The Future of Higher Education lies in Machine Learning

Despite tremendous improvements in how Machine Learning and Big Data have become applicable to almost every field, they must still be extended to higher education, for example, in the simulation of academic scenarios within educational management.

Among some of the benefits of using information technologies in higher education, machine learning improves the learning experience, the ability to analyze the management of campus at all levels and better organize tasks. In addition, it allows receiving new opinions, from the input of a computer.

But, although much has been said about this matter, many have overlooked their projections. 

Some of the most mundane tasks are relieved through the use of ML allowing us to focus on vital activities that we can only perform.

However, how can Machine Learning improve educational management further ahead?

Better simulations, for example. We are not just talking about using case studies and games in the classroom – although we still have to make progress in that area. We are talking about the power of machine learning and Big Data analysis, which fully exploits the power of Artificial Intelligence to expand the number of options and scenarios of any complex planning in our institution, such as admissions management.

Imagine planning only one academic program: How many variables should you be considering? How many options do you have available? How many combinations of courses, rooms or students should you consider? How long does it take humans to reach a decision?

At the World Economic Forum in Davos, Switzerland,  Dr. Katharina Hauck, Senior Lecturer in Health Economics, Imperial College London, talked about the future of Artificial Intelligence and how it is beginning to enhance large-scale analysis, for example, in the health industry.

It uses variable selection models through which it tests the importance of each factor with respect to the rest in scenarios where there may be more than one hundred thousand sub-models allowing us to reduce estimates, from weeks to a few days.

This discipline could play a key role in the area of learning analysis, not only in curriculum quality but in the creation of more adaptive learning systems.

ML information allows administrators to explore possible future scenarios simulating realities at low cost without incurring many of the risks of real experimentation. With the help of human questions and a good conceptual framework, smart machines can help managers to review vast volumes of data to discover patterns.

In addition to that, the use of Machine Learning in management can show the long-term consequences of certain short-term decisions. Thus, machines can help identify unexpected consequences of a resolution or discover value niches with rapid experimentation, at high speed.

In this way, higher education managers can consider many more alternative perspectives, simulating the impact of certain events and helping them to prevent problems in decision making and even identifying and solving various cultural, moral and ethical problems in different scenarios.

But how do we apply these assumptions in higher education management?

Once we have assumptions to manage our university resources, our team uploads data in ERP systems.

These allow managing all this influx of information. However, they fail to automate the delivery of solutions. Thus many universities have realized that an ERP fails to handle so much workload because it fails to automate the academic planning process.

It is here where Machine Learning techniques become necessary using what is termed as “Intelligent Decision-Making Systems” that allow the elimination of several inconveniences of ERP and other traditional decision-making systems.

These applications can effectively manage simulations and predictions in areas such as decentralization of campus management, student profiling, collaborative work, planning and much more.