Technology can only do so much. It can provide incredible insights that organizations might not have imagined possible…but only if the people putting it together do it the right way. Take, for example, the recent story about a start-up that used artificial intelligence to help improve disaster response. The company used census information to predict where people would need the most help in case of, for example, an earthquake to help emergency responders quickly get to the people who needed help.
Among other problems, the start-up was mostly using residential census data, so stores where a large number of people might be trapped in a disaster would be ignored if rescuers relied solely on the information the start-up was providing. The lesson? The data is only as good as the people entering the information.
For many higher education organizations, so much emphasis is placed on analyzing data that it almost seems too obvious to point out: that data needs to be reliable. But how often are organizations stopping to look at data quality? (more…)
You only get one chance to make a first impression. Sound advice for socialization, sure, but it can also apply to the world of higher ed. (Beyond the chats at the water cooler, that is.) It is advice that might be heeded during a professor’s first class of the semester…or maybe even the first few minutes of every lesson that professor is teaching.
But it can also be applicable to the technology an institution of higher education is using. Many colleges and universities are increasingly using data for a number of purposes – to increase retention, to identify and help struggling students, or to make processes more efficient, to name just a few. A data dashboard can help or hinder the analytics process. It is the first impression a user gets with the data, and if it is too clumsy to navigate, a user might not come back to work with the data in a meaningful way. If it is easy to use, it could benefit everyone in the community. Here is some advice about how to find success with dashboards, and what makes a good dashboard in the first place. (more…)
Many colleges and universities are using higher ed analytics in one form or another, as they figure out how to best improve student performance or the school’s bottom line. For the most part, this is an individual venture on the part of the school, figuring out what data it can use to make a difference on its campus.
The United Kingdom is working on a different approach. It has spent the past year using a national learning analytics service. Institutions in England, Scotland, Wales, and Northern Ireland pool their resources and have opportunities to learn together about how to best use learning analytics. Here’s what collaboration around learning analytics through a diverse group of schools looks like.
With many colleges and universities considering expansion – new buildings, bigger spaces – they are increasingly turning to data analytics for their real estate needs. They are also considering how to balance the needs of a physical university with student demands for virtual learning.
I recently attended the European University Information Systems (EUNIS) 2019 Annual Congress in Norway, in which the discussion of university real estate featured prominently. Here are some of my takeaways from the event.
Often the conversation about higher education starts at the college level. But there are many lessons that colleges and universities can learn from the elementary and high school models when it comes to building a successful program.
As far as student success, that groundwork could be laid as early as pre-Kindergarten. In his book, How Schools Work: An Inside Account of Failure and Success from One of the Nation’s Longest-Serving Secretaries of Education, Arne Duncan makes the case that schools need to shift from their current K-12 model to a PK-14 model to best keep up with a twenty-first-century economy, and they also need to make more data-driven decisions. Here are some of the takeaways from his book.
Colleges and universities are using data in ever-growing ways. They analyze data in order to figure out which programs are doing well or whether new programs need to be added. They then examine the data to identify past patterns that might help predict what will happen next. And the data is increasingly being put to work in the field of artificial intelligence.
The use of artificial intelligence in U.S. education is predicted to grow by almost 50% over the next few years. Its uses range from the immediate and practical to the aspirational. Let’s take a look at how schools are using data in the field of AI, both directly with students and for administrative purposes on the back end, as well as the challenges the technology can present.
Recent news from the world of higher education has been a real mixed bag. There was the feel-good story from Morehouse College, where billionaire tech investor Robert F. Smith shocked everyone with the surprise pledge in his graduation speech to pay the student debt of that school’s class of 2019.
The month before, the news was more sordid, as high-profile celebrities became the public face of a college admissions scandal. The two stories seem starkly different at first glance, but what they have in common is the fact that they shed light on the economic situations faced by students and families when it comes to higher education. These are the types of situations that colleges and universities are using data to try and manage.
Congratulations to the class of 2019! That’s congratulations for the completion of all the hard work that earned a diploma, and also congratulations on some very good timing. The class of 2019 is entering the best job market in nearly 50 years.
As with any graduating class, though, there will be competition for the best jobs, and the skills that might determine who employers pick might not be what you think. Despite the appeal of data analysts – one of the college majors drawing the most attention from both students and employers – it isn’t just about the data. Which is not to say there isn’t plenty of data to look into about this year’s graduating class. Let’s dive into it a little more and explore the job prospects for the Class of 2019.
It’s a saying we’ve all heard in one history class or another: Those who do not learn from the past are doomed to repeat it. The maxim can apply to business intelligence as well – especially when it comes to predictive analytics. In fact, learning from past experiences is key to making predictive analytics work.
Data analysts are able to use past results to plot out the most likely outcomes in the future. Many colleges and universities are using predictive analytics to increase retention. Let’s take a look at how they’re doing it and exactly what information they are using to make the most informed decisions.
It is a tense time in the world of higher education. The schools that aren’t putting pressure on themselves to respond to popular trends and try to stay ahead of the curve are certainly receiving pressure from their communities to do so – whether it’s from a Board of Trustees or reflected in feedback from students and parents.
It’s easy to find the talking points that make it sound like an administration is making necessary changes to improve the way a school is using technology. But doing the work to back up the talk is a different story. A recent situation at the University of Texas at Austin provides a look at what not to do in this situation. Here’s how you can avoid falling into the same trap.