arXiv subjects

Behdokht Kiafar

Publications and source records attributed to Behdokht Kiafar.

10 recordsLinked to original sources

arXiv2026

Understanding Computing Identity Development Through Mentorship and Epistemic Network Analysis

Computing identity plays an important role in students' participation, persistence, and sense of belonging in computing, yet identity development can be difficult to capture through survey measures alone. This study examines how computing identity is expressed in open-ended survey responses from 37 participants in computing-related fields. Using a Quantitative Ethnography approach, we applied Epistemic Network Analysis (ENA) to model co-occurrence patterns among six identity-related constructs: recognition, interest, competence, sense of belonging, self-doubt, and imposter syndrome. We compared the structure of computing identity narratives between participants who reported mentorship support and those who did not. Findings showed that participants with mentorship support had stronger connections among interest, competence, recognition, and sense of belonging, suggesting a more integrated and supportive identity structure. In contrast, participants without mentorship support showed stronger connections involving self-doubt and imposter syndrome, indicating that uncertainty and feelings of not belonging were more closely connected in their narratives. A two-sample t-test comparing ENA scores showed a statistically significant difference between the two groups along the X-axis, with a large effect size (Cohen's d = 1.72). These findings suggest that mentorship is associated with differences in the structure of computing identity and may help individuals connect their interests, abilities, recognition, and belonging within computing.

cs.CY↗

arXiv2026

Feedback Effects on Cognitive Dynamics: Network-Based Insights from EEG Patterns and Behavioral Performance

This study examines the impact of feedback on Electroencephalography (EEG) activity and performance during the Reading the Mind in the Eyes Test. In a within-subject design, eleven participants completed the test under Feedback and No-Feedback conditions. Using the principles of Epistemic Network Analysis (ENA) and Ordered Network Analysis (ONA), we extend these network-based models to explore the link between neural dynamics and task outcomes. ENA results showed that feedback is associated with stronger connections between higher frequency EEG bands (Beta and Gamma) and correct responses, while the absence of feedback activated lower frequency bands (Theta and Alpha). ONA further disclosed directional shifts toward higher frequency activity preceding correct answers in the Feedback condition, whereas the No-Feedback condition showed more self-connections in lower bands and a higher occurrence of wrong answers, suggesting less effective reasoning strategies without feedback. Both ENA and ONA revealed statistically significant differences between conditions (p = 0.01, Cohen's d > 2). This study highlights the methodological benefits of integrating EEG with ENA and ONA for network analysis, capturing both temporal and relational dynamics, as well as the practical insight that feedback can foster more effective reasoning processes and improve task performance.

cs.HC↗

arXiv2025

Reflections from Research Roundtables at the Conference on Health, Inference, and Learning (CHIL) 2025

The 6th Annual Conference on Health, Inference, and Learning (CHIL 2025), hosted by the Association for Health Learning and Inference (AHLI), was held in person on June 25-27, 2025, at the University of California, Berkeley, in Berkeley, California, USA. As part of this year's program, we hosted Research Roundtables to catalyze collaborative, small-group dialogue around critical, timely topics at the intersection of machine learning and healthcare. Each roundtable was moderated by a team of senior and junior chairs who fostered open exchange, intellectual curiosity, and inclusive engagement. The sessions emphasized rigorous discussion of key challenges, exploration of emerging opportunities, and collective ideation toward actionable directions in the field. In total, eight roundtables were held by 19 roundtable chairs on topics of "Explainability, Interpretability, and Transparency," "Uncertainty, Bias, and Fairness," "Causality," "Domain Adaptation," "Foundation Models," "Learning from Small Medical Data," "Multimodal Methods," and "Scalable, Translational Healthcare Solutions."

cs.LG↗

arXiv2025

MENA: Multimodal Epistemic Network Analysis for Visualizing Competencies and Emotions

The need to improve geriatric care quality presents a challenge that requires insights from stakeholders. While simulated trainings can boost competencies, extracting meaningful insights from these practices to enhance simulation effectiveness remains a challenge. In this study, we introduce Multimodal Epistemic Network Analysis (MENA), a novel framework for analyzing caregiver attitudes and emotions in an Augmented Reality setting and exploring how the awareness of a virtual geriatric patient (VGP) impacts these aspects. MENA enhances the capabilities of Epistemic Network Analysis by detecting positive emotions, enabling visualization and analysis of complex relationships between caregiving competencies and emotions in dynamic caregiving practices. The framework provides visual representations that demonstrate how participants provided more supportive care and engaged more effectively in person-centered caregiving with aware VGP. This method could be applicable in any setting that depends on dynamic interpersonal interactions, as it visualizes connections between key elements using network graphs and enables the direct comparison of multiple networks, thereby broadening its implications across various fields.

cs.HC↗

arXiv2024

Neurophysiological Insights into Multimedia-based Education: A PRISMA-ScR Review of fNIRS in Game-Integrated Learning Systems

Game-integrated learning systems (GILS) are a growing form of multimedia education. Brain-based evidence can help researchers and designers understand how GILS design choices shape how learners think and process information. This scoping review follows PRISMA-ScR and synthesizes 20 empirical studies (2014-2025) in which functional near-infrared spectroscopy (fNIRS) measured brain activity during GILS use. This corpus shows that fNIRS can capture brain responses across GILS platforms and game elements, and points to how neurophysiological evidence can inform multimedia design decisions, such as that different platforms activate different brain regions, that adaptive difficulty reduces cognitive load and improves performance simultaneously, and that collaborative gameplay predicts knowledge retention. The 20 studies in this corpus reflect a field with substantial room to grow. Causal links between brain activation and learning outcomes would give designers more reliable evidence for platform decisions. As fNIRS and multimedia devices improve, standardized methods, classroom settings, and real-time neural adaptation represent directions where future work can translate these findings into practical multimedia learning systems.

cs.HC↗

arXiv2024

MMASD+: A Novel Dataset for Privacy-Preserving Behavior Analysis of Children with Autism Spectrum Disorder

Autism spectrum disorder (ASD) is characterized by significant challenges in social interaction and comprehending communication signals. Recently, therapeutic interventions for ASD have increasingly utilized Deep learning powered-computer vision techniques to monitor individual progress over time. These models are trained on private, non-public datasets from the autism community, creating challenges in comparing results across different models due to privacy-preserving data-sharing issues. This work introduces MMASD+, an enhanced version of the novel open-source dataset called Multimodal ASD (MMASD). MMASD+ consists of diverse data modalities, including 3D-Skeleton, 3D Body Mesh, and Optical Flow data. It integrates the capabilities of Yolov8 and Deep SORT algorithms to distinguish between the therapist and children, addressing a significant barrier in the original dataset. Additionally, a Multimodal Transformer framework is proposed to predict 11 action types and the presence of ASD. This framework achieves an accuracy of 95.03% for predicting action types and 96.42% for predicting ASD presence, demonstrating over a 10% improvement compared to models trained on single data modalities. These findings highlight the advantages of integrating multiple data modalities within the Multimodal Transformer framework.

cs.CV↗

arXiv2024

Analyzing Nursing Assistant Attitudes Towards Empathic Geriatric Caregiving Using Quantitative Ethnography

An emergent challenge in geriatric care is improving the quality of care, which requires insight from stakeholders. Qualitative methods offer detailed insights, but they can be biased and have limited generalizability, while quantitative methods may miss nuances. Network-based approaches, such as quantitative ethnography (QE), can bridge this methodological gap. By leveraging the strengths of both methods, QE provides profound insights into need-finding interviews. In this paper, to better understand geriatric care attitudes, we interviewed ten nursing assistants, used QE to analyze the data, and compared their daily activities in real life with training experiences. A two-sample t-test with a large effect size (Cohen's d=1.63) indicated a significant difference between real-life and training activities. The findings suggested incorporating more empathetic training scenarios into the future design of our geriatric care simulation. The results have implications for human-computer interaction and human factors. This is illustrated by presenting an example of using QE to analyze expert interviews with nursing assistants as caregivers to inform subsequent design processes.

cs.HC↗

arXiv2024

Functional Near-Infrared Spectroscopy (fNIRS) Analysis of Interaction Techniques in Touchscreen-Based Educational Gaming

Educational games enhance learning experiences by integrating touchscreens, making interactions more engaging and intuitive for learners. However, the cognitive impacts of educational gameplay input modalities, such as the hand and stylus technique, are unclear. We compared the experience of using hands vs. a stylus for touchscreens while playing an educational game by analyzing oxygenated hemoglobin collected by functional Near-Infrared Spectroscopy and self-reported measures. In addition, we measured the hand vs. the stylus modalities of the task and calculated the relative neural efficiency and relative neural involvement using the mental demand and the quiz score. Our findings show that the hand condition had a significantly lower neural involvement, yet higher neural efficiency than the stylus condition. This result suggests the requirement of less cognitive effort while using the hand. Additionally, the self-reported measures show significant differences, and the results suggest that hand-based input is more intuitive, less cognitively demanding, and less frustrating. Conversely, the use of a stylus required higher cognitive effort due to the cognitive balance of controlling the pen and answering questions. These findings highlight the importance of designing educational games that allow learners to engage with the system while minimizing cognitive effort.

cs.HC↗

arXiv2023

Immersive Virtual Reality and Robotics for Upper Extremity Rehabilitation

Stroke patients often experience upper limb impairments that restrict their mobility and daily activities. Physical therapy (PT) is the most effective method to improve impairments, but low patient adherence and participation in PT exercises pose significant challenges. To overcome these barriers, a combination of virtual reality (VR) and robotics in PT is promising. However, few systems effectively integrate VR with robotics, especially for upper limb rehabilitation. This work introduces a new virtual rehabilitation solution that combines VR with robotics and a wearable sensor to analyze elbow joint movements. The framework also enhances the capabilities of a traditional robotic device (KinArm) used for motor dysfunction assessment and rehabilitation. A pilot user study (n = 16) was conducted to evaluate the effectiveness and usability of the proposed VR framework. We used a two-way repeated measures experimental design where participants performed two tasks (Circle and Diamond) with two conditions (VR and VR KinArm). We observed no significant differences in the main effect of conditions for task completion time. However, there were significant differences in both the normalized number of mistakes and recorded elbow joint angles (captured as resistance change values from the wearable sleeve sensor) between the Circle and Diamond tasks. Additionally, we report the system usability, task load, and presence in the proposed VR framework. This system demonstrates the potential advantages of an immersive, multi-sensory approach and provides future avenues for research in developing more cost-effective, tailored, and personalized upper limb solutions for home therapy applications.

cs.HC↗

arXiv2023

Towards Anatomy Education with Generative AI-based Virtual Assistants in Immersive Virtual Reality Environments

Virtual reality (VR) and interactive 3D visualization systems have enhanced educational experiences and environments, particularly in complicated subjects such as anatomy education. VR-based systems surpass the potential limitations of traditional training approaches in facilitating interactive engagement among students. However, research on embodied virtual assistants that leverage generative artificial intelligence (AI) and verbal communication in the anatomy education context is underrepresented. In this work, we introduce a VR environment with a generative AI-embodied virtual assistant to support participants in responding to varying cognitive complexity anatomy questions and enable verbal communication. We assessed the technical efficacy and usability of the proposed environment in a pilot user study with 16 participants. We conducted a within-subject design for virtual assistant configuration (avatar- and screen-based), with two levels of cognitive complexity (knowledge- and analysis-based). The results reveal a significant difference in the scores obtained from knowledge- and analysis-based questions in relation to avatar configuration. Moreover, results provide insights into usability, cognitive task load, and the sense of presence in the proposed virtual assistant configurations. Our environment and results of the pilot study offer potential benefits and future research directions beyond medical education, using generative AI and embodied virtual agents as customized virtual conversational assistants.

cs.HC↗