# A Comprehensive Review of Multimodal Analysis in Education
## 6 Future Directions  
The continued advancement of MMLA presents promising opportunities for both theoretical and practical contributions in educational research. The future directions outlined in this section reflect a synthesis of the insights gained from the reviewed literature, combined with the authors' critical assessment of current challenges and emerging opportunities in the field. These directions are intended to guide future research efforts and highlight areas where significant impact can be achieved. Real-World Deployment and Scalability: A significant proportion of current MMLA research remains confined to controlled or small-scale settings. Future work should prioritise the deployment of MMLA systems in real-world classrooms across diverse educational contexts. This includes primary and secondary education, non-formal learning environments, and under-represented geographical regions. Addressing issues related to technical scalability, such as latency reduction, robustness to environmental noise, and cost-effective hardware integration, will be essential to ensure broader adoption. Enhancing Model Interpretability and Transparency: While deep learning models have demonstrated remarkable performance, their opaque nature hinders practical implementation by educators and stakeholders. It is recommended that future research concentrate on the development of interpretable models that can provide teachers and learners with actionable feedback. The integration of explainable AI (XAI) techniques with multimodal pipelines has the potential to engender greater levels of trust and facilitate meaningful pedagogical decision-making processes. Ethical Frameworks and Participatory Design: The ethical implications of MMLA remain a pressing concern, especially regarding privacy, consent, and data ownership. Future research should explore participatory approaches that involve students, teachers, and caregivers in the design and governance of MMLA systems. Emphasising transparency, fairness, and inclusivity can help mitigate concerns about surveillance and data misuse while supporting agency and equity in learning analytics. Integration of Emerging Modalities and Technologies: Although current MMLA studies predominantly utilise visual, auditory, and physiological signals, the integration of emerging modalities such as tactile data, brain-computer interfaces, and augmented reality remains under-explored. Moreover, the incorporation of large language models (LLMs) and generative AI for contextual interpretation of multimodal inputs could enhance feedback generation and learner modelling, particularly in naturalistic learning environments. Longitudinal and Cross-Cultural Studies: The majority of MMLA studies are short-term and often context-specific. Longitudinal studies are necessary to evaluate the sustained impact of multimodal interventions on learning trajectories and educational outcomes. Additionally, cross-cultural investigations are crucial to assess the generalisability and cultural sensitivity of multimodal models and frameworks, especially in multilingual and socio-economically diverse settings. Professional Development and Teacher Support: MMLA has the potential to significantly enhance teacher professional development by providing insights into classroom dynamics, instructional effectiveness, and student engagement. Future research should explore how multimodal analytics can be integrated into teacher training programmes and continuous professional development, with an emphasis on usability and alignment with teachers' pedagogical practices. Standardisation and Benchmarking: There is a pressing need for standardised protocols and shared datasets to support replication, benchmarking, and cross-study comparisons. Establishing open-access repositories, benchmark tasks, and evaluation frameworks will strengthen methodological rigour and accelerate progress in the field.