It is not new that the education sector suffered from the decrease in revenues in the pandemic period between 2019 and 2021. It was with the emergence of a new need, whichinstitutionsin the segment were forced to reinvent themselves and seek assertive solutions to adapt to distance learning. Adigital transformationwas about to intensify.
Speaking of Digital Transformation
The resolution of new business challenges in the educational sector has been the main motivating agent for investment in Digital Transformation by the executives of these companies. Most of the developed solutions are related to the Machine Learning, which basically consist of developing algorithms based on various data sources, enabling the prediction of certain student behaviors.
An example is the prediction algorithm for truancy, with it it is possible to identify the probability of dropout of students. Another application, which works similarly to streaming platforms, where the best titles are always suggested based on the student's school profile, is the course recommendation engine.
Want to know a little more about these solutions? So let's go...
What are recommendation algorithms?
As the name suggests, the algorithm course recommendation, aims to suggest relevant courses based on a certain personal profile. Through an in-depth analysis that includes patterns, correlations between data and even the distance between existing variables in the database, this solution promises to facilitate the selection of courses and reduce decision-making on the part of students.
Increasing student enrollment and reducing rejection during the course are some challenges that are solved with the recommendation algorithm, and without a doubt, it is one of the most relevant solutions in the digital transformation for the education sector.
How can Machine Learning help reduce student dropout?
According to Semesp (association representing higher education institutions in Brazil), more than 3.42 million students dropped out of universities in 2021, a rate of 36.6% of evasion. The million-dollar question that any leadership in the education sector would like to be answered is: How can I predict and/or lessen the student dropout?
This is another important theme in our journey of digital transformation. The answer to this question was developed through algorithms Machine Learning developed by ST IT Cloud, which make it possible to predict the student dropout. The algorithm uses information from the institution's database for a probability index of evasion for each student.
The solutions of Machine Learningdeveloped byST IT Cloud, can also be applied to other challenges in the educational sector, according to the needs of each project.
accelerate to Digital Transformationof your institution, through the solutions of Machine Learning developed by ST IT Cloud!
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