## 5 Conclusions  
The evaluation of student's abilities and knowledge is defined by the GPA. According to it, we consider students to be good, bad, talented or lazy. Students' learning outcomes and their achievements are influenced by many factors, such as learning patterns, talents, interpersonal relationships, motivation and many others. If we were able to identify factors which influence the GPAs of specific students, we could nudge students for better results. This effect would have many positive benefits for both the school and the student. Therefore, the first main objective of our research was to identify the factors that influence students' learning results. Moreover, if we were able to predict GPAs, management of schools could optimize the functioning of the university, e.g., in the enrolling process for optional subjects or in the admission process to the university. Therefore, the second main objective of our research was to predict the GPA of students. We performed literature review analysis of the current state. Analyzing studies from many authors, we identified factors that influence the student's results. We divided the identified factors into psychological, sociological and study factors. Using these findings, we then designed a questionnaire and collected data. Data were collected from students of the Faculty of Management and Informatics at University of Zilina. To become familiar with the data, we first used the basic and structured exploratory analysis. We compared the results of the answers of different groups of respondents. We also tested the differences in means between the groups using statistical tests. For this purpose, we used the Shapiro-Wilk test and the non-parametric Mann-Whitney U-test. To meet our first research objective (to identify the factors that influence students' learning results), we performed correlation analysis, where we examined the statistically significant influence of factors on the dependent variable--the student's GPA. We used Pearson test and ANOVA test. Based on the correlation analysis we identified factors with a statistically significant dependence with the GPA. The identified factors were as follows: motivation to study again on the faculty, regular studying during the whole semester, lecture attendance, grade from the subject Algorithm and Data Structures 1, grade from the subject Algorithm and Database Systems, grade from the subject Informatics 2, grade from the subject Mathematical Analysis 1, grade from the subject Discrete Optimization, grade from the subject Algorithm and Data Structures 1, use of consultation during studies, watching specific teaching videos on YouTube, GPA in the first year of college, GPA in the second year of college, GPA at high school, group of students a student usually studies with, number of hours in a day a student uses social networks, number of hours a student sleeps per day, number of household members, number of cups of coffee a student drinks in a day. To meet our second main objective (to predict the GPAs of students), we implemented supervised machine learning models in R programming language. The assumption was that these models would be able to predict the GPA based on other, independent variables. We created 10 models, using methods of linear regression, decision trees and random forests. The models predicted the GPA of a student studying at the faculty using independent variables. One random forest model and one decision tree model used all variables as inputs. Based on the MAPE metric on test set, the model created by the backward regression procedure of linear regression provided the best results. However, the random forest model achieved the best average accuracy on five validation sets in cross-validation procedure. In other words, the random forest model RF1 achieved the best generalization accuracy on the new data. Therefore, we recommend the use of a random forest as a starting model for modeling learning outcomes based on other independent variables. Conceptualization, L.F.; methodology, L.F. and T.P.