## 1 Introduction
Another study compared HMM performance against traditional classification models that included support vector machine (SVM), random forest (RF), linear regression (LR), and deep neural network (DNN) in predicting students learning behavior in e-learning environments [37]. The study utilized early assessment data and results indicated that HMM outperformed the other models for 5 of the 6 courses with accuracies above 90%. Similarly, [PERSON] et al. [34], utilized an HMM to predict effortful behavior in adaptive learning environments using both performance and physiological data, enabling real-time feedback based on predicted behavioral patterns. In this work, our first contribution is the design of an HMM-based model to recognize collaborative behaviors in dyadic interactions using multimodal signals, aiming to provide real-time feedback and scaffold learning in computer-based interactions. As a baseline for comparison, we implemented a rule-based behavior recognition method designed in consultation with experienced behavior analysts. We compared the HMM model against the rule-based method with expert labeled data as the ground truth. While previous works have focused on behavior recognition in single-user interactions, recognizing human behavior in dyadic interactions, such as teamwork, remains underexplored. Teamwork, defined as collaborative work between individuals toward a common goal, has garnered increased research attention in recent years, particularly within organizational contexts [40; 41]. In addition to the benefits teamwork brings to a company, teamwork also leads to increased satisfaction in the workplace, which can fulfill personal growth [42]. As society embraces inclusive workplaces with neurodiverse individuals, research interest on cross-neurotype collaboration, i.e., collaboration between neurotypical (people with \"normal\" neurotypes) and neurodiverse individuals, has increased [43]. Studies have shown that cross-neurotype collaboration can be less effective than collaborations between individuals within the same neurotype [44]. This phenomenon, driven by the double empathy problem [45], underscores that challenges in cross-neurotype communication and social interaction are the responsibilities of both neurotypical and autistic individuals [46; 47]. Our second contribution is the implementation of the prediction model in our previously designed teamwork training simulator to support cross-neurotype teamwork training. Additionally, we contribute to the field by creating a novel dataset containing multimodal signals from dyadic interactions labeled with collaborative behaviors by expert annotators. The outcome of this work can motivate future research to incorporate a robust behavioral prediction model in a feedback mechanism within virtual training environments that could enhance training experiences by scaffolding learning and promoting active collaboration. By creating a novel prediction model and a multimodal dyadic interaction dataset, our work seeks to advance the integration of robust behavioral prediction mechanisms into computer-based teamwork training, ultimately enhancing collaborative skill development.