## 2 Literature Review
### Modeling Approaches: A Comparative Analysis of Temporal Models  
To develop a robust comparative analysis of the MHMM against other prominent temporal modeling techniques, it is essential to draw on existing literature that covers the strengths and weaknesses of each model, as well as their applicability to dynamic assessment scenarios. Below is a summarized comparative analysis with references that support the characteristics of each modeling approach discussed. * Long Short-Term Memory (LSTM) Networks: LSTM networks are advanced neural networks capable of capturing long-range dependencies in sequential data, making them highly effective for various tasks, including language modeling and time-series prediction. They achieve superior accuracy, but at the cost of interpretability. The complexity of LSTMs can lead to a \"black-box\" effect, where understanding the rationale behind predictions is challenging [52]. Recent studies have shown that LSTMs outperform traditional models in various contexts due to their flexibility and power [53]. * Conditional Random Fields (CRFs): CRFs serve as a powerful discriminative model that incorporates the entire context of sequences for prediction. They effectively avoid the independence assumptions typical of generative models, thereby improving the accuracy of predictions. However, training CRFs can be computationally intensive, and they might fall short of capturing very long-range dependencies compared to LSTMs [54; 55]. Their integration into applications such as image labeling and natural language processing showcases their versatility [56]. * Markov chain: Markov chains are well-studied and highly interpretable because they assume the system's state is fully observable at every time-step, so the transition matrix can be inspected directly [57]. This transparency is valuable when clear state-to-state \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Data Modality** & **Example Metric** & **Typical Sensor Types** & **Example Devices/Methods** \\ \hline \multirow{3}{*}{Physiological} & \multirow{3}{*}{Heart Rate Variability (HRV)} & ECG, & Polar H10 (ECG), Apple \\  & & Photoplethysmography (PPG) sensors & Watch (PPG), Oura Ring \\ \hline \multirow{3}{*}{Behavioral} & \multirow{3}{*}{Task Completion Time (TCT)} & Computer input logs, & Event logs, accelerometers, \\  & & motion sensors, RFID & RFID tools \\ \hline \multirow{3}{*}{Subjective} & \multirow{3}{*}{NASA Task Load Index (NASA-TLX)} & Digital/paper & \multirow{3}{*}{NASA-TLX app, web} \\  & & questionnaires, mobile & \\ \cline{1-1}  & & apps & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Sensor types for multimodal data streams. dynamics must be reported. However, a plain Markov chain has two key limitations for multimodal proficiency assessment:  
* It cannot represent hidden or latent proficiency states, because there are no observation model mapping unobserved states to measured signals; problems that involve unobservable constructs therefore require more expressive models such as Hidden Markov Models [57]. * When multiple data streams (e.g., physiological, behavioral, subjective) are concatenated into a composite state, the number of distinct states grows exponentially, producing the classic curse of dimensionality and making the approach impractical for rich multimodal data [58]. * Unimodal Hidden Markov Model (HMM): Unimodal HMMs are proficient at modeling temporal sequences and are well documented for their effectiveness in applications such as speech recognition and time series analysis. Their strength lies in their ability to capture temporal dependencies clearly, according to [PERSON] et al. [59]. However, they are inherently limited by their inability to incorporate multiple data streams, leading to a potentially incomplete assessment of human proficiency [57]. Table 2 shows a comparison of Temporal Modeling. The choice of modeling approach hinges on the trade-off between predictive accuracy and interpretability.