Predicting the students' performance has become a challenging task due to the increasing amount of data in educational systems. In keeping with this, identifying the factors affecting the students' performance in higher education, especially by using predictive data mining techniques, is still in short supply. This field of research is usually identified as educational data mining. Hence, the main aim of this study is to identify the most commonly studied factors that affect the students' performance, as well as, the most common data mining techniques applied to identify these factors. In this study, 36 research articles out of a total of 420 from 2009 to 2018 were critically reviewed and analyzed by applying a systematic literature review approach. The results showed that the most common factors are grouped under four main categories, namely students' previous grades and class performance, students' e-Learning activity, students' demographics, and students' social information. Additionally, the results also indicated that the most common data mining techniques used to predict and classify students' factors are decision trees, Naïve Bayes classifiers, and artificial neural networks.