# A Comprehensive Review of Multimodal Analysis in Education
## 3 Methodologies in Multimodal Analysis
### Modelling Techniques
#### 3.3.1 Classical Machine Learning Approaches  
Support Vector Machine (SVM): SVMs have been widely applied in MMLA due to their robustness in handling high-dimensional data and their effectiveness in classification tasks. In the context of engagement detection and behavioural analysis, SVMs have been used to classify eye states from visual input [76], detect patterns in socio-spatial behaviour [77], and model facial expressions using Gabor features [73]. Several studies have integrated SVMs with ensemble approaches to improve predictive accuracy across educational tasks. For instance, SVMs were employed alongside decision trees and neural networks to estimate collaboration quality and analyse behavioural data [13; 78]. In simulation-based environments, they have contributed to identifying relationships between spatial-procedural behaviours and team performance [65]. SVMs have also been used in conjunction with random forest (RF) and gradient boosting in informal learning contexts, such as modelling visitor engagement in museums using multimodal sensor data [1]. Moreover, the adaptability of SVM kernels has been leveraged in predictive modelling tasks, with linear, radial, and polynomial kernels employed to enhance stability and accuracy [41]. In content analysis and student artefact evaluation, SVMs have been integrated into ML pipelines using open-source tools like scikit-learn [79]. The technique has also supported music-related learning applications, such as genre classification and intelligent labelling, contributing to the development of adaptive educational tools [80]. Overall, the use of SVMs spans diverse educational domains and serves various analytical goals, from behavioural prediction to artefact quality estimation, illustrating their ongoing relevance in multimodal modelling frameworks. Decision trees: decision tree-based models are frequently employed in multimodal educational research due to their interpretability, low computational cost, and capacity to handle diverse data types. Several studies have utilised standard decision trees or their ensemble variants to support modelling efforts in learning analytics. For instance,  
Figure 2: Classification of modelling techniques used in MMLA, along with their primary analytical purposes in educational contexts. decision trees were applied to classify students based on their cluster frequencies and learning gains in engineering design tasks, highlighting their utility in tracing conceptual progression [81]. Similarly, they have been employed for rule generation and behavioural pattern prediction in student engagement studies, where their transparency has proven beneficial for educational practitioners [82]. In the context of SRL, decision trees have also served as part of quantitative learning analytics approaches using mobile-generated data to identify student types [83]. More complex implementations, such as Gradient Boosted Decision Trees (GBDTs), have demonstrated strong predictive performance in applications like dropout detection in K-12 online environments, particularly when combined with data augmentation techniques to mitigate class imbalance [84]. Decision trees have also been adopted for multimodal modelling of affective and cognitive engagement, integrating features derived from brain waves, eye movement, and emotion metrics [54]. In collaborative learning contexts, they are frequently included in comparative analyses with other ML methods to assess modelling robustness in noisy environments [13; 48; 78]. Random Forest (RF): RF algorithms are frequently adopted in MMLA for their robustness, scalability, and ability to handle heterogeneous data. They have been employed for various educational tasks such as modelling engagement [85], predicting behavioural changes [86], assessing artefact quality [79], and evaluating collaboration quality [48]. Studies have also leveraged RFs to predict student progression from socio-spatial behaviours [77], estimate lesson performance [3], and model simulation-based team behaviours [65].