Enhancing E-Learning Adaptability with Automated Learning Style Identification and Sentiment Analysis: A Hybrid Deep Learning Approach for Smart Education

Information, Journal Year: 2024, Volume and Issue: 15(5), P. 277 - 277

Published: May 13, 2024

In smart education, adaptive e-learning systems personalize the educational process by tailoring it to individual learning styles. Traditionally, identifying these styles relies on learners completing surveys and questionnaires, which can be tedious may not reflect their true preferences. Additionally, this approach assumes that are fixed, leading a cold-start problem when automatically based platform behaviors. To address challenges, we propose novel annotates unlabeled student feedback using multi-layer topic modeling implements Felder–Silverman Learning Style Model (FSLSM) identify automatically. Our method involves answering four FSLSM-based questions upon logging into providing personal information like age, gender, cognitive characteristics, weighted fuzzy logic. We then analyze learners’ behaviors activities web usage mining techniques, classifying sequences specific with an advanced deep model. textual latent Dirichlet allocation (LDA) for sentiment analysis enhance experience further. The experimental results demonstrate our outperforms existing models in accurately detecting improves overall quality of personalized content delivery.

Language: Английский

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Enhancing online education recommendations through clustering-driven deep learning

Biomedical Signal Processing and Control, Journal Year: 2024, Volume and Issue: 97, P. 106669 - 106669

Published: Aug. 10, 2024

Language: Английский

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Beyond the Classroom: Understanding the Evolution of Educational Data Mining With Key Route Main Path Analysis

Computer Applications in Engineering Education, Journal Year: 2025, Volume and Issue: 33(2)

Published: Feb. 18, 2025

ABSTRACT Educational data mining (EDM) enhances the educational system by uncovering hidden patterns of academic data. The discipline EDM has grown rapidly and produced numerous publications, leading to knowledge dissemination among researchers. This research aims understand field literature examining citation network significant publications. utilizes a quantitative approach based on main path analysis (MPA) analyze 1009 Web Science (WoS) publications between 1988 2023. study uncovers 22 that have shaped diffusion trajectories EDM. reveals undergone three phases evolution, each which represents substantial shift in focus: automated adaptation, leveraging human decision, advanced predictive analytics. Unlike previous reviews, this applies novel using multiple global MPA, five key sub‐research areas: student performance, early warning, learning behavior, transfer learning, dropout. Notably, recent trends emphasize growing focus performance. primary contribution paper lies its comprehensive mapping EDM's developmental trajectory, offering an understanding diverse trends. By elucidating these emerging areas, not only enriches existing but also identifies unexplored topics can guide future directions, distinguishing itself from other reviews more systematic data‐driven field's evolution.

Language: Английский

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Construction of a multi-modal digital human education platform based on GAN and vision transformer

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: April 28, 2025

With the rapid development of artificial intelligence technology, digital human education platforms have become a research hotspot in education. This paper proposes method to build multi-modal platform based on Generative Adversarial Network and Vision Transformer. The enables high-quality avatar generation interactive learning experiences. In experimental part, we construct large-scale dataset containing 1000 students 50 teachers evaluate performance proposed method. results show that has significantly improved avatars' authenticity, interaction response speed, effect by comparing them with existing platforms. Specifically, average recognition accuracy avatars increased 12%, time been shortened 25%, students' academic 8% average. shows GAN ViT excellent application potential can provide new solutions for future models.

Language: Английский

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Developing a Model to Predict Self-Reported Student Performance during Online Education Based on the Acoustic Environment

Sustainability, Journal Year: 2024, Volume and Issue: 16(11), P. 4411 - 4411

Published: May 23, 2024

In recent years, great developments in online university education have been observed, favored by advances ICT. There are numerous studies on the perception of academic performance classes, influenced aspects a very diverse nature; however, acoustic environment students at home, which can certainly affect activities, has barely evaluated. This study assesses influence home students’ self-reported performance. assessment is performed calculating prediction models using Recursive Feature Elimination method with 40 initial features and following classifiers: Random Forest, Gradient Boosting, Support Vector Machine. The optimal number predictors their relative importance were also was assessed metrics such as accuracy area under receiver operating characteristic curve (ROC_AUC-score). model smallest (with 14 predictors, 9 them about perceived environment) best achieves an 0.7794; furthermore, maximum difference for same algorithm between 33 0.03. Consequently, simplicity ease interpretation, reduced variables preferred.

Language: Английский

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Trends of Social Anxiety in University Students of Pakistan Post-COVID-19 Lockdown: A Healthcare Analytics Perspective

Information, Journal Year: 2024, Volume and Issue: 15(7), P. 373 - 373

Published: June 28, 2024

This paper disseminates our research findings that we conducted on university students in the years 2021, 2022, and 2023, with year 2021 taken as base year. Our mined excavated a concealed prevalence of social anxiety an important crucial facet study Pakistan. Using Liebowitz Social Anxiety Scale (LSAS), found significant increase level among past three after COVID-19 lockdown. data showed ‘very severe anxiety’ soared up to 52.94% 2023 from just 5.98% showing net 47.06%. Statistical analyses demonstrate noteworthy differences overall levels students, reaching significance at 5% discernable upward trend anxiety. We also employed predictive analytics, including binary classifiers generalized linear models 95% confidence interval, identify individuals risk. highlights dynamic shift escalating thus emphasizing its awareness, which is significantly for timely intervention, potentially preventing symptom escalation improving outcomes.

Language: Английский

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Enhancing Advertising Effectiveness Through AIDA, AI, and Data Visualization Integration for Business Strategies

Advances in business information systems and analytics book series, Journal Year: 2024, Volume and Issue: unknown, P. 85 - 102

Published: Sept. 13, 2024

For optimal outcomes, traditional advertising models must be combined with modern technologies as digital marketing evolves. This study improves AIDA, AI, and data visualisation. The examines how AI-driven analytics sophisticated visualisation might boost company advertising. AI-powered customisation increases customer engagement product interest, according to a literature review, campaign analysis, in-depth interviews industry professionals consumers. Data simplifies AI strategy insights. Case studies demonstrate improve Research utilises the AIDA model create targeted, engaging, powerful ads. findings show executives researchers integrated initiatives can conversions audience engagement. promotes hybrid conventional These tactics help firms succeed in ever-changing world.

Language: Английский

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The Impact of Federated Learning on Urban Computing

Journal of Internet Services and Applications, Journal Year: 2024, Volume and Issue: 15(1), P. 380 - 409

Published: Sept. 21, 2024

In an era defined by rapid urbanization and technological advancements, this article provides a comprehensive examination of the transformative influence Federated Learning (FL) on Urban Computing (UC), addressing key challenges, contributions to existing literature. By integrating FL into urban environments, study explores its potential revolutionize data processing, enhance privacy, optimize applications. We delineate benefits challenges implementation, offering insights effectiveness in domains such as transportation, healthcare, infrastructure. Additionally, we highlight persistent including scalability, bias mitigation, ethical considerations. pointing towards promising future directions advancements edge computing, transparency, continual learning models, underscore opportunities further positive impact shaping more adaptable environments.

Language: Английский

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