## 4 Conclusions and Future Work  
HCI technologies have become integral in skills learning, offering engaging interactions and replicable solutions that enhance learning experiences. These HCI-based systems often incorporate real-time feedback based on user performance to boost user engagement and learning outcomes. By including human behavior in addition to user performance into the feedback mechanism, we can further increase skills learning and engagement. Manual labeling or annotation of data by experts is often used for offline analysis, however evaluating human behavior using this method can be resource-intensive, hindering real-time feedback capability. Thus, the ability to accurately and autonomously recognize  
Figure 10: Confusion matrix for: (**a**) HMM; (**b**) rule-based model. For both confusion matrices, the column on the left for the collaborative behaviors represent the real labels, while the labels at the top represent the predicted behaviors. The boxes highlighted in yellow indicate the highest number of predictions for each of the behaviors, the boxes highlighted in green represent a positive performance measure of either recall, precision, or accuracy. The boxes highlighted in orange represent a negative performance measure of recall, precision, or accuracy. human behavior using computational classification methods is crucial for effective feedback mechanisms. Among various machine learning methods used to predict human behavior [68], probabilistic models, such as Hidden Markov Models (HMM), have been commonly used to predict or extract behavior patterns [34, 36, 37, 38]. Leveraging probabilistic classification methods, HMMs analyze temporal patterns, making them suitable for predicting human behavior in collaborative interactions [33]. Our work contributes to this field by developing an HMM-based model tailored to recognize collaborative behaviors in dyadic interactions using multimodal signals. We additionally designed a rule-based method of behavior prediction for a baseline comparison. Building on our previous work which developed a teamwork training simulator in a CVE [11], which showed acceptability in dyadic interactions between autistic and neurotypical participants, we extend our work by designing prediction models that could be used to recognize collaborative behaviors in dyadic teamwork interactions. In future work, we want to explore the impact of utilizing the predicted collaborative behaviors as an input for a real-time feedback mechanism and how that could improve collaborative interactions in dyadic interactions, specifically cross-neurotype collaboration. The results of the preliminary study indicated that our HMM prediction model was able to recognize collaborative behaviors with 90.59% accuracy, outperforming the rule-based model. While both models excelled in predicting _Engaged_ behavior, the HMM demonstrated greater flexibility, particularly in predicting the _Waiting_ and _Struggling_ behaviors, due to its ability to leverage temporal information learned during training. This underscores the advantage of the HMM without requiring extensive labeled data for training. Additionally, our creation of a novel dataset, comprised of multimodal signals from dyadic interactions labeled with collaborative behaviors by expert annotators, opens avenues for further research and experimentation in this domain. By integrating robust behavioral prediction mechanisms into computer-based teamwork training, we anticipate a significant enhancement in collaborative skills development, ultimately advancing the efficacy of virtual training environments. Although the results are promising, it is important to acknowledge limitations in the HMM design and suggest key improvements for future studies. First, extracting more complex features from the multimodal data would allow researchers to better understand and observe a wider range of human behavior related to collaborative interactions.