Search Results (24)

Brain–Computer Interfaces (BCIs) have increasingly been explored as tools for monitoring and modulating cognitive processes relevant to learning. However, their application to learning disorders, and especially to mathematical learning difficulties such as dyscalculia and ageometria, remains conceptually promising but empirically underdeveloped. The present study offers a scoping review with structured narrative synthesis of recent empirical research on BCI-based interventions in learning disorder populations, with particular attention paid to their possible translational relevance for mathematical learning. Following PRISMA-ScR principles and a Population–Concept–Context framework, studies published between 2020 and 2025 were identified through database searches in Scopus, IEEE Xplore, and PubMed. A total of 30 studies met the inclusion criteria. All eligible studies focused on Attention-Deficit/Hyperactivity Disorder (ADHD), while no eligible BCI intervention studies were found for dyscalculia or ageometria. The reviewed literature was dominated by EEG-based neurofeedback interventions. To move beyond descriptive summary, the included studies were organized using a structured analytical framework based on intervention modality, primary cognitive target, methodological robustness, and translational proximity to mathematical learning disorders. Across the evidence base, the most consistent findings concerned attention regulation and executive function outcomes, whereas academic and mathematics-related outcomes were sparse and methodologically less developed. Although several studies suggested improvements in domain-general cognitive mechanisms relevant to mathematical learning, the absence of direct evidence in dyscalculia and ageometria prevents confirmatory conclusions. The review therefore identifies both the promise and the limits of current BCI applications in learning disorder contexts and argues that future research should prioritize theory-driven, disorder-specific trials targeting numeracy, visuospatial reasoning, and executive processes in mathematical learning disabilities. Although current findings suggest promising cognitive and educational potential, these technologies are not yet ready for routine implementation in standard classroom environments without further validation, teacher training, ethical safeguards, and cost-effective deployment models. Full article

We survey recent advances in second-language (L2) Mandarin lexical tones research and show how an interpretable computational approach can deliver parameter-aligned feedback across perception–production (P ↔ P). We synthesize four strands: (A) conventional evaluations and tasks (identification, same–different, imitation/read-aloud) that reveal robust tone-pair asymmetries and early P ↔ P decoupling; (B) physiological and behavioral instrumentation (e.g., EEG, eye-tracking) that clarifies cue weighting and time course; (C) audio-only speech analysis, from classic F0 tracking and MFCC–prosody fusion to CNN/RNN/CTC and self-supervised pipelines; and (D) interpretable learning, including attention and relational models (e.g., graph neural networks, GNNs) opened with explainable AI (XAI). Across strands, evidence converges on tones as time-evolving F0 trajectories, so movement, turning-point timing, and local F0 range are more diagnostic than height alone, and the contrast between Tone 2 (rising) and Tone 3 (dipping/low) remains the persistent difficulty; learners with tonal vs. non-tonal language backgrounds weight these cues differently. Guided by this synthesis, we outline a tool-oriented framework that pairs perception and production on the same items, jointly predicts tone labels and parameter targets, and uses XAI to generate local attributions and counterfactual edits, making feedback classroom-ready. Full article

This study examines the efficacy of virtual reality exposure therapy (VRET) in alleviating foreign language anxiety (FLA) among university students. Although research exists on FLA, interventions have relied on self-reporting measures, leaving a gap in understanding physiological indicators and anxiety reduction. While previous research has explored either the therapeutic potential of virtual reality or the neurophysiological correlations of anxiety through electroencephalography (EEG), few have integrated these methodologies within a single experimental framework. This study combined the foreign language classroom anxiety scale (FLCAS) with (EEG) data to capture subjective and neural responses to anxiety in second language (L2) speaking. The participants (n = 20) completed language speaking tasks both before and after VR intervention, which exposed them to anxiety-inducing conditions replicating language challenges. During these tasks, brainwave signals were recorded, focusing on frontal alpha asymmetry (FAA) and alpha power (F3, F4), indicating neural activity associated with stress and emotional regulation. Results showed participants experienced a significant decrease (p = 0.017 < 0.05) in self-reported FLCAS scores after VRET. The reduction in FLA showed a negative correlation with increased alpha power at F3 (r = −0.55, p = 0.012), suggesting a link between left frontal neural regulation and anxiety reduction. These findings underscored VRET’s effectiveness in influencing emotional responses during L2-speaking tasks. Full article

This review systematically examines electroencephalography (EEG) studies on gifted children, focusing on the signal processing pipelines across acquisition, preprocessing, feature extraction, and analysis, and identifying opportunities for methodological standardisation relevant to educational research. Following PRISMA 2020 guidelines, a comprehensive search was carried out in PubMed, Scopus, Web of Science, IEEE Xplore, and PsycINFO. From 197 records, 14 studies met the inclusion criteria and were analysed for EEG setup, preprocessing strategies, and analytical approaches, including event-related potentials, spectral and connectivity measures, and applications of machine learning. Substantial heterogeneity was observed in device configurations, preprocessing practices, and analytical choices, limiting cross-study comparability and the transfer of findings to educational contexts. Nevertheless, recurring neurophysiological markers were identified, such as P300, frontoparietal $\gamma$ synchronisation, and $\theta$–$\alpha$ modulations during cognitive tasks. Only a minority of studies implemented supervised classification methods, suggesting an underexplored potential for advanced data-driven approaches in paediatric EEG. Transparent and standardised EEG pipelines, with explicit reporting of filters, artefact thresholds, and rejection rates, are essential to enhance reproducibility and translational value. By framing EEG signal processing within an educational perspective, this review provides methodological guidance to support early identification, inform classroom practice, and strengthen the bridge between neuroscience and education. Full article

In evaluating classroom teaching quality, students’ attention assessment is a critical indicator in education management, as it holds significant practical value for improving teaching methods and instructional quality. Electroencephalogram (EEG) signals can monitor dynamic neural activity in the brain in real time. Their objectivity and non-invasive nature make them particularly suitable for attention assessment in classroom environments. This article first provides a brief overview of existing attention assessment methods, and then presents a comprehensive review of the current research status and methodologies in EEG-based attention assessment, including signal acquisition, preprocessing, feature extraction and selection, classification, and evaluation. Subsequently, the challenges in EEG-based teaching attention assessment are discussed, including the acquisition of high-quality signals, multimodal data fusion, complexity of data, and hardware setups for deep learning method implementation. Finally, a multimodal classroom attention assessment method, which integrates EEG and eye movement signals, is proposed to enhance teaching management. Full article

Light is a powerful environmental factor with proven effects on human cognitive activity. This study investigated the effects of two types of light—LED with an enhanced long-wavelength spectrum and classic fluorescent—on concentration and attention of undergraduate students. Concentration was assessed through EEG, while attention was evaluated using d2 and TP psychometric tests. The experiment was carried out in a classroom equipped with both lighting systems, with each participant completing two testing sessions under different light conditions, separated by at least seven days to allow for washout. Results showed that during the first administration, LED lighting supported better performance across both EEG and psychometric measures compared to fluorescent light, suggesting enhanced concentration and attention. By the second administration, these differences were less evident, likely due to learning and task familiarization effects. Nonparametric ANOVA-type analyses further indicated that the effect of lighting on performance depended not only on the light type but also on the order of exposure, with students who switched from fluorescent to LED showing improvement, whereas the reverse sequence was associated with a decline. Overall, the findings suggest that LED lighting enriched in warm tones may positively influence attention and concentration, though results should be viewed as exploratory due to the small sample size. Full article

Primary school students, as the main users of classrooms, are directly affected by the lighting environment, which not only affects their visual comfort but also their cognitive performance. This study investigated the effects of different correlated colour temperature (CCT) levels in classroom lighting on the cognitive performance of primary school students based on a multidimensional evaluation combining physiological signals (EEG and EDA) and subjective assessment. In this study, 53 subjects aged 10–13 years old from a primary school in Anshan City were used in a controlled experiment under five CCT conditions (3000 K, 4000 K, 5000 K, 6000 K, and 7000 K) at a constant illumination level of 500 lx. EEG and skin conductance (SC) signals were collected and subjective perceptions of visual comfort and fatigue were assessed while cognitive tasks were carried out. The results showed that students performed best cognitively at a colour temperature of 4000 K, with the lowest EEG absolute power (AP) (p < 0.01) and highest comfort (p < 0.05). Females were more sensitive to colour temperature changes and showed better cognitive performance in cooler colour temperature conditions, while male students performed better in warmer light conditions (p < 0.01). The above findings suggest that optimising the CCT of classroom lighting enhances students’ cognitive functioning and comfort, providing empirical support for lighting design guidelines in educational environments. Full article

Classroom attention is a fundamental cognitive function that is crucial to effective learning and significantly influences academic performance. Recent advances in investigating neural correlates of attention in classroom environments provide insights into underlying neural mechanisms and potentially enhance educational outcomes. This paper presents a scoping review of empirical studies investigating neural activities associated with students’ attention in classroom environments. Based on the 16 studies that we included after systematically searching, five main objectives were identified: (i) examination of neural markers of student attention in classroom environments, (ii) comparison of different learning environments, (iii) comparison of different classroom activities, (iv) data quality examination, and (v) student attention improvement. All selected studies used electroencephalogram (EEG) recording to measure neural activities, primarily using NeuroSky and Emotiv EPOC devices. Researchers measured classroom attention through brain-to-brain synchrony or frequency power. While differences in neural activity across classroom activities were noted, further investigation is needed for consistent results. Most studies focused on university students and had limited sample sizes, though they covered diverse study domains. Overall, while some preliminary results have been identified, there are several concerns regarding the neural measurements of attention used, contradictory findings, lack of verification, and limited sample sizes and techniques. Further studies are recommended to extend our understanding of neural evidence of attention in classroom environments. Full article

The growing implementation of digital platforms and mobile devices in educational environments has generated the need to explore new approaches for evaluating the learning experience beyond traditional self-reports or instructor presence. In this context, the NPFC-Test dataset was created from an experimental protocol conducted at the Experiential Classroom of the Institute for the Future of Education. The dataset was built by collecting multimodal indicators such as neuronal, physiological, and facial data using a portable EEG headband, a medical-grade biometric bracelet, a high-resolution depth camera, and self-report questionnaires. The participants were exposed to a digital test lasting 20 min, composed of audiovisual stimuli and cognitive challenges, during which synchronized data from all devices were gathered. The dataset includes timestamped records related to emotional valence, arousal, and concentration, offering a valuable resource for multimodal learning analytics (MMLA). The recorded data were processed through calibration procedures, temporal alignment techniques, and emotion recognition models. It is expected that the NPFC-Test dataset will support future studies in human–computer interaction and educational data science by providing structured evidence to analyze cognitive and emotional states in learning processes. In addition, it offers a replicable framework for capturing synchronized biometric and behavioral data in controlled academic settings. Full article

The deployment of emotion-recognition technologies expands across healthcare education and gaming sectors to improve human–computer interaction. These systems examine facial expressions together with vocal tone and physiological signals, which include pupil size and electroencephalogram (EEG), to detect emotional states and deliver customized responses. The technology provides benefits through accessibility, responsiveness, and adaptability but generates multiple complex ethical issues. The combination of emotional profiling with biased algorithmic interpretations of culturally diverse expressions and affective data collection without meaningful consent presents major ethical concerns. The increased presence of these systems in classrooms, therapy sessions, and personal devices makes the potential for misuse or misinterpretation more critical. The paper integrates findings from literature review and initial emotion-recognition studies to create a conceptual framework that prioritizes data dignity, algorithmic accountability, and user agency and presents a conceptual framework that addresses these risks and includes safeguards for participants’ emotional well-being. The framework introduces structural safeguards which include data minimization, adaptive consent mechanisms, and transparent model logic as a more complete solution than privacy or fairness approaches. The authors present functional recommendations that guide developers to create ethically robust systems that match user principles and regulatory requirements. The development of real-time feedback loops for user awareness should be combined with clear disclosures about data use and participatory design practices. The successful oversight of these systems requires interdisciplinary work between researchers, policymakers, designers, and ethicists. The paper provides practical ethical recommendations for developing affective computing systems that advance the field while maintaining responsible deployment and governance in academic research and industry settings. The findings hold particular importance for high-stakes applications including healthcare, education, and workplace monitoring systems that use emotion-recognition technology. Full article

This study explored the effectiveness of VR-based social skills training for students with autism and typically developing students with social difficulties. Six autistic students and five typically developing students from upper elementary grades participated in the study. Participants were recruited based on their willingness to participate, ability to follow instructions, and absence of other significant learning or behavioral disorders. Five VR modules were developed, covering scenarios like classrooms, ticket booths, exhibitions, restaurants, and parks. These modules incorporated foundational social settings and more complex scenarios to enhance emotional regulation and adaptive responses, aligned with the 12-year Basic Education Curriculum Guidelines. The intervention took place from May to July 2023, with participants attending six 30–40 min VR sessions once or twice a week. Various assessment tools measured the impact, focusing on social responses, emotion recognition, and reactions to unexpected situations. Results indicated consistent improvements in conversation speed, expression effectiveness, and environmental adaptation. Social Skills Behavior Checklist scores showed significant differences between pre- and post-tests, while EEG data revealed enhanced empathetic responses among autistic students. Typically, developing students shifted from independent problem-solving to seeking social support. This study highlights the potential of VR as an effective tool for social skills development in both groups. Full article

Sensory integration theory (SIT), which posits that the neurological process of integrating sensory information from the environment and one’s body influences learning and behaviour, and the sensory processing measure (SPM), a psychometric tool with versions for individuals aged 4 months to 87 years, are fundamental to understanding and assessing sensory processing. This study examined the existing evidence on the SPM and SIT using scientometric and narrative methods. A search of Scopus and Web of Science Core Collection from 1983 to 2024 yielded 238 unique records after deduplication. Scientometric analysis, conducted with CiteSpace (Version 6.4.R1) and VOSviewer (Version 1.6.19) explored publication trends, keyword co-occurrences, and citation bursts. A narrative method, based on a purposive sample of studies selected by title relevance from the 238 records, provided qualitative insights into key themes and concepts. Scientometric analysis revealed 11 key clusters, including ‘sensory processing behaviour’, ‘classroom context’, and ‘using electroencephalogram (EEG) technology’, reflecting diverse research areas and a growing publication trend, particularly after 2011. A narrative analysis, guided by these clusters, explored sensory processing differences in children with developmental disorders like autism spectrum disorder (ASD) compared to typically developing children, the relationship between sensory processing and other functional areas, the impact of classroom contexts on sensory processing, the use of EEG in sensory processing disorder (SPD) diagnosis, and the effectiveness of interventions like sound-based therapy and sensory integration therapy. The combined approach highlighted the wide application of the SPM and SIT, informing future research directions, such as longitudinal studies, comparative effectiveness research, and cultural adaptations of assessments and interventions. Full article

Electroencephalograms (EEGs) can be used to study the influence of environmental elements on human emotions, cognition, and behavior. EEGs can reveal unconscious responses and fill in the gaps left by subjective responses provided in survey questionnaires or interviews. EEG research on the impact of classroom design elements on concentration and creativity is scarce; the design elements studied have not been diverse enough. In addition, no researchers have examined the brain and subjective responses to multiple indoor environmental elements regarding concentration and creativity. Thus, the purpose of this study was to explore how the human brain responds to different indoor environmental elements as shown by objective EEG signals related to concentration and creativity, and their similarities and differences to subjective self-reported responses. The experimental stimuli included 16 images combining four indoor environmental elements—classroom space shape, furniture arrangement, ceiling height, and color—along with images of white walls, a full-window wall with a view of nature, and a windowless scenario, totaling 19 images. The brainwaves of 20 people collected from eight channels were analyzed to determine the concentration index (CI) for concentration and relative theta (RT) for creativity. As a subjective response, participants were asked to choose the stimuli in which they felt they could best concentrate and be most creative in a self-report format. The results showed the following tendencies: (a) More brainwaves in the parietal and occipital lobes than in the prefrontal or frontal lobes; (b) a higher CI with rectilinear shapes, traditional frontal furniture arrangements, and red walls; (c) a higher RT with curvilinear shapes, collaborative furniture arrangements, white walls, and a full view of nature; and (d) participants selected white walls and a front-facing furniture layout as supportive of concentration and a full view of nature, curvilinear shape, and collaborative furniture layout for creative thinking. The results showed that similarities in brain and subjective responses were related to furniture layout and shape, whereas differences existed in color. This study contributes to the understanding of the characteristics of indoor environments that appear to enhance the manifestation of concentration and creativity. Full article

Classroom EEG recordings classification has the capacity to significantly enhance comprehension and learning by revealing complex neural patterns linked to various cognitive processes. Electroencephalography (EEG) in academic settings allows researchers to study brain activity while students are in class, revealing learning preferences. The purpose of this study was to develop a machine learning framework to automatically classify different learning-style EEG patterns in real classroom environments. Method: In this study, a set of EEG features was investigated, including statistical features, fractal dimension, higher-order spectra, entropy, and a combination of all sets. Three different machine learning classifiers, random forest (RF), K-nearest neighbor (KNN), and multilayer perceptron (MLP), were used to evaluate the performance. The proposed framework was evaluated on the real classroom EEG dataset, involving EEG recordings featuring different teaching blocks: reading, discussion, lecture, and video. Results: The findings revealed that statistical features are the most sensitive feature metric in distinguishing learning patterns from EEG. The statistical features and RF classifier method tested in this study achieved an overall best average accuracy of 78.45% when estimated by fivefold cross-validation. Conclusions: Our results suggest that EEG time domain statistics have a substantial role and are more reliable for internal state classification. This study might be used to highlight the importance of using EEG signals in the education context, opening the path for educational automation research and development. Full article

Students often experience high levels of daily academic pressure, spending extended periods within indoor classroom environments. Windows, as a medium of proximity to nature, play an important role in relieving stress. However, the broader implications of the Window Green View Index (WGVI) on individual well-being remain underexplored. This study aims to assess the effects of WGVI on stress recovery in college students by utilizing virtual reality technology to create five classroom environments with varying WGVI levels: 0%, 25%, 50%, 75%, and 100%. Twenty-four participants were subjected to the Trier Social Stress Test before engaging with the different WGVI scenarios for stress recovery. Both subjective assessments and objective physiological indicators were evaluated. Results indicated that participants exhibited the lowest Profile of Mood States (POMS) score (−4.50) and significantly improved systolic blood pressure recovery at a 25% WGVI level. The examination of EEG data revealed that the O2 channel in the occipital region exhibited the highest level of activity in the alpha frequency range during the experiment. Additionally, a significant association was observed between the EEG measurements and the subjective rating of stress. This study underscores the significance of incorporating WGVI into the design and planning of college buildings to promote mental health and well-being among students. Full article