Integrating ARCS-V and MST motivation models into AI-supported distance education design: A synergistic approach

Açıköğretim Uygulamaları ve Araştırmaları Dergisi, Journal Year: 2025, Volume and Issue: 11(1), P. 38 - 61

Published: Jan. 28, 2025

This article proposes a new framework that integrates the ARCS-V (Attention, Relevance, Confidence, Satisfaction, and Volition) model Motivational Systems Theory (MST) into AI-supported distance learning environments. The proposed shows how integration of these models can support student motivation in more holistic way. By combining AI tools with assessment, adaptive interventions synergistic mechanisms, customized environments be developed according to needs. Combining strengths model, which focuses on providing engaging satisfying experiences, MST, emphasizes importance personal goals, emotions, environmental factors, this approach suggests effective way sustain motivation. paper examines MST combined intervention dimensions Artificial Intelligence education settings. integrating two motivational ODL AI, not only presentation content but also increased engagement achieved.

Language: Английский

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Internet Use for Creative Purposes and Its Correlation with Perceived Usefulness, Computer Anxiety, and Emotional Intelligence: The Intermediary Effect of the Perceived Ease of Use

Behavioral Sciences, Journal Year: 2025, Volume and Issue: 15(2), P. 221 - 221

Published: Feb. 16, 2025

Although researchers have shown great interest in the antecedents and consequences of internet use due to becoming a part daily life, there is gap literature regarding factors that affect teachers' for creative purposes. This study aims empirically examine purposes explores its relationship with emotional intelligence, computer anxiety, perceived ease internet. Furthermore, possible intermediary effect on creativity among these variables also investigated. In this context, data were obtained from 264 teachers Marmara Region Turkey using survey method. To test hypothesized relationships, structural equation modeling was conducted. Findings revealed anxiety has negative creativity, while usefulness, positive effect. Our results supported partial mediating role relationships between intelligence as well usefulness full creativity. Therefore, research extends understanding technology acceptance by linking two. Moreover, findings provide important information shape educational policies or professional development programs basis digital education offer different approach educators.

Language: Английский

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Relationships between interactive network patterns and students’ cognitive and emotional processes: Evidence from the individual and group levels

Computers & Education, Journal Year: 2025, Volume and Issue: unknown, P. 105309 - 105309

Published: March 1, 2025

Language: Английский

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A comprehensive approach to enhance emotion recognition through advanced feature extraction and Attention

Biomedical Signal Processing and Control, Journal Year: 2025, Volume and Issue: 107, P. 107860 - 107860

Published: March 29, 2025

Language: Английский

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Exploring the prospects of multimodal large language models for Automated Emotion Recognition in education: Insights from Gemini

Computers & Education, Journal Year: 2025, Volume and Issue: unknown, P. 105307 - 105307

Published: March 1, 2025

Language: Английский

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A Systematic Review on Artificial Intelligence-Based Multimodal Dialogue Systems Capable of Emotion Recognition

Multimodal Technologies and Interaction, Journal Year: 2025, Volume and Issue: 9(3), P. 28 - 28

Published: March 14, 2025

In the current context, use of technologies in applications for multimodal dialogue systems with computers and emotion recognition through artificial intelligence continues to grow rapidly. Consequently, it is challenging researchers identify gaps, propose new models, increase user satisfaction. The objective this study explore analyze potential based on incorporating recognition. methodology used selecting papers accordance PRISMA identifies 13 scientific articles whose research proposals are generally focused convolutional neural networks (CNNs), Long Short-Term Memory (LSTM), GRU, BERT. results proposed models as Mindlink-Eumpy, RHPRnet, Emo Fu-Sense, 3FACRNNN, H-MMER, TMID, DKMD, MatCR. datasets DEAP, MAHNOB-HCI, SEED-IV, SEDD-V, AMIGOS, DREAMER. addition, metrics achieved by presented. It concluded that such H-MMER obtain outstanding results, their accuracy ranging from 92.62% 98.19%, TMID scene-aware model BLEU4 values 51.59% 29%, respectively.

Language: Английский

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Explainable evaluation framework for facial expression recognition in web-based learning environments

International Journal of Machine Learning and Cybernetics, Journal Year: 2024, Volume and Issue: unknown

Published: Oct. 21, 2024

Language: Английский

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How Does Social Support Detected Automatically in Discussion Forums Relate to Online Learning Burnout? The Moderating Role of Students’ Self-Regulated Learning

Computers & Education, Journal Year: 2024, Volume and Issue: unknown, P. 105213 - 105213

Published: Dec. 1, 2024

Language: Английский

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Towards Integrating Automatic Emotion Recognition in Education: A Deep Learning Model Based on 5 EEG Channels

International Journal of Computational Intelligence Systems, Journal Year: 2024, Volume and Issue: 17(1)

Published: Sept. 2, 2024

In a technologically advanced world, artificial intelligence has impacted all fields of activity. The augmentation online learning by means emotion recognition systems raises new challenges in terms obtaining high-performance and interpreting the results. paper aims to investigate usage automated develop deep model based on physiological data recognize emotions often encountered classrooms. So, an 1D-CNN is used seven emotions: boredom, confusion, frustration, curiosity, excitement, concentration, anxiety. These are described according PAD 5 EEG signals, FP1, AF3, F7, T7, FP2, taken from DEAP dataset train test convolutional neural network model. high accuracy we obtained (i.e. boredom—99.64%, confusion—99.70%, frustration—99.66%, curiosity—99.80%, excitement—99.91%, concentration—99.70%, anxiety—99.21%) proves that use signals via only five channels sufficient presence emotions. Furthermore, improved method analysis LIME proposed obtain reliable explanations for predictions our

Language: Английский

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Predicting Cognitive States Through Mouse Micromovement Analysis

Published: Jan. 1, 2024

Language: Английский

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