# A Comprehensive Review of Multimodal Analysis in Education
## 5 Discussion
### Challenges and Methodological Limitations  
Rapid advances in MMLA have led to increasingly sophisticated frameworks for capturing and interpreting complex learning behaviours. However, the integration of these systems into educational practice remains fraught with unresolved issues spanning technical, methodological, ethical and pedagogical dimensions. These challenges not only hinder current implementation but also serve as important points of reflection for future research and development. A critical observation from this review is the relatively sparse consideration of multimodal approaches in many of the selected studies. This gap reflects a broader challenge in educational research, where the adoption of MMLA is often hindered by technical, financial, and institutional barriers. The high cost of multimodal sensors, the complexity of data synchronisation, and the need for specialised expertise limit the scalability of MMLA in real-world educational settings. Furthermore, many institutions lack the infrastructure required to support these data-intensive approaches, and there is often a reluctance to adopt unfamiliar methodologies without clear evidence of their pedagogical impact. This highlights the need for broader empirical validation and theoretical grounding to ensure that future MMLA models are not only technically sound but also contextually relevant, ethically transparent, and pedagogically meaningful. Without addressing these foundational gaps, the full potential of MMLA to enhance educational outcomes may remain unrealised. Technically, multimodal integration remains a primary bottleneck. The heterogeneity of data sources from video and audio to physiological and log data makes synchronisation and alignment difficult. While promising frameworks have emerged, the latency and high dimensionality of features often hamper real-time applications, especially when using deep learning models that are both resource-intensive and opaque [91; 107; 150; 174]. The labour-intensive nature of data pre-processing exacerbates these problems, requiring manual annotation or domain-specific configurations that are difficult to scale [97; 134]. From a modelling perspective, the tension between accuracy and interpretability remains unresolved. Although DNNs often outperform traditional models in classification or prediction tasks [69], their lack of transparency hinders both user confidence and pedagogical applicability. Educators and stakeholders are often reluctant to rely on systems whose inner workings are not understandable or actionable [71]. In addition, the generalisability of findings is undermined by small or institution-specific data sets, calling into question the ecological validity of many experimental designs [129; 140]. Another persistent concern is that of scalability. The robustness of MMLA tools in naturalistic classrooms is frequently compromised by environmental variability, such as lighting, occlusion and background noise, which affects the quality of video and audio inputs [8; 78]. A significant number of educational institutions also lack the necessary infrastructure to support the use of data-intensive tools, which hinders their adoption in institutions with less advanced technological capabilities [18; 85]. Ethical issues have become increasingly salient, particularly in light of concerns around surveillance and privacy. While some studies attempt to address consent and data security, there is often insufficient transparency regarding how data are processed, stored, and interpreted [7; 58; 99]. Teachers and students express ambivalence or resistance to persistent video and audio monitoring, especially when such practices lack clear pedagogical benefits or explanations [169]. A further layer of complexity arises in translating MMLA outputs into pedagogically meaningful actions. Despite the existence of systems capable of generating real-time feedback or visualisations, numerous teachers have reported difficulties in interpreting these outputs.