Big Data. Algorithms. Predictive Analytics. The latest round of buzzwords in the digital space may sound daunting. Once you have large sets of data, what are you supposed to do with them? How do you even create algorithms? And how do you take data from the past to make predictions about the future? Thanks to developments in computer science, there is one solution for all these problems: machine learning.
Machine learning is based in pattern recognition. It is a method of analyzing data to make future predictions based on historical trends. The computer learns patterns, analyzes them and makes predictions without explicitly being programmed to do so. The computer learns and adapts as it is exposed to new sets of data. Sound complicated? Maybe, but you interact with machine learning more often than you think.
When you upload a photo to Facebook, it suggests that you tag the friends who are in the picture. But how does Facebook know exactly which friends are in the photo? Machine learning. Based on all the previously uploaded pictures Facebook has learned to identify your friends’ faces and recognize them in new pictures, even if the lighting, their ages or their hairstyles are completely different.
Machine learning methods have been making their way into the education space. Here are three ways machine learning has had an impact on education so far:
A challenge that teachers have always faced is that every student is different—what makes perfect sense to one student might be a complete mystery to another. Since teachers don’t have the time to instruct each student one-on-one, some students might slip through the cracks or lack the attention they need. Enter machine learning. With machine learning, students receive the personalized instruction they need. Digital tools and assessments allow students to learn at their own pace and to learn with methods that are most effective for them.
Case Study: Teach to One: Math
This company works with middle and high schools across the country to teach math in a personalized manner. Their assessments and lessons utilize machine learning to teach to students’ strengths and needs.
Machine learning can impact every aspect of a student’s education experience, and college recruitment is no exception. Choosing a college can be an overwhelming and challenging task. Even if the student is confident in knowing which major they’ll declare, it can be difficult to decide which college is right for them. With the help of machine learning, companies are aiming to solve that problem. Machine learning matches the values and characteristics of an individual student with those of an institution and recommends which schools will be the best fit.
Case Study: GoSchoolWise
GoSchoolWise aims to help international students who often can’t visit a US campus before enrolling. With the help of IBM Watson, GoSchoolWise analyzes application essays and other writings to extract personality characteristics and career goals and then find the colleges that would be the best fit.
Through machine learning, computers can examine all the data concerning current students and alumni and identify factors and trends of the most successful students. So, when prospective students apply, the computer can use its knowledge of successful students to determine if the student is likely to be the right-fit and graduate at the institution. This helps colleges focus their energy on recruiting the right students instead of guessing.
Case Study: RiteClass
RiteClass developed an Insights App that deals with predictive admissions. The app produces a “Prospective Student Fit Score” to determine if a certain student should be recruited or not.
As machine learning continues to become smarter, the impacts in the education field will be even greater. These are just a few examples of the many opportunities at our fingertips with new tools and technology. Our industry is evolving every minute.
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