It is no secret that the educational sector suffered from decreased revenue during the pandemic period between 2019 and 2021. It was with the emergence of a new need thatinstitutionsin the segment were forced to reinvent themselves and seek assertive solutions to adapt to distance learning. Digital Transformation was about to intensify.
Speaking of Digital Transformation
Solving new business challenges in the educational sector has been the main motivating agent for investment in Digital Transformation by executives of these companies. In the vast majority, the solutions developed are related to Machine Learning, which basically consist of developing algorithms based on various data sources, enabling the prediction of certain student behaviors.
One example is the school dropout prediction algorithm, with it it is possible to identify the probability of student dropout. Another application, working 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 learn a little more about these solutions? Let's go…
What are recommendation algorithms?
As the name suggests, thecourse recommendationalgorithm aims to suggest relevant courses based on a specific personal profile. Through a deep analysis that encompasses patterns, correlations between data, and even the distance between variables existing in the database, this solution promises to facilitate course selection and reduce decision-making on the part of students.
Increasing student adherence and reducing rejection during the course are some of the challenges solved with the recommendation algorithm, and without a doubt, it is one of the most relevant solutions in digital transformation for the educational sector.
How can Machine Learning help reduce student dropout?
According to Semesp (representative association of higher education institutions in Brazil), more than 3.42 million students abandoned universities in 2021, a dropout rate of 36.6%. The million-dollar question, which any leadership in the educational sector would like to have answered is: How can I predict and/or reducestudent dropout?
This is another important topic in ourdigital transformationjourney. The answer to this question was developed throughMachine Learning algorithms developed by ST IT Cloud, which enable having a forecast onstudent dropout. The algorithm uses information from the institution's database to provide a probability index ofdropout for each student.
TheMachine Learningsolutions developed byST IT Cloudcan also be applied to other challenges in the educational sector, according to the needs of each project.
Accelerate the Digital Transformationof your institution, through theMachine Learning solutions developed by ST IT Cloud!
