How Big Data Affects Higher Education
Big data is collected everywhere and technology is evolving to not only gain more, but to use it in higher education. How a school chooses to use this data can be extremely helpful to faculty and students, but it could also hurt a student’s chances at an education. Through two examples, you can see both sides of how universities are starting to use big data.
Purdue University has found a data-driven method for measuring student success, and more importantly, if the student needs assistance. It’s called Course Signals, and it has already shown great results, improving six-year graduation rates by 21.48 percent since the project began. The way it works is fairly simple.
- A professor chooses to use Course Signals (over 145 do now) and sets the parameters for the course, such as frequency, signal levels, and feedback.
- Course Signals then monitors students across 20 categories to track progress at each feedback point.
- Students (nearly 24,000 have been impacted) receive a green, amber, or red signal.
- Green means the student is performing at a top level to achieve
- Amber means the student is doing well, but could improve
- Red means the student is not on a path to pass the course
- Each signal comes with feedback that is sent to the student to ensure they understand what the signal means and how they can improve or maintain at that point in the course.
This system is a great step that I believe most schools should implement. Not only will it help with retention of students who are at risk, but it will also provide students with a better understanding of how they are performing. Finally, it goes beyond the standard tools a professor uses to ensure each and every student is reaching their potential.
This idea of predictive analysis above is great, but it may have a limit. While it can work wonderfully for current students, it can also create a barrier for new students during admissions. The access to big data and student information allows universities to predict who will perform the best at the college level. While this concept seems good, it may be leaving out an important factor – personal evolution though higher education.
A potential student may be in the wrong age bracket, have other responsibilities, be in a low-income group, and the list goes on and on. This list could help prevent their admission. However, through higher education, they may be trying to achieve great things that are unseen by big data. Qualitative data may be blocking people with the biggest potential from accessing the education they need.
Now this view is personal. I understand that most schools already use some form of predictive analysis for admissions. My worry is that big data can take the “human” element out of it. To be successful, I believe that there needs to be a mix of both big data and human review to provide chances to all deserving students, not just the ones that received a virtual check mark.
Education technology is doing great things, and no one should hold it back. They should simply ensure they understand exactly how it impacts students and student success, because in the end, student success for any student is what’s most important.