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20212026
most citedConfidence-Aware Learning Assistant

6 citations · 13 across the 12 of their papers we have counts for

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Showing 2025Show all

6 papers · 1 filter

cs.HC2025

Push or Light: Nudging Standing to Break Prolonged Sitting

Sohshi Yoshida, Ko Watanabe, Andreas Dengel +3

Prolonged sitting is a health risk leading to metabolic and cardiovascular diseases. To combat this, various "nudging" strategies encourage stand-ups. Behavior change triggers use…

cs.HC2025

TrackThinkDashboard: Understanding Student Self-Regulated Learning in Programming Study

Ko Watanabe, Yuki Matsuda, Yugo Nakamura +2

In programming education, fostering self-regulated learning (SRL) skills is essential for both students and teachers. This paper introduces TrackThinkDashboard, an application desi…

cs.HC2025★ 1 cited

GenAIReading: Augmenting Human Cognition with Interactive Digital Textbooks Using Large Language Models and Image Generation Models

Ryugo Morita, Ko Watanabe, Jinjia Zhou +2

Cognitive augmentation is a cornerstone in advancing education, particularly through personalized learning. However, personalizing extensive textual materials, such as narratives a…

cs.HC2025

Augmenting Online Meetings with Context-Aware Real-time Music Generation

Haruki Suzawa, Ko Watanabe, Andreas Dengel +1

As online communication continues to expand, participants often face cognitive fatigue and reduced engagement. Cognitive augmentation, which leverages technology to enhance human a…

cs.HC2025

SensPS: Sensing Personal Space Comfortable Distance between Human-Human Using Multimodal Sensors

Ko Watanabe, Nico Förster, Shoya Ishimaru

Personal space, also known as peripersonal space, is crucial in human social interaction, influencing comfort, communication, and social stress. Estimating and respecting personal…

cs.HC2025★ 3 cited

Human-in-the-Loop Annotation for Image-Based Engagement Estimation: Assessing the Impact of Model Reliability on Annotation Accuracy

Sahana Yadnakudige Subramanya, Ko Watanabe, Andreas Dengel +1

Human-in-the-loop (HITL) frameworks are increasingly recognized for their potential to improve annotation accuracy in emotion estimation systems by combining machine predictions wi…