Dealing with uncertainty: A systematic approach to addressing value‐based ethical dilemmas in behavioral services

Huang, Weihe; Lee, Gabrielle T.; Zhang, Xiaofan

doi:10.1002/bin.1969

Cited by 2 publications

(1 citation statement)

References 44 publications

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“…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].…”

Section: Introductionmentioning

confidence: 99%

Prediction Models of Collaborative Behaviors in Dyadic Interactions: An Application for Inclusive Teamwork Training in Virtual Environments

Amat,

Plunk,

Adiani

et al. 2024

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Collaborative virtual environment (CVE)-based teamwork training offers a promising avenue for inclusive teamwork training. The incorporation of a feedback mechanism within virtual training environments can enhance the training experience by scaffolding learning and promoting active collaboration. However, an effective feedback mechanism requires a robust prediction model of collaborative behaviors. This paper presents a novel approach using hidden Markov models (HMMs) to predict human behavior in collaborative interactions based on multimodal signals collected from a CVE-based teamwork training simulator. The HMM was trained using k-fold cross-validation, achieving an accuracy of 97.77%. The HMM was evaluated against expert-labeled data and compared against a rule-based prediction model, demonstrating the superior predictive capabilities of the HMM, with the HMM achieving 90.59% accuracy compared to 76.53% for the rule-based model. These results highlight the potential of HMMs to predict collaborative behaviors that could be used in a feedback mechanism to enhance teamwork training experiences despite the complexity of these behaviors. This research contributes to advancing inclusive and supportive virtual learning environments, bridging gaps in cross-neurotype collaborations.

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Section: Introductionmentioning

confidence: 99%

Prediction Models of Collaborative Behaviors in Dyadic Interactions: An Application for Inclusive Teamwork Training in Virtual Environments

Amat,

Plunk,

Adiani

et al. 2024

Signals

View full text Add to dashboard Cite

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Kendra’s journey: an intrinsic case study of a deaf autistic child

Gosselin Hills,

Bowen

2024

Journal of Deaf Studies and Deaf Education

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This study used an intrinsic case study to investigate how interventions from applied behavior analysis impacted language development, academic progress, and the reduction of severe challenging behaviors for an 8-year-old child who is Deaf and autistic. 3 main themes were identified: language acquisition, behavioral change strategies, and academic performance. Additionally, 2 common threads wove in and out of each and connected all 3 themes: language access and qualified providers, including Deaf professionals. This study expands the literature about using behavioral strategies with children who are Deaf and autistic and discusses implications for clinical and educational practice.

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Dealing with uncertainty: A systematic approach to addressing value‐based ethical dilemmas in behavioral services

Cited by 2 publications

References 44 publications

Prediction Models of Collaborative Behaviors in Dyadic Interactions: An Application for Inclusive Teamwork Training in Virtual Environments

Prediction Models of Collaborative Behaviors in Dyadic Interactions: An Application for Inclusive Teamwork Training in Virtual Environments

Kendra’s journey: an intrinsic case study of a deaf autistic child

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