# A Review of Multimodal Interaction in Remote Education: Technologies, Applications, and Challenges
## 3 Commonly Utilized Methods
### Data Collection Methods  
* Digital Interaction Data: Traditional LMS and online learning platforms primarily collect learner behavioral data through log records (clickstreams), assignment submissions, and quiz performance metrics [9].
* Physical Learning Analytics: Physical learning analytics leverages sensor technologies and the Internet of Things (IoT) to embed computational capabilities into physical environments, thereby enabling real-time interaction and data collection [43].
* Sensors and Wearable Devices: By using hard devices such as eye trackers, posture recognition cameras, and heart rate monitors, researchers can capture learners' spatial positioning, postural dynamics, and physical interactions [39]. This approach facilitates the seamless integration of learning activities across multiple environments [44], thereby extending the scope of learning analytics beyond digital interactions to encompass real-world learning contexts.
* Physiological Data: The primary objective of multimodal physiological signal research is to enhance the understanding of emotional and cognitive states by integrating multiple physiological indicators, including EEG, EDA, and electrocardiography (ECG) (Table 2).  
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt} p{142.3 pt}} \hline \hline
**Dimension** & **Description** & **Reference** \\ \hline Behavior & Learners’ interaction patterns, including digital behaviors such as mouse clicks, scrolling, and text input, as well as physical actions in the learning environment & [PERSON] et al., 2018 [39] \\ \hline Cognition & Learners’ cognitive processes, including problem-solving ability, knowledge construction, and memory recall & [PERSON] et al., 2023 [40] \\ \hline Emotion & Encompasses learners’ emotional states such as anxiety, confidence, or frustration, which can be detected through physiological signals (e.g., EEG, EDA) or facial expression analysis & [PERSON] \& [PERSON] 2011 [9] \\ \hline Engagement & Measures learners’ attention levels and sustained participation, often assessed through interaction behaviors or physiological data (e.g., heart rate variability) & [PERSON] \& [PERSON] 2016 [41] \\ \hline Collaboration & Describes learners’ interactions in group learning, including distinctions between individual and collective attention or differences between self-regulated and collaborative learning emotions & [PERSON], [PERSON], \& [PERSON] 2020 [42] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Learning dimensions and their descriptions.  
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt} p{142.3 pt}} \hline \hline
**Modality** & **Application** & **Reference** \\ \hline EEG & Records brain activity to analyze emotional and cognitive states & [PERSON] \& [PERSON] (2014) [45]; [PERSON] \& [PERSON] (2023) [46] \\ \hline EDA & Detects autonomic nervous system activity, reflecting emotional fluctuations & [PERSON] et al. (2021) [47] \\ \hline ECG & Analyzes heart rate variability to assess emotional responses & [PERSON] \& [PERSON] (2023) [46] \\ \hline Other Signals & Includes electromyography (EMG), respiratory patterns, and photoplethysmography (PPG), which enhance emotion detection accuracy in specific contexts & [PERSON] \& [PERSON] (2014) [45] \\ \hline Wearable Devices & Increasingly used flexible skin-attached sensors and smart devices for long-term monitoring of multiple physiological signals & [PERSON] et al. (2019) [48]; [PERSON] et al. (2024) [49] \\ \hline \hline \end{tabular}
\end{table}
Table 2: Physiological measurement modalities and applications.