# A Comprehensive Review of Multimodal Analysis in Education
## 3 Methodologies in Multimodal Analysis
### Modelling Techniques
#### 3.3.7 Other Modelling Frameworks  
Epistemic Network Analysis (ENA): ENA has been applied to examine the co-occurrence and sequencing of coded features in learning processes, integrating temporality and structure into network-based interpretations. This method uses techniques such as Means Rotation and Singular Value Decomposition to simplify the dimensionality of behavioural connections. In one study, ENA was combined with a BERT-based predictive model to analyse student communications and forecast collaborative problem-solving skills, further enriched by MMLA that included verbal, spatial, and physiological data [2].  
Inverse Reinforcement Learning (IRL): IRL has been employed to model SRL by inferring the reward functions underlying students' observed behaviours, enabling the assessment of skill mastery and the delivery of personalised interventions. One study integrated IRL into a teacher dashboard system, MetaDash, to support instructional decision-making through real-time visualisations of learning trajectories [103].  
Multi-Task Learning (MTL): MTL plays a significant role in enhancing predictive accuracy through the shared representation of related tasks. For example, one study introduced a multimodal, multi-task stealth assessment framework that combined game trace logs and written reflections. Within this setup, MTL was leveraged to concurrently predict post-test scores and reflection ratings, allowing the model to generalise more effectively across distinct yet interconnected learning outcomes [96].  
Transfer Learning (TL): TL has emerged as a valuable approach in multimodal educational modelling, enabling systems to leverage prior knowledge from external datasets. In one study, TL was incorporated into a hybrid deep learning framework for facial expression recognition, contributing to improved generalisation and performance in multimodal teaching behaviour analysis [71]. Similarly, TL was employed to enhance predictive models in a game-based learning context by reusing learned representations across different tasks and modalities, including gameplay data, facial expressions, and reflective text [93].  
Adaptive factorisation machines: the study by [57] presents an individualised Adaptive Factorisation Machine (iAFM) model, extending traditional AFM approaches by employing linear mixed models to estimate student-specific learning rates in problem-solving tasks. Through binomial regression, the model predicts whether students can solve steps without tutor assistance, accounting for initial difficulty and the rate of learning per knowledge component. This personalised approach enables the differentiation of student learning trajectories, supporting the evaluation of teacher practices in AI-supported educational environments.  
Rule-based and expert systems: the study by [82] explores the use of rule-based systems and decision trees for classifying student engagement and behavioural patterns, emphasising their interpretability and suitability for educational contexts. The approach highlights the efficiency and clarity of rule generation from decision tree models, offering educators an accessible means to understand and apply ML outcomes in practice.