EduGage: Methods and Dataset for Sensor-Based Momentary Assessment of Engagement in Self-Guided Video Learning

• URL: http://arxiv.org/abs/2605.01238v1
• Date: Sat, 02 May 2026 04:29:16 GMT
• Title: EduGage: Methods and Dataset for Sensor-Based Momentary Assessment of Engagement in Self-Guided Video Learning
• Abstract summary: We develop and evaluate a system for engagement estimation, compare different sensing modalities, and analyze the feasibility and effectiveness of multimodal modeling for characterizing learner engagement.<n>Our results suggest that fine-grained engagement estimation is feasible but inherently noisy, and that practical systems should prioritize lightweight combinations of behavioral and physiological signals over full multimodal instrumentation.
• Score: 6.377791358687315
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Engagement, which links to attentional, emotional, and cognitive dimensions, plays an important role in learning. In online and video-based learning environments, learners often need to regulate their own interactions with instructional materials. Measuring and reflecting on engagement can therefore support both learners and adaptive learning systems. In this study, we use wearable and camera-based sensing devices to collect physiological and motion signals, including PPG, ECG, EDA, EEG, IMU, heart rate, temperature, and eye-tracking data, to estimate learner engagement. We conducted a user study with 16 participants in a video-based learning scenario, where participants completed learning tasks and provided repeated in-situ self-reports of engagement through brief probes. We develop and evaluate a system for engagement estimation, compare different sensing modalities, and further analyze the feasibility and effectiveness of multimodal modeling for characterizing learner engagement. Across participant-based cross-validation, our model achieves an MAE of 0.81, 83.75% within-1 accuracy, 73.93% binary accuracy, and 68.45% binary Macro-F1, outperforming sensor-free, statistical, deep temporal, foundation-model, and LLM-based baselines. Our results suggest that fine-grained engagement estimation is feasible but inherently noisy, and that practical systems should prioritize lightweight combinations of behavioral and physiological signals over full multimodal instrumentation. We release the EduGage dataset, including synchronized multimodal sensor signals, probe-aligned momentary engagement labels, video metadata, quizzes, and study materials, to support reproducible research on fine-grained sensor-based engagement modeling in self-guided learning.

Related papers

• Single-Channel EEG-Based Cognitive Load Assessment in Online Learning: A Hybrid Deep Learning Approach [0.0]
  We study whether a single-channel, consumer-grade EEG device can distinguish easy from difficult educational-video content.<n>We implement a hybrid CNN+LSTM+Attention model that combines the raw waveform with band-power features.
  arXiv  Detail & Related papers  (2026-07-02T07:13:01Z)
• Edu-Theater: A Data-Efficient Agent Framework for Scalable Learner Behavior Simulation through Staging Roll-Call [47.34713494064664]
  Edu-Theater enables scalable future behavior simulation without the need for dense per-learner histories.<n>Edu-Theater achieves higher simulation accuracy with significantly fewer LLM calls.
  arXiv  Detail & Related papers  (2026-06-13T09:48:25Z)
• Real-Time Multimodal Data Collection Using Smartwatches and Its Visualization in Education [0.0]
  This paper presents Watch-DMLT, a data acquisition application for Fitbit Sense 2 smartwatches, and ViSeDOPS, a dashboard-based visualization system for analyzing synchronized multimodal data collected during oral presentations.<n>We report on a classroom deployment involving 65 students and up to 16 smartwatches, where data streams including heart rate, motion, gaze, video, and contextual annotations were captured and analyzed. Results demonstrate the feasibility and utility of the proposed system for supporting fine-grained, scalable, and interpretable Multimodal Learning Analytics in real learning environments.
  arXiv  Detail & Related papers  (2025-12-02T11:12:46Z)
• From Physics to Machine Learning and Back: Part II - Learning and Observational Bias in PHM [52.64097278841485]
  Review examines how incorporating learning and observational biases through physics-informed modeling and data strategies can guide models toward physically consistent and reliable predictions.<n>Fast adaptation methods including meta-learning and few-shot learning are reviewed alongside domain generalization techniques.
  arXiv  Detail & Related papers  (2025-09-25T14:15:43Z)
• Enhancing Online Learning by Integrating Biosensors and Multimodal Learning Analytics for Detecting and Predicting Student Behavior: A Review [0.0]
  This systematic review explores the integration of biosensors and Multimodal Learning Analytics.<n>Our study highlights the growing role of physiological signals, such as heart rate, brain activity, and eye-tracking.<n>The review identifies current research trends, limitations, and emerging directions in the field.
  arXiv  Detail & Related papers  (2025-09-09T13:41:40Z)
• Improving Out-of-distribution Human Activity Recognition via IMU-Video Cross-modal Representation Learning [3.177649348456073]
  Human Activity Recognition (HAR) based on wearable inertial sensors plays a critical role in remote health monitoring.<n>We propose a new cross-modal self-supervised pretraining approach to learn representations from large-sale unlabeled IMU-video data.<n>Our results indicate that the proposed cross-modal pretraining approach outperforms the current state-of-the-art IMU-video pretraining approach.
  arXiv  Detail & Related papers  (2025-07-17T18:47:46Z)
• Supervised Contrastive Learning for Ordinal Engagement Measurement [2.166000001057538]
  Student engagement plays a crucial role in the successful delivery of educational programs.<n>This paper identifies two key challenges in this problem: class imbalance and incorporating order into engagement levels.<n>A novel approach to video-based student engagement measurement in virtual learning environments is proposed.
  arXiv  Detail & Related papers  (2025-05-27T03:49:45Z)
• Uncovering Student Engagement Patterns in Moodle with Interpretable Machine Learning [0.0]
  This study introduces a methodology for quantifying engagement across an entire module using virtual learning environment (VLE) activity log data.<n>Using study session frequency, immediacy, and diversity, we create a cumulative engagement metric.<n>In a case study of a computing module at University College London's Department of Statistical Science, we examine how delivery methods impact student behaviour.
  arXiv  Detail & Related papers  (2024-12-16T14:47:25Z)
• Analyzing Participants' Engagement during Online Meetings Using Unsupervised Remote Photoplethysmography with Behavioral Features [50.82725748981231]
  Engagement measurement finds application in healthcare, education, services. Use of physiological and behavioral features is viable, but impracticality of traditional physiological measurement arises due to the need for contact sensors. We demonstrate the feasibility of the unsupervised photoplethysmography (rmography) as an alternative for contact sensors.
  arXiv  Detail & Related papers  (2024-04-05T20:39:16Z)
• Responsible Active Learning via Human-in-the-loop Peer Study [88.01358655203441]
  We propose a responsible active learning method, namely Peer Study Learning (PSL), to simultaneously preserve data privacy and improve model stability. We first introduce a human-in-the-loop teacher-student architecture to isolate unlabelled data from the task learner (teacher) on the cloud-side. During training, the task learner instructs the light-weight active learner which then provides feedback on the active sampling criterion.
  arXiv  Detail & Related papers  (2022-11-24T13:18:27Z)
• Motivating Learners in Multi-Orchestrator Mobile Edge Learning: A Stackelberg Game Approach [54.28419430315478]
  Mobile Edge Learning enables distributed training of Machine Learning models over heterogeneous edge devices. In MEL, the training performance deteriorates without the availability of sufficient training data or computing resources. We propose an incentive mechanism, where we formulate the orchestrators-learners interactions as a 2-round Stackelberg game.
  arXiv  Detail & Related papers  (2021-09-25T17:27:48Z)
• Relational Graph Learning on Visual and Kinematics Embeddings for Accurate Gesture Recognition in Robotic Surgery [84.73764603474413]
  We propose a novel online approach of multi-modal graph network (i.e., MRG-Net) to dynamically integrate visual and kinematics information. The effectiveness of our method is demonstrated with state-of-the-art results on the public JIGSAWS dataset.
  arXiv  Detail & Related papers  (2020-11-03T11:00:10Z)
• Semantics-aware Adaptive Knowledge Distillation for Sensor-to-Vision Action Recognition [131.6328804788164]
  We propose a framework, named Semantics-aware Adaptive Knowledge Distillation Networks (SAKDN), to enhance action recognition in vision-sensor modality (videos) The SAKDN uses multiple wearable-sensors as teacher modalities and uses RGB videos as student modality.
  arXiv  Detail & Related papers  (2020-09-01T03:38:31Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.