During the academic year 2023/24 one subject was monitored in the Bachelor´s degree in Food Engineering at the Escuela Tecnica Superior de Ingenieria Agronomica, Alimentaria y de Biosistemas (ETSIAAB, Higher Technical School of Agricultural, Food and Biosystems Engineering) at the Technical University of Madrid (UPM). Data were collected when monitoring the subject with the aim of analysing which variables affect academic performance of the students. The analysis of the data was done with the statistical tool called principal component analysis (PCA). Variables were collected on attendance of the students (presence on teaching-learning activities, such as attendance at theoretical and practical classes), dropout (monitoring of evaluation activities, such as completion of midterm and final exams) and also variables relating to basic knowledge of students and grades obtained in the exams. PCA is a statistical multivariate methodology used to study large sets of data. This method reproduces a great proportion of variance among a big number of variables by using a small number of new variables called principal components (PCs). The PCs are linear combinations of the original variables, and the analysis of multidimensional data is simplified when these are correlated. High absolute values of loadings of the variables on the PCs imply that the variable has a large bearing on the creation of that component. Thus, the most important variables in each component that best explain variance will also be useful in explaining variability between observations (i.e. students). The two first components explained a high percentage of the variance and were related with grades of the exams and attendance to lectures. Several clusters of students could be done with the values of that variables represented with the principal components and these clusters differentiated clearly the groups of the students that showed a high attendance to the lectures and also higher grades in the exams and the groups with low attendance and worse grades.

Keywords: Academic performance, attendance, multivariate analysis, academic indicators.