive and Prescriptive Analytics and the Movies

Desk Set still

Tracy, Hepburn and EMERAC in DESK SET, 1957

I watched the 1959 film Desk Set over the holiday break. It is set within the TV network FBN, Federal Broadcasting Network (the exterior shots were done at Rockefeller Center, headquarters of NBC). Bunny Watson (Katharine Hepburn) is in charge of its reference library, which is responsible for researching and answering questions on almost any topic. With a secret merger pending, and anticipating a lot more demand for the department, the network boss has ordered two new computers.

Of course, this being 1959, the computers are called "electronic brains" in the film and they are huge. Richard Sumner (Spencer Tracy) is the inventor of them and they are called EMERAC. That name is some wordplay from ENIAC - the Electronic Numerical Integrator And Computer that was the first electronic general-purpose computer.

I also saw a new film over the break - The Imitation Game based on the book Alan Turing: The Enigma. The ENIAC computer was considered to be "Turing-complete" - a term from the work of Alan Turing. In the book and film, set during WWII, Turing is trying to crack the German Enigma code and in the course of doing that, saves the Allies from the Nazis, and sort of invents the computer and artificial intelligence.

The Spencer Tracy character in the 1959 film was also trying to create a digital way of solving problems. They describe him as being an "efficiency expert" which was a new and big concern in the 1950s.

Today, predictive analytics has become a big topic in educational technology and I have written a number of posts about its use in education. It is a way of using statistics, modeling and data mining to analyze current and historical facts in order to make predictions about future events. An example of one of the desired educational uses is to monitor at-risk students and allow interventions at the proper times.


Data analytics in higher education is still in its early years and the terms have changed over the past few years as the use of the term "big data" has replaced "data mining" in popular conversations. Where I was once reading articles about using "descriptive analytics" - the analysis of historical data to understand what has happened in the past - now I'm more likely to find articles on "predictive analytics" - using historical data to develop models for helping to predict the future.

Prescriptive analytics takes those predictions and goes to the next step of prescribing recommendations or actions to influence what happens in the future.

Confused? As an example, using big data and descriptive analytics about students and any particular student, we might predict the student's performance and problems in the current semester and then using a prescriptive analytics-driven learning management system we could recommend additional material, resources online or even notify on-campus people and departments to interact with the student early on.

Prescriptive analytics seems best-suited for educational problems like student retention, enrollment management, prospect analysis, improving learning outcomes and curricular planning. These are all problems that can be addressed with data analytics because there is adequate high-quality data to analyze those problem.

Did you read Moneyball or see the film version of Michael Lewis' popular book? It can be viewed as the story of the power of predictive analytics as he describes baseball's Oakland A’s team manager working with the lowest team budget in Major League Baseball and using predictive analytics techniques to turn around his team’s performance.

What schools need to do is similar to the "business rules" that companies formulate with input from various stakeholders in the organization.

Sometimes the data may produce results that are open to interpretation and campus experts need to be involved. In an article, "Prescriptive Analytics for Student Success," it is pointed out that student data now includes that generated by mobile device usage, campus cards, social media and sensor technologies. It presents an interesting case of an on-campus student who is not using dining services as often as before. Does that mean the student isn't as active socially on campus? Is she depressed? Can we add to the data class attendance or even clicker use? Is she more likely to drop out?

These "alternative data sources" are still emerging and may cross over into FERPA and privacy concerns about what is permissible in data collection. 

Learning analytics is another term used and seems to apply more to using learner-produced data and analysis models to discover information and social connections for predicting and advising people's learning. This area sounds like it would appeal more to teachers than the earlier examples, but in some ways there is a lot of crossover in studying individual learners. The data might allow the learner to reflect on their achievements and patterns of behavior in relation to their peers. It can warn them of topics or courses requiring extra support and attention. It can help teachers and support staff plan interventions with individuals and groups.

For departments and institutions, it can help improve current courses, help develop new offerings and develop marketing and recruitment strategies.

Predictive analytics is a big field and one that seems to strike fear into teachers much like those computers in Desk Set did more than 50 years ago. It encompasses statistical techniques from modeling, machine learning, and data mining. Do we trust the predictions about future, or otherwise unknown, events?

This is being done in actuarial science, marketing, financial services, insurance, telecommunications, retail, travel, healthcare, pharmaceuticals and other fields, but many educators (and I confess to still being one) are hesitant about moving business models into education.

You may be able to accurately do credit scoring (as in the FICO score) in order to rank-order individuals by their likelihood of making future credit payments on time, but can we really predict how a student will do next semester in Biology 102?

Nate Silver gained a lot of attention with his blog and books, especially during the last presidential election, showing that predictive analytics pays big dividends in politics, sports and business.

Companies such as Google, Twitter and Netflix are hiring predictive analytics professionals to mine consumer behavior and they are in the front of the office rather than crunching numbers behind the scenes.

In higher education, student retention is such a big concern that colleges find success using data analytics, it will quickly find its way from administrative tasks and into classrooms.