A Multimodal Dataset of Student Oral Presentations with Sensors and Evaluation Data

[2601.07576] A Multimodal Dataset of Student Oral Presentations with Sensors and Evaluation Data Skip to main content Search Submit Donate Log in Computer Science > Human-Computer Interaction arXiv:2601.07576 (cs) [Submitted on 12 Jan 2026 ( v1 ), last revised 28 Aug 2026 (this version, v2)] Title: A Multimodal Dataset of Student Oral Presentations with Sensors and Evaluation Data Authors: Alvaro Becerra , Ruth Cobos , Roberto Daza View PDF HTML (experimental) Abstract: 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 delivered by 65 undergraduate and master's students at the Universidad Autonoma de Madrid, comprising 46 individual presentations with a mean presentation duration of 9 min 48 s (SD = 33 s) followed by a mean Q&A duration of 6 min 28 s (SD = 3 min 02 s), and 4 group presentations with a mean presentation duration of 14 min 11 s (SD = 1 min 40 s) followed by a mean Q&A duration of 8 min 22 s (SD = 1 min 21 s). SOPHIAS integrates eight timestamped 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 available for research, including both academic and legitimate commercial research and development, under controlled access through Science Data Bank, subject to approval of a Data Usage Agreement (DUA), with code provided through GitHub. Comments: Preprint of an article accepted for publication in Scientific Data. GitHub repository of the dataset at: this https URL Subjects: Human-Computer Interaction (cs.HC) ; Computer Vision and Pattern Recognition (cs.CV) Cite as: arXiv:2601.07576 [cs.HC] (or arXiv:2601.07576v2 [cs.HC] for this version) https://doi.org/10.48550/arXiv.2601.07576 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Alvaro Becerra [ view email ] [v1] Mon, 12 Jan 2026 14:29:05 UTC (2,236 KB) [v2] Fri, 28 Aug 2026 10:12:48 UTC (2,243 KB) Full-text links: Access Paper: View PDF HTML (experimental) TeX Source view license Additional Features Audio Summary Current browse context: cs.HC < prev | next > new | recent | 2026-01 Change to browse by: cs cs.CV References & Citations SciX Google Scholar Semantic Scholar export BibTeX citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. 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