## 1 Introduction  
Human-computer interaction (HCI) technologies have become prevalent tools for facilitating skill learning, offering engaging interactions and replicable solutions to benefit and enhance learning experiences [1; 2; 3]. These systems teach a range of skills, including cognitive abilities such as visual-spatial, auditory, and verbal skills [4]; affective learning such as emotion regulation [5]; and collaboration skills [6; 7; 8]. Real-time prompts and feedback through visual, audio, and tactile cues are integral features of these systems that help in enhancing user engagement and learning experiences [9; 10]. In our prior work, we developed a series of virtual collaborative tasks as a teamwork training simulator within a collaborative virtual environment (CVE), focusing on facilitating dyadic interaction [11]. While participants found the training paradigms engaging, we identified a need for real-time feedback mechanisms to support participants during these collaborative tasks. Recognizing human behavior in feedback mechanisms is crucial for effective training outcomes [12]. Recent studies reported that incorporating human behavior recognition, along with task performance, in adaptive training paradigms can significantly enhance participants' engagement and learning outcomes [7; 8; 13]. Current methods for behavior recognition rely heavily on behavioral experts to observe and evaluate human behavior either in real-time during experimental sessions [14] or through analysis of video recordings post-experiment [15; 16; 17]. While manual labeling of human behavior is reliable, it lacks real-time accessibility, is resource-intensive, and is prone to bias [18; 19; 20], highlighting the necessity for an automated human behavior prediction model. Various machine learning methods have been employed to predict human behavior in computer-based interactions [21; 22], aiming to address the inherent uncertainty in recognizing human behaviors [19]. Traditional machine learning approaches have been pivotal, with several studies achieving notable success [23; 24; 25; 26]. For instance, [PERSON] et al. [27] used deep learning and neural network methods to predict engagement and disengagement in human-robot interaction by extracting and scoring engagement-related features from human participants, such as gaze, head pose, and body posture, to infer engagement. The model achieved a 93% F1 score. Many of these studies used multimodal signals for the classification of behavior to improve the classification accuracy due to the complex nature of human behavior. According to a meta-analysis review of 30 studies that compared affect detection accuracy between multimodal and unimodal signals, researchers reported that accuracies based on multimodal data fusion were consistently better than those based on unimodal signals [28]. This is further supported by the findings in a study by [PERSON] et al. [29], where they reported the best agreement score using multimodal signals compared to a unimodal signal in a robotic empathy recognition system. In another study by [PERSON] et al. [30], signals from speech and head movement were captured and both verbal and non-verbal features were extracted from the signals to assess the collaborative behavior in discussions. To handle the stochastic nature of human behavior, hidden Markov models (HMMs) have been widely used [31; 32], offering robustness and flexibility in analyzing temporal patterns which are necessary for predicting behavioral patterns (e.g., emotions, activities, learning behaviors such as engagement, disengagement, confusion, frustration, distress, etc.) [33; 34; 35; 36; 37; 38]. [PERSON] et al. [39] demonstrated the effectiveness of an incremental discrete hidden Markov model (IDHMM) to recognize and generate multimodal joint actions in face-to-face interactions. The results reported that the classification accuracy of IDHMM was 92%, while a support vector machine (SVM)'s classification accuracy was 81%.