Conference Agenda

The enrollment process in public schools is an important procedure for school administration. In Northern Lima, most of the enrollment forms are filled manually, which leads to inefficiencies, risk of losing information and administrative overload [1], [2]. This research proposes a technological model based on machine learning techniques aimed at improving the management of these forms. The machine learning model will adapt predictive and classification algorithms to predict how likely is that a new enrollment process will encounter problems or delays due to paperwork required. Recent studies have shown that the right selection of algorithms based on the characteristics of data is critical to achieve effective models on educational and business scenarios [8]. Additionally, international studies have highlighted the importance of explainable machine learning models in predicting the academic performance of high school students, which reinforces the pertinence of applying this scope on public education [7]. Unlike traditional methods, this approach leverages the features of handling large volumes of data and learn from historical patterns, thereby assisting informed decision-making. The study contributes to the digital transformation of the public education system in Peru, with a high potential for scalability at regional and national levels.