Oral presentation skills are a critical component of higher education, yet comprehensive datasets capturing real-world student performance across multiple modalities remain scarce. To address this gap, we present SOPHIAS (Student Oral Presentation monitoring for Holistic Insights & Analytics using Sensors), a 12-hour multimodal dataset containing recordings of 50 oral presentations (10-15-minute presentation followed by 5-15-minute Q&A) delivered by 65 undergraduate and master's students at the Universidad Autonoma de Madrid. SOPHIAS integrates eight synchronized sensor streams from high-definition webcams, ambient and webcam audio, eye-tracking glasses, smartwatch physiological sensors, and clicker, keyboard, and mouse interactions. In addition, the dataset includes slides and rubric-based evaluations from teachers, peers, and self-assessments, along with timestamped contextual annotations. The dataset captures presentations conducted in real classroom settings, preserving authentic student behaviors, interactions, and physiological responses. SOPHIAS enables the exploration of relationships between multimodal behavioral and physiological signals and presentation performance, supports the study of peer assessment, and provides a benchmark for developing automated feedback and Multimodal Learning Analytics tools. The dataset is publicly available for research through GitHub and Science Data Bank.

翻译：口头报告能力是高等教育的关键组成部分，然而捕捉真实世界中学生多模态表现的综合数据集仍然稀缺。为填补这一空白，我们提出了SOPHIAS（基于传感器的学生口头报告监测与综合分析系统），这是一个12小时的多模态数据集，包含马德里自治大学65名本科生和硕士生进行的50场口头报告（10-15分钟报告及5-15分钟问答环节）的录制内容。SOPHIAS集成了来自高清网络摄像头、环境与摄像头音频、眼动追踪眼镜、智能手表生理传感器以及答题器、键盘和鼠标交互的八个同步传感器数据流。此外，该数据集还包含演示文稿以及来自教师、同伴和自我评估的基于评分标准的评价，并附有时间戳上下文标注。该数据集捕捉了真实课堂环境中进行的报告，保留了学生真实的行为、互动和生理反应。SOPHIAS支持探索多模态行为与生理信号与报告表现之间的关系，有助于同伴评估研究，并为开发自动化反馈工具和多模态学习分析系统提供了基准。该数据集已通过GitHub和科学数据银行公开供研究使用。