# A Comprehensive Review of Multimodal Analysis in Education
## 7 Conclusions  
This review provides a comprehensive synthesis of recent advances in MMLA in education, drawing on peer-reviewed studies to examine theoretical foundations, data modalities, modelling techniques, tools, and educational applications. The findings demonstrate that MMLA offers a robust framework for capturing the complexity of learning processes by integrating diverse data sources--visual, auditory, physiological, contextual, and interaction-based.  
A key strength of MMLA lies in its capacity to model both observable behaviours and internal cognitive-affective states, which supports more personalised and adaptive learning environments. Methodologically, the field benefits from the integration of advanced ML and deep learning models, with growing interest in fusion strategies, interpretable AI, and real-time feedback mechanisms. The application domains explored range from engagement monitoring and collaborative learning to inclusive education and simulation-based training.  
Nevertheless, the implementation of MMLA in real-world contexts remains challenged by technical constraints, data synchronisation complexities, model opacity, and ethical considerations surrounding privacy and consent. Furthermore, there is a need to improve the scalability and reproducibility validity of multimodal systems, especially in under-represented educational settings.  
Looking ahead, future research should prioritise not only technical and methodological advancements but also a deeper consideration of the epistemological and pedagogical dimensions of learning. MMLA technologies must be designed to capture the nuanced ways in which learners engage with, interpret, and produce knowledge within diverse educational contexts. This involves recognising that learning is not merely a cognitive process but also a social and cultural one, shaped by the negotiation and transformation of knowledge. To ensure that MMLA systems yield meaningful, equitable, and contextually relevant insights, researchers and practitioners should emphasise interpretability, fairness, and pedagogical alignment, alongside longitudinal studies, cross-cultural validation, and participatory design approaches that actively involve educators and learners in the co-development of these systems.  
This review aims to consolidate existing knowledge, identify ongoing gaps, and offer guidance for researchers and practitioners seeking to design, implement, and evaluate MMLA in education. By doing so, it contributes to shaping a future where multimodal evidence meaningfully informs teaching, learning, and assessment across diverse learning environments.  
Conceptualization, J.D.T.G.-S., F.P.R. and V.H.M.-D.; investigation, J.D.T.G.-S. and F.P.R.; writing--original draft preparation, J.D.T.G.-S., F.P.R., V.H.M.-D., J.S.-G. and A.M.-M.; writing--review and editing, J.D.T.G.-S., F.P.R., V.H.M.-D., J.S.-G. and J.A.O.; visualization, J.D.T.G.-S. and F.P.R.; supervision, F.P.R. and V.H.M.-D.; funding acquisition, J.S.-G. and J.A.O. All authors have read and agreed to the published version of the manuscript.  
The Spanish Government has partially supported this work under the grant SAFER: PID2019-104735 RB-C42 (ERA/ERDF, EU), and project PLEC2021-007681 funded by MCIN/AEI/ 10.13039/501100011033 and the European Union NextGenerationEU/PRTR.  
Not applicable.  
**Conflicts of Interest:** The authors declare that they have no conflicts of interest. The authors have no relevant financial or non-financial interests to disclose.