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Quantitative Movement Testing: Measuring Patient Movements from a Single Smartphone Video Vision-Language Models Suppress Female Representations Under Ambiguous Input The New Social Image: How AI Competency and AI Proactivity Influence Self- and Peer-Perceptions in the Workplace TUX: Measuring Human--AI Tacit Understanding LLUMI: Improving LLM Writing Assistance for Mental Health Support with Online Community Feedback VideoFDB: Evaluating Full-Duplex Vision-Speech Capabilities in Conversational Agents Label Over Logic? How Source Cues Bias Human Fallacy Judgments More Than LLMs Inform, Coach, Relate, Listen: Auditing LLM Caregiving Support Roles How Coding Agents Fail Their Users: A Large-Scale Analysis of Developer-Agent Misalignment in 20,574 Real-World Sessions MetaRanker: Human-in-the-loop Active Ranking for Metalens Image Quality Analyzing Persona Effects in Generated Explanations from Multimodal LLM Agents in Urban Perception First head-to-head comparison of agentic AI applied to the analysis of simulated data of the Einstein Telescope Granuscore: A Reference-Free Measure of Granularity for Text Analysis and Question Answering The Timing Dependencies of Trust: Speed, Accuracy, and cBCI Neuro-Decoupling in Human-AI Teams Bayesian Distributional Models of Executive Functioning Visual Matters: Connecting Aesthetic Appeal and Production Quality of Photos, Infographics and Data Visualizations to Credibility of Social Media Posts Data-driven Head Motion Generation through Natural Gaze-Head Coordination Agreement Metrics for LLM-as-Judge Evaluation: What to Report and Why Perceptually Lossless Tactile Texture Synthesis with Compact Spectral Envelope Models MambaGaze: Bidirectional Mamba with Explicit Missing Data Modeling for Cognitive Load Assessment from Eye-Gaze Tracking Data CogAdapt: Transferring Clinical ECG Foundation Models to Wearable Cognitive Load Assessment via Lead Adaptation Augmented Analytics and Decision Quality: The Role of Trust among Non-Technical BI Users Faster Completion, Less Learning: Generative AI Reduced Study Time on Math Problems and the Knowledge They Build PaintCopilot: Modeling Painting as Autonomous Artistic Continuation Personality Engineering with AI Agents: A New Methodology for Negotiation Research PULSE: Agentic Investigation with Passive Sensing for Proactive Intervention in Cancer Survivorship Access Timing as Scaffolding: A Reinforcement Learning Approach to GenAI in Education Conversations in Space: Structuring Non-Linear LLM Interactions on a Canvas MAPLE: Self-Supervised Learning-Enhanced Nonlinear Dimensionality Reduction for Visual Analysis nASR: An End-to-End Trainable Neural Layer for Channel-Level EEG Artifact Subspace Reconstruction in Real-Time BCI
TSConnect: An Enhanced MOOC Platform for Bridging Communi...
Qianyu Liu, Xinran Li, Xiaocong Du, Quan Li · 2025-03-12 · via cs.HC updates on arXiv.org

Knowledge dissemination in educational settings is profoundly influenced by the curse of knowledge, a cognitive bias that causes experts to underestimate the challenges faced by learners due to their own in-depth understanding of the subject. This bias can hinder effective knowledge transfer and pedagogical effectiveness, and may be exacerbated by inadequate instructor-student communication. To encourage more effective feedback and promote empathy, we introduce TSConnect, a bias-aware, adaptable interactive MOOC (Massive Open Online Course) learning system, informed by a need-finding survey involving 129 students and 6 instructors. TSConnect integrates instructors, students, and Artificial Intelligence (AI) into a cohesive platform, facilitating diverse and targeted communication channels while addressing previously overlooked information needs. A notable feature is its dynamic knowledge graph, which enhances learning support and fosters a more interconnected educational experience. We conducted a between-subjects user study with 30 students comparing TSConnect to a baseline system. Results indicate that TSConnect significantly encourages students to provide more feedback to instructors. Additionally, interviews with 4 instructors reveal insights into how they interpret and respond to this feedback, potentially leading to improvements in teaching strategies and the development of broader pedagogical skills.