Given the growing use of learning analytics in higher education and its role in improving the learning process, this study aimed to systematically review the theoretical foundations of learning analytics interventions in higher education. This systematic review was conducted by searching relevant keywords in domestic and international databases such as the Scientific Information Database (SID), ERIC, Scopus, etc. to examine articles published from 2017 onwards. 54 articles related to the research topic were selected and reviewed. The findings showed that learning analytics interventions are implemented with various goals, including improving academic performance, increasing student engagement and interaction, and enhancing teaching quality. These interventions utilize various types of data, such as learning activity data (e.g., login history and grades) and student characteristic data (e.g., age and gender). Intervention methods are also diverse and include providing personalized feedback
and recommendations, designing adaptive educational content, and using gamification elements. The results of these interventions have generally been positive, leading to improved academic performance, increased student engagement and interaction, and enhanced teaching quality. However, the implementation of learning analytics also faces challenges such as data privacy concerns and resistance to change. Finally, given the high potential of learning analytics in improving the higher education process, it is suggested that educational institutions benefit from this technology to enhance the quality of education with careful planning and attention to existing challenges.