Mind the Student: Behavioral and Contextual Cues for Automated Engagement Prediction in Online Learning

• URL: http://arxiv.org/abs/2608.24340v1
• Date: Tue, 25 Aug 2026 10:03:07 GMT
• Title: Mind the Student: Behavioral and Contextual Cues for Automated Engagement Prediction in Online Learning
• Abstract summary: We develop a framework that integrates implicit features extracted from video, audio, and image encoders along with structured behavioral modalities like head pose, gaze, facial action units, emotion, and wavelet-based audio features.<n>We employ evidential regression and spectral-normalized Gaussian process classification heads for uncertainty-aware prediction to improve and calibration robustness.
• Score: 9.31020526280198
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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 reliable prediction requires bringing together different types of behavioral signals as well as expressive cues. Through our analysis of the CASED dataset, it is clear that engagement prediction gets even harder due to the high inter-person variability as well as the subjectivity of the engagement annotation. To tackle these challenges, we develop a multimodal framework that integrates the implicit spatiotemporal features extracted from pretrained video, audio, and image encoders along with structured behavioral modalities like head pose, gaze, facial action units, emotion, and wavelet-based audio features. We integrate these modalities via a Perceiver IO latent bottleneck. Moreover, student and instructor personalities are modeled as variational posteriors over learnable embeddings to enable partial pooling across participants. We employ evidential regression and spectral-normalized Gaussian process classification heads for uncertainty-aware prediction to further improve robustness and calibration. Benchmark on the CASED challenge test set shows that all participating methods converge near random-chance performance, revealing the difficulty of the dataset. In this highly ambiguous regime, our framework achieves competitive performance while uniquely offering well-calibrated uncertainty metrics, demonstrating that reliable risk-quantification is an essential prerequisite for deploying engagement models in real-world educational tools.

Related papers

• Uncertainty-Aware and Decoder-Aligned Learning for Video Summarization [0.0]
  Video summarization aims to produce a compact representation of a long video by selecting a subset of temporally important segments.<n>This task is inherently difficult due to strong annotation subjectivity and the reliance on discrete decoding procedures.<n>We propose VASTSum, an uncertainty-aware and decoder-aligned learning framework for video summarization.
  arXiv  Detail & Related papers  (2026-05-10T12:31:02Z)
• Explicit Uncertainty Modeling for Active CLIP Adaptation with Dual Prompt Tuning [51.99383151474742]
  We propose a robust uncertainty modeling framework for active CLIP adaptation based on dual-prompt tuning.<n>We show that our method consistently outperforms existing active learning methods under the same annotation budget.
  arXiv  Detail & Related papers  (2026-02-04T09:01:55Z)
• Heterogeneous Uncertainty-Guided Composed Image Retrieval with Fine-Grained Probabilistic Learning [49.28548464288051]
  Composed Image Retrieval (CIR) enables image search by combining a reference image with modification text.<n>In intrinsic noise in CIR triplets incurs intrinsic uncertainty and threatens the model's robustness.<n>This paper introduces a Heterogeneous Uncertainty-Guided (HUG) paradigm to overcome these limitations.
  arXiv  Detail & Related papers  (2026-01-16T16:05:49Z)
• Imitation Learning for Combinatorial Optimisation under Uncertainty [1.0781866671930855]
  This paper introduces a systematic taxonomy of experts for IL optimisation under uncertainty.<n>Experts are classified along three dimensions: (i) their treatment of uncertainty, including myopic, deterministic, full-information, two-stage, and multi-stage formulations; (ii) their level of optimality, distinguishing task-optimal and approximate experts; and (iii) their interaction mode with the learner, ranging from one-shot supervision to iterative, interactive schemes.
  arXiv  Detail & Related papers  (2026-01-08T21:16:25Z)
• Pseudo-D: Informing Multi-View Uncertainty Estimation with Calibrated Neural Training Dynamics [3.8836478189829164]
  One-hot labels erase inter-rater variability and force models to make overconfident predictions.<n>We introduce a novel framework that brings uncertainty back into the label space.
  arXiv  Detail & Related papers  (2025-09-15T11:30:12Z)
• Stochastic Encodings for Active Feature Acquisition [100.47043816019888]
  Active Feature Acquisition is an instance-wise, sequential decision making problem.<n>The aim is to dynamically select which feature to measure based on current observations, independently for each test instance.<n>Common approaches either use Reinforcement Learning, which experiences training difficulties, or greedily maximize the conditional mutual information of the label and unobserved features, which makes myopic.<n>We introduce a latent variable model, trained in a supervised manner. Acquisitions are made by reasoning about the features across many possible unobserved realizations in a latent space.
  arXiv  Detail & Related papers  (2025-08-03T23:48:46Z)
• MIRRAMS: Learning Robust Tabular Models under Unseen Missingness Shifts [2.5357049657770516]
  Missing values often reflect variations in data collection policies, which may shift across time or locations.<n>Such shifts in the missingness distribution between training and test inputs pose a significant challenge to achieving robust predictive performance.<n>We propose a novel deep learning framework designed to address this challenge, particularly in the common yet challenging scenario where the test-time dataset is unseen.
  arXiv  Detail & Related papers  (2025-07-11T03:03:30Z)
• Self-Distilled Disentangled Learning for Counterfactual Prediction [49.84163147971955]
  We propose the Self-Distilled Disentanglement framework, known as $SD2$. Grounded in information theory, it ensures theoretically sound independent disentangled representations without intricate mutual information estimator designs. Our experiments, conducted on both synthetic and real-world datasets, confirm the effectiveness of our approach.
  arXiv  Detail & Related papers  (2024-06-09T16:58:19Z)
• Uncertainty-guided Boundary Learning for Imbalanced Social Event Detection [64.4350027428928]
  We propose a novel uncertainty-guided class imbalance learning framework for imbalanced social event detection tasks. Our model significantly improves social event representation and classification tasks in almost all classes, especially those uncertain ones.
  arXiv  Detail & Related papers  (2023-10-30T03:32:04Z)
• Hierarchical Decomposition of Prompt-Based Continual Learning: Rethinking Obscured Sub-optimality [55.88910947643436]
  Self-supervised pre-training is essential for handling vast quantities of unlabeled data in practice. HiDe-Prompt is an innovative approach that explicitly optimize the hierarchical components with an ensemble of task-specific prompts and statistics. Our experiments demonstrate the superior performance of HiDe-Prompt and its robustness to pre-training paradigms in continual learning.
  arXiv  Detail & Related papers  (2023-10-11T06:51:46Z)

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.