# A Comprehensive Review of Multimodal Analysis in Education
## 3 Methodologies in Multimodal Analysis
### Modelling Techniques
#### 3.3.1 Classical Machine Learning Approaches
In more specialised applications, hierarchical RFs were used to estimate learning interest from fused multimodal features [87], while ensemble comparisons showed RFs as strong performers in tasks like public speaking assessment [88], artefact classification [1], and collaboration modelling with noisy data [59; 78]. Their integration into multimodal pipelines has been shown to improve classification reliability in educational data mining [13], often in combination with data augmentation and feature normalisation techniques [79]. Finally, RFs have also supported exploratory modelling of engagement and learning outcomes through mobile and classroom-based multimodal recordings [65]. K-Nearest Neighbour (KNN): KNN has been applied in multimodal educational modelling for classification tasks due to its simplicity and effectiveness in handling small to medium-sized datasets. It has been used to analyse students' spatial-procedural behaviours in simulation-based learning, alongside other common classifiers such as logistic regression and RFs [65]. In broader MMLA frameworks, KNN has supported predictions of behavioural changes in students with special educational needs, contributing to model ensembles evaluated through standard validation protocols [13]. Additionally, KNN has proven valuable in identifying low-progress learners based on socio-spatial behaviours, offering a non-parametric alternative to linear classifiers in student performance prediction [77]. Logistic/linear regression: logistic and linear regression models offer interpretable solutions for predicting binary and continuous outcomes in MMLA contexts. Logistic regression has been used to model students' spatial-procedural behaviours in simulation-based learning [65], estimate collaboration quality from multimodal indicators [78], and identify low-progress students based on socio-spatial patterns [77]. In parallel, linear regression approaches, including multiple and ridge regression, have been employed to analyse collaborative problem-solving scores [67], assess learner performance and personalise support in instructional environments [89], and predict lesson activity outcomes by linking discourse patterns with action unit metrics [3]. Naive Bayes: naive Bayes classifiers serve as efficient probabilistic models for estimating collaboration quality and predicting learner behaviours in MMLA. These probabilistic models have been included in comparative evaluations alongside other ML algorithms to model constructs in classroom collaboration scenarios [78], support predictive modelling in special education contexts [13], and analyse multimodal signals--such as audio, video, and physiological data--to assess collaborative behaviours in educational settings [48]. Gradient Boosting (XGBoost, AdaBoost): these gradient boosting techniques have been applied in MMLA to model collaboration quality and improve classification accuracy. These approaches have been evaluated in regression-based models to assess learning sub-dimensions [59], as part of diverse classifier ensembles in simulation-based learning settings [13], and to estimate collaboration outcomes using robust model development pipelines [78]. Additionally, AdaBoost has been explored alongside other ML algorithms in the modelling of multimodal collaboration scenarios [48].