The convergence of educational technologies, ergonomics, and active learning frameworks offers a multidimensional approach to improving educational outcomes. This study examines the role of Learning Analytics (LA) in optimizing ergonomic educational spaces to support active learning within the higher education context of Kazakhstan, however the outcomes may equally be applied to neighboring countries. Addressing a gap between ergonomic design principles and data-driven educational practices, the study adopts a mixed-methods approach, combining quantitative and qualitative data collected from multiple institutions in Kazakhstan. Key Learning Analytics indicators were analyzed alongside parameters derived from ergonomic design frameworks to explore their relationship with active learning processes. The findings reveal statistically significant associations between selected Learning Analytics metrics and ergonomic features of learning environments, highlighting how data-informed spatial design can enhance student engagement and participation. These results underscore the importance of integrating technological and physical learning environments within a context characterized by ongoing higher education modernization and increasing adoption of digital tools. While the study provides empirically grounded insights relevant to institutional development in Kazakhstan, the findings are interpreted as context-sensitive rather than universally generalizable. Nevertheless, they offer potential implications for educational systems with similar structural and technological conditions (such as the countries like Uzbekistan, Kyrgyzstan, etc.)provided that adaptations are made to local or similar contexts. This study contributes to the growing body of research on Learning Analytics by extending its application beyond curriculum and assessment into the design of physical learning environments. It further emphasizes the need for context-aware, interdisciplinary strategies to support active learning in diverse educational settings.
Zhumazhanova et al. (Mon,) studied this question.