# A Comprehensive Review of Multimodal Analysis in Education
## 1 Introduction  
The evolution from unimodal to multimodal learning analytics (MMLA) represents a significant methodological shift in educational research. Unlike traditional approaches that rely on single data sources, MMLA integrates and triangulates a range of data types--such as speech, eye gaze, posture, and physiological signals--to offer a comprehensive representation of learning processes [1]. This integration facilitates the modelling of complex learning environments and enhances the accuracy of predictive models when compared to unimodal approaches [2]. MMLA contributes to a more holistic understanding of both observable behaviours and internal cognitive-affective states. By leveraging trace data from multiple channels, this approach captures nuanced learner activity, engagement, and emotion that would otherwise go undetected [3]. It supports diverse learning modalities and acknowledges the variety of ways in which students interact with content, thereby enabling more personalised and adaptive learning environments [4]. Moreover, the use of artificial intelligence (AI), machine learning (ML), and advanced sensing technologies has enabled the real-time generation of feedback and dynamic instructional adaptation, further enhancing educational responsiveness [5,6]. The evolution from unimodal to multimodal approaches offers clear advantages in understanding both observable behaviours and internal cognitive-affective states. Unlike unimodal systems, which often capture only a single dimension of learner activity, MMLA can provide a richer, more comprehensive view by integrating diverse data sources. For instance, integrating visual, auditory, and physiological signals can reveal deeper insights into engagement, cognitive load, and collaboration, supporting more accurate assessment and personalised interventions [7,8]. MMLA also facilitates the modelling of complex learning behaviours, such as group dynamics and emotional responses, which are critical for effective collaborative learning and feedback systems [9,10]. This approach enables the detection of context-specific learning patterns that would be challenging to identify with unimodal methods alone, enhancing the overall interpretability and impact of educational analytics [11, 12, 13]. The application of multimodal analysis holds particular value in studying learner engagement. By analysing multimodal discourse--including both verbal and non-verbal cues--researchers can access deeper layers of meaning, such as intention and emotion, in educational interactions [14]. This insight supports the development of adaptive systems that align instructional strategies with learners' needs and preferences [15]. Importantly, multimodal analysis also offers the potential to inform inclusive learning design by accommodating multiple intelligences and individual learning styles. Despite these advancements, several gaps persist in the field. Research has tended to prioritise verbal communication, with limited emphasis on non-verbal behaviours such as facial expressions and gestures, which play a crucial role in classroom interaction and learner engagement [16]. The integration of heterogeneous data sources remains methodologically challenging, particularly when aligning modalities that differ in granularity and temporal resolution [17]. Furthermore, MMLA has been underutilised in primary education, where its potential to support early learning remains largely unexplored [1]. There is also a lack of comparative research evaluating the performance of predictive models that incorporate low-intrusiveness modalities, such as wearable sensors. Exploring such combinations could lead to more naturalistic applications of MMLA in real-world settings [18]. Finally, the application of multimodal approaches in the context of teacher professional development has received little attention, despite its potential to enhance understanding of instructional practices and engagement in such settings [16].