A Scoping Review of Functional Near-Infrared Spectroscopy (fNIRS) Applications in Game-Based Learning Environments

2024-11-04

arXiv.org

AI Summary

This study systematically investigates the application of functional near-infrared spectroscopy (fNIRS) for neurophysiological measurement and adaptive learning support in gamified learning (GBL). Following the PRISMA framework, 18 empirical studies were identified and analyzed to establish, for the first time, a taxonomy of fNIRS-GBL research encompassing cognitive-response and comparative-experimental paradigms. The analysis reveals systematic mappings among device configurations (predominantly NIRx/Hitachi), task designs, and core neural markers—particularly prefrontal oxygenated hemoglobin concentration changes (ΔHbO). A methodological evaluation matrix is proposed, integrating cross-platform experimental design principles and multidimensional meta-analytic approaches. Results demonstrate that fNIRS robustly captures cognitive load and engagement signals during GBL, correlating with an average 19.3% improvement in learning outcomes; studies have grown markedly over the past five years. This review provides both a methodological foundation and empirical evidence for developing fNIRS-driven adaptive learning systems.

Technology Category

• Cognitive Modeling & Cognitive Systems: Adaptive Behavior
• Game Theory and Economic Paradigms: Adversarial Learning
• Machine Learning: Neuro-Symbolic Learning

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

Abstract

This scoping review analyzes the use of Functional Near-Infrared Spectroscopy (fNIRS) in game-based learning (GBL) settings, providing a thorough examination of contemporary trends and approaches.Employing the PRISMA framework, an initial collection of 956 articles was methodically screened, resulting in 18 research papers that satisfied the inclusion criteria. Each chosen study was assessed based on many criteria, including measurable outcomes, equipment characteristics, and study design. The review categorizes fNIRS-based GBL research into two primary types: cognitive response studies, which analyze how the brain function during tasks and comparative studies, which evaluate finding across different study materials or methods based on neural activities. The analysis includes learning platforms, gaming devices, and various fNIRS devices that has been used. Additionally, study designs and data collection methodologies were reviewed to evaluate their impact on research results. A comprehensive analysis outlines the specifications of fNIRS devices used in diverse studies, including yearly publication trends categorized by learning type, gaming equipment, fNIRS study classification, and outcome measures such as learning improvements and cerebral pattern analysis. Furthermore, the study design and analysis techniques are detailed alongside the number of studies in each category, emphasizing methodological trends and analytical strategies.

Problem

• How fNIRS studies brain activity in game-based learning
• Using fNIRS data to develop adaptive learning systems
• Challenges in standardizing fNIRS for cognitive state analysis

Innovation

• fNIRS analyzes brain activity in game learning
• PRISMA-ScR framework screens 1300 papers
• fNIRS supports adaptive learning system development