Enhancing Online Learning by Integrating Biosensors and Multimodal Learning Analytics for Detecting and Predicting Student Behavior: A Review

• URL: http://arxiv.org/abs/2509.07742v1
• Date: Tue, 09 Sep 2025 13:41:40 GMT
• Title: Enhancing Online Learning by Integrating Biosensors and Multimodal Learning Analytics for Detecting and Predicting Student Behavior: A Review
• Authors: Alvaro Becerra, Ruth Cobos, Charles Lang,
• Abstract summary: 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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In modern online learning, understanding and predicting student behavior is crucial for enhancing engagement and optimizing educational outcomes. This systematic review explores the integration of biosensors and Multimodal Learning Analytics (MmLA) to analyze and predict student behavior during computer-based learning sessions. We examine key challenges, including emotion and attention detection, behavioral analysis, experimental design, and demographic considerations in data collection. Our study highlights the growing role of physiological signals, such as heart rate, brain activity, and eye-tracking, combined with traditional interaction data and self-reports to gain deeper insights into cognitive states and engagement levels. We synthesize findings from 54 key studies, analyzing commonly used methodologies such as advanced machine learning algorithms and multimodal data pre-processing techniques. The review identifies current research trends, limitations, and emerging directions in the field, emphasizing the transformative potential of biosensor-driven adaptive learning systems. Our findings suggest that integrating multimodal data can facilitate personalized learning experiences, real-time feedback, and intelligent educational interventions, ultimately advancing toward a more customized and adaptive online learning experience.

Related papers

• GuideAI: A Real-time Personalized Learning Solution with Adaptive Interventions [0.5833117322405447]
  Large Language Models (LLMs) have emerged as powerful learning tools, but they lack awareness of learners' cognitive and physiological states.<n>We introduce GuideAI, a multi-modal framework that enhances LLM-driven learning by integrating real-time biosensory feedback.
  arXiv  Detail & Related papers  (2026-01-28T09:06:45Z)
• The Imperfect Learner: Incorporating Developmental Trajectories in Memory-based Student Simulation [55.722188569369656]
  This paper introduces a novel framework for memory-based student simulation.<n>It incorporates developmental trajectories through a hierarchical memory mechanism with structured knowledge representation.<n>In practice, we implement a curriculum-aligned simulator grounded on the Next Generation Science Standards.
  arXiv  Detail & Related papers  (2025-11-08T08:05:43Z)
• Evolving Machine Learning: A Survey [3.8137023630861315]
  This survey focuses on five core challenges: data drift, concept drift, catastrophic forgetting, skewed learning, and network adaptation.<n>We systematically review over 120 studies, categorizing state-of-the-art methods across supervised, unsupervised, and semi-supervised approaches.<n>Our findings aim to guide researchers and practitioners in developing robust, ethical, and scalable systems for real-world deployment.
  arXiv  Detail & Related papers  (2025-05-23T13:50:02Z)
• Student-AI Interaction in an LLM-Empowered Learning Environment: A Cluster Analysis of Engagement Profiles [28.794946431719392]
  This study explored diverse learner profiles within a multi-agent, LLM-empowered learning environment.<n>Students exhibit varied behavioral, cognitive, and emotional engagement tendencies.
  arXiv  Detail & Related papers  (2025-03-03T16:08:28Z)
• A First Step in Using Machine Learning Methods to Enhance Interaction Analysis for Embodied Learning Environments [4.349901731099916]
  This study aims to simplify researchers' tasks, using Machine Learning and Multimodal Learning Analytics. We present an initial case study to determine the feasibility of visually representing students' states, actions, gaze, affect, and movement on a timeline. The timeline allows us to investigate the alignment of critical learning moments identified by multimodal and interaction analysis.
  arXiv  Detail & Related papers  (2024-05-10T02:40:24Z)
• Towards Automated Knowledge Integration From Human-Interpretable Representations [55.2480439325792]
  We introduce and motivate theoretically the principles of informed meta-learning enabling automated and controllable inductive bias selection.<n>We empirically demonstrate the potential benefits and limitations of informed meta-learning in improving data efficiency and generalisation.
  arXiv  Detail & Related papers  (2024-02-25T15:08:37Z)
• Integrating AI and Learning Analytics for Data-Driven Pedagogical Decisions and Personalized Interventions in Education [0.2812395851874055]
  This research study explores the conceptualization, development, and deployment of an innovative learning analytics tool. By analyzing critical data points such as students' stress levels, curiosity, confusion, agitation, topic preferences, and study methods, the tool provides a comprehensive view of the learning environment. This research underscores AI's role in shaping personalized, data-driven education.
  arXiv  Detail & Related papers  (2023-12-15T06:00:26Z)
• Incomplete Multimodal Learning for Complex Brain Disorders Prediction [65.95783479249745]
  We propose a new incomplete multimodal data integration approach that employs transformers and generative adversarial networks. We apply our new method to predict cognitive degeneration and disease outcomes using the multimodal imaging genetic data from Alzheimer's Disease Neuroimaging Initiative cohort.
  arXiv  Detail & Related papers  (2023-05-25T16:29:16Z)
• Synergistic information supports modality integration and flexible learning in neural networks solving multiple tasks [107.8565143456161]
  We investigate the information processing strategies adopted by simple artificial neural networks performing a variety of cognitive tasks. Results show that synergy increases as neural networks learn multiple diverse tasks. randomly turning off neurons during training through dropout increases network redundancy, corresponding to an increase in robustness.
  arXiv  Detail & Related papers  (2022-10-06T15:36:27Z)
• Co-Located Human-Human Interaction Analysis using Nonverbal Cues: A Survey [71.43956423427397]
  We aim to identify the nonverbal cues and computational methodologies resulting in effective performance. This survey differs from its counterparts by involving the widest spectrum of social phenomena and interaction settings. Some major observations are: the most often used nonverbal cue, computational method, interaction environment, and sensing approach are speaking activity, support vector machines, and meetings composed of 3-4 persons equipped with microphones and cameras, respectively.
  arXiv  Detail & Related papers  (2022-07-20T13:37:57Z)
• Assessing Human Interaction in Virtual Reality With Continually Learning Prediction Agents Based on Reinforcement Learning Algorithms: A Pilot Study [6.076137037890219]
  We investigate how the interaction between a human and a continually learning prediction agent develops as the agent develops competency. We develop a virtual reality environment and a time-based prediction task wherein learned predictions from a reinforcement learning (RL) algorithm augment human predictions. Our findings suggest that human trust of the system may be influenced by early interactions with the agent, and that trust in turn affects strategic behaviour.
  arXiv  Detail & Related papers  (2021-12-14T22:46:44Z)
• Human-Robot Collaboration and Machine Learning: A Systematic Review of Recent Research [69.48907856390834]
  Human-robot collaboration (HRC) is the approach that explores the interaction between a human and a robot. This paper proposes a thorough literature review of the use of machine learning techniques in the context of HRC.
  arXiv  Detail & Related papers  (2021-10-14T15:14:33Z)
• Nonparametric Estimation of Heterogeneous Treatment Effects: From Theory to Learning Algorithms [91.3755431537592]
  We analyze four broad meta-learning strategies which rely on plug-in estimation and pseudo-outcome regression. We highlight how this theoretical reasoning can be used to guide principled algorithm design and translate our analyses into practice.
  arXiv  Detail & Related papers  (2021-01-26T17:11:40Z)

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.