Systematic Review on the Application of Multimodal Learning Analytics to Personalize Students' Learning

In personalized learning (PL), learning processes are customized to account for student skills and preferences. However, as PL is generally based on a single data type, it cannot wholly represent students' learning behaviors and progress. Hence, it is crucial to leverage Multimodal Learning Analytics (MMLA) in PL to alleviate these restrictions. A systematic literature review was conducted to explore the use of MMLA in PL and investigate its benefits across several contexts and approaches. The underexplored aspects of MMLA in PL, like the gaps in topics, pedagogies, learning settings and environments, populations, and modalities studied, are addressed, and MMLA’s potential to provide real-time tailored feedback and improve engagement is discussed.