Abstract: Feedback is a crucial element of a student’s learning process. It enables students to identify weaknesses and improve self-regulation. However, studies show this to be an area of great dissatisfaction in higher education. With ever-growing course participation numbers, delivering effective feedback is becoming an increasingly challenging task. Hence, this paper explores the use of automated content analysis to examine feedback provided by instructors for good feedback practices measured on \texit{self}, \texit{task}, \texit{process}, and \texit{self-regulation} levels. For this purpose, four binary XGBoost classifiers were trained and evaluated, one for each level of feedback. The results indicate effective classification performance on self, task, and process levels with accuracy values of 0.87, 0.82, and 0.69, respectively. Additionally, inter-language transferability of feedback features is measured using cross-language classification performance and feature importance analysis. Findings indicate a low generalizability of features between English and Portuguese feedback spaces.