Abstract Students’ level of positive learning-centered affective states, like engagement or flow state, has been proved to be strongly related to dropouts’ prevention, higher learning rate, and better student performance in their courses. Measuring users’ engagement state in a more effective and user-independent way may help create a better design of interactive applications and develop intelligent, more sophisticated, and adaptative study environments. The engagement reviews found in the literature go through psychological definitions but do not go deeper into the physiological and behavioral indicators of the state. This review aims to analyze the current state of the art on engagement detection, to identify which are some of the most relevant physiological and behavioral indicators for engagement in students for its prediction during presential or online courses. A computer-aided systematic literature search was performed following the PRISMA methodology. A total of 24 articles were selected after removing duplicates and applying the selection criteria. These studies were analyzed to extract data relative to the physiological behavioral indicators, the classifier used, its’ accuracy, and the number of participants. Indicators, such as leaning forward or backward and parasympathetic activation (such as HR, HRV, and GSR) have proven to be strongly related to students’ engagement states. The multimodal channel systems have been proven to have better performance, but the question of the best channel combination is still on the table. Different classification methods (SVM, RF, NB) have achieved high accuracy performance in experimental setups, but there are still challenges for real-life setups detection systems.