# Recommender System for University Degree Selection:
## 3 Theoretical Framework  
Academic processes generate much contextual data, such as academic performance and demographic and financial information. Thus, universities and colleges have tutors who carry out accompaniment processes to advise students on the most relevant careers according to their academic profile. However, a tutor cannot manage all the information from the thousands of previous students about whom there is information about their chosen career and academic performance; in addition, tutors can generate a bias based on their relationship with the students or their own experiences and beliefs. Previous recommender systems for university programme selection have predominantly relied on academic data such as grades and standardised test scores to generate recommendations. While these systems can be effective in specific contexts, they often need to account for the broader factors that influence a student's educational and career decisions. By contrast, our approach integrates academic and socioeconomic data, offering several key advantages, especially in countries with a lower Human Development Index (HDI). In many countries with a low HDI, the information available to students for making informed decisions about university programmes is often limited and not easily accessible. This lack of comprehensive, open information can hinder students' ability to choose programmes that align with their academic strengths and personal circumstances. Integrating socioeconomic variables--such as family income, parental occupation, and educational background--into our system provides a more holistic understanding of a student's context. This allows for academically appropriate recommendations and is better suited to the student's socioeconomic reality, thereby improving the relevance and practicality of the guidance provided. For instance, in regions where students may face financial constraints or a limited access to educational resources, our system can recommend programmes that match the student's academic abilities and can consider factors like potential financial limitations and the personal fit between the student and the university. This is particularly important in contexts where such information is not readily available, as it empowers students to make informed and feasible decisions given their personal and economic circumstances. Additionally, by addressing educational equity, our approach helps mitigate the barriers faced by students from disadvantaged backgrounds, who may not have the same support or resources as their peers. The integration of socioeconomic data enables the system to identify programmes that provide additional support or resources, helping to level the playing field and promote more equitable educational outcomes. For the present study, we articulate a dataset where the academic and socio-demographic information of the students in the final stage of secondary school and university is related. The dataset comprises the results of standardised tests administered to students at the end of their high school and college education. Therefore, a supervised learning model was employed to train various algorithms to predict university exam results based on data from the high school stage. In addition, the key variables that determine the results in the standardised examination of the university stage were determined. To facilitate the application of the model, the results are provided as recommendations that students, tutors, or universities can utilise. This information is intended to aid in making one of the most critical decisions in a person's life: selecting a university career. The vector \(\{X_{1},X_{2},\ldots X_{n}\}\) gives the data structure, representing the input variables for the model, and \"y\" means the student's average result on the university standardised test. The output \"y\" is a continuous vector from 0 to 300.