The impact of artificial intelligence (AI) on our lives is undeniable. Although we may not notice it, AI has become ubiquitous. From the security features of our banking app to content recommendations on our favorite social media platform or even weather forecasting, the presence of AI touches almost every aspect of today’s reality.
Entire industries have been revolutionized by the immense power of software and hardware advancements. In the case of education, this revolution started not long ago, and many of the most important institutions have already taken steps to invest in technology that enables them to achieve the continuous and stable operating efficiency other industries have.
Particularly, the branch of AI that education can benefit most from is called machine learning.
What is machine learning and how can education benefit from it?
Machine learning is based on the idea that computer software can develop consistently better analysis by learning from new data without the need for human intervention. This means the results computer models produce can improve substantially over time.
Numerous higher education processes and operations can be automated and streamlined using machine learning. It may be used to deliver more accurate data and provide more informed suggestions.
Automation operates at three stages: providing correct information, offering suggestions, and taking action. We will see how machine learning can make a true impact on institutional operations, focusing on student retention, by impacting each of those stages.
Today, a lot of higher education institutions are still on the fence about taking the last step to go past predictive analytics and traditional data modeling and into the world of machine learning.
However, during the last few years it has become clear that adopting a machine learning–centered data analysis approach as a tool for administrators and faculty has become a game changer for higher education institutions and for the sector as a whole.
Using machine learning to prevent student dropout
Early warning systems allow administrators to improve interaction with students who are at risk of failing, losing financing, or dropping out entirely. Machine learning can assist in identifying at-risk students based on multiple criteria by analyzing student data more thoroughly and quickly than institutions can today. Based on the data, they may construct and constantly improve mathematical models of student success.
It can help schools develop more accurate criteria and indicators for at-risk pupils in the short term. In the long term, it will aid in the development of potentially game-changing retention models that will assist administrators in drawing conclusions about what genuinely helps different sorts of students commit from enrollment through graduation.
Developing student retention strategies
Students sometimes don’t ask their counselors for help on the best way to finish their degrees or may not get the counseling they require, often because they lack the time or are unaware that they need assistance (or even what the right kind of support is).
Machine learning can use student data to uncover correlations or trends that will assist advisors or student success experts to be more successful in their interactions with learners. Instead of giving out early warnings, a chatbot (a digital messaging software that delivers automatic answers) driven by machine learning may guide students to a system that offers personalized, one-on-one help.
College students can receive an email or a text message that not only suggests they register for classes, but also suggests a schedule based on their progress throughout their degree and the hours they regularly take. The model may even highlight courses that are risky to take together if the student wishes to maintain a high grade point average.
With machine learning and chatbots, for example, this scenario is becoming a reality. Smart registration can improve student engagement and success, and higher education institutions can implement these emerging technologies.
Machine learning will continue to bring great benefits
With the use of analytical data, predictive models are becoming the future of artificial intelligence.
In addition, the varied degrees of automation that machine learning offers open a wide range of skills that may be used in the higher education sector, including informing, generating suggestions, and taking action. It’s fascinating to see how machine learning can advance higher education and help institutions and students all over the world. Sometimes, institutions may not even be as far as they think from being able to implement such a solution.
Learning methods that are more tailored to each student’s requirements may be created inside the institutions’ analytics plans and predictive models can assist in this task. This will provide each student a far more individualized experience, improving their interactions with the institution, increasing their chances of academic success, and ultimately reducing their chances of dropping out.