Challenges and opportunities of multimodal data in human learning: The computer science students' perspective

Multimodal data have the potential to explore emerging learning practices that extend human cognitive capacities. A critical issue stretching in many multimodal analytics (MLA) systems and studies is current focus aimed at supporting researchers model learner behaviours, rather than directly learners. Moreover, MLA are designed deployed without learners' involvement. We argue order create interfaces support learning, we need gain an expanded understanding of how can authentic needs. present a qualitative study which 40 computer science students were tracked activity using wearable static sensors. Our findings outline curated representations about non-technical challenges these their practice. The paper discusses 10 dimensions serve as guidelines for designers effective ethically aware student-facing innovations.