arXiv:2608. 07188v1 Announce Type: new Abstract: SetEasy optimizes classroom engagement in fixed seating grids.
arXiv:2605.01238v2 Announce Type: replace-cross Abstract: Engagement, which links to attentional, emotional, and cognitive dimensions, plays an important role in learning. In online and video-based l...
The paper tackles the challenge of predicting student engagement from online tutoring videos, noting that engagement is a complex, multidimensional construct influenced by behavioral, emotional, and cognitive states. By analyzing the CASED dataset, the authors highlight the difficulty posed by high inter‑person variability and subjective annotations. They propose a multimodal framework that fuses implicit spatiotemporal features from pretrained video, audio, and image encoders with structured behavioral cues such as head pose, gaze, facial action units, emotion, and wavelet‑based audio features, integrating them via a Perceiver IO bottleneck and modeling participant personalities with variational posteriors. The system employs evidential regression and spectral‑normalized Gaussian process classification heads to provide uncertainty‑aware predictions, achieving competitive performance on the CASED challenge test set while offering well‑calibrated uncertainty metrics.
The prediction of student engagement from the online tutoring videos is difficult because engagement is a multidimensional construct comprising distinct behavioral, emotional, and cognitive states. A...
The paper presents a modality‑agnostic, hierarchical Transformer framework for assessing cognitive workload using heterogeneous biosignals. In a pilot study, the authors evaluated all 31 combinations of five modalities (ECG, EDA, RESP, SpO₂, EEG) across three tasks (IQ, MATH, GAME) and found that EEG alone performed best, while adding more modalities did not consistently improve results. The full five‑modality model achieved the highest average accuracy (73.02% on IQ, 68.08% overall) and reduced model size by about 50% compared to late‑fusion approaches.
arXiv:2601.07576v2 Announce Type: replace-cross Abstract: Oral presentation skills are a critical component of higher education, yet comprehensive datasets capturing real-world student performance ac...
Automated classroom engagement recognition holds substantial promise for scalable learning analytics, yet the suitability of modern Vision-Language Models (VLMs) for this task under zero-shot conditions remains largely unexplored. We present a systematic benchmark that evaluates five widely-used VLMs: CLIP, BLIP-VQA, GPT-4o, LLaVA-1.
arXiv:2606. 00752v1 Announce Type: new Abstract: Autonomous Sensory Meridian Response (ASMR) is a somatosensory phenomenon characterized by pleasant tingling sensations and cardiovascular slowing.
arXiv:2503. 06525v2 Announce Type: replace-cross Abstract: Analyzing student behavior in educational scenarios is crucial for enhancing teaching quality and student engagement.
arXiv:2606. 15890v1 Announce Type: new Abstract: Understanding urban wellbeing from multimodal data requires integrating heterogeneous spatial and temporal signals, posing significant challenges for current multimodal large language models (MLLMs).
arXiv:2609.39238v1 Announce Type: new Abstract: An agent that moves must recognise a place from a viewpoint it has never seen. We introduce 4MT-VLM, a dataset of procedurally generated landscapes, ea...
arXiv:2610.28405v1 Announce Type: new Abstract: Recent advances in multimodal large language models (MLLMs) have substantially improved spoken interaction and video understanding. Existing online and...
The study examined whether ultra‑brief naturalistic concern text could complement wearable sensor data for monitoring student health. Over a year, 458 university students provided three‑word responses to a bi‑monthly prompt while wearing Oura rings, and the researchers compared different NLP methods to link these brief texts to nine sleep and physical activity outcomes. Findings indicated that weeks with academic‑concern framing correlated with lower physical activity, while emotional exhaustion language correlated with poorer sleep quality and lower heart rate variability, suggesting that affective content may add value to passive physiological monitoring.