Жарияланымдар / Публикации
Постоянный URI для этого сообществаhttps://cris.qyzpu.kz/handle/123456789/2
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- Некоторые метрики заблокированы вашиминастройками согласия
Тип записи:Публикация, A Multi-Modal Deep Learning Framework for Student Soft Skills Development in Adaptive Learning Environments(Science and Information Organization, 2026-01-01); ; Nuraim IbragimovaThe rapid evolution of artificial intelligence and adaptive educational technologies has created increasing demand for intelligent systems capable of automatically assessing and enhancing student soft skills within digital learning environments. This study proposes MST-SoftNet, a multimodal transformer-based deep learning framework designed for adaptive soft skill assessment and personalized educational recommendation. The proposed architecture integrates heterogeneous educational modalities, including textual interactions, speech signals, facial expressions, behavioral engagement patterns, performance indicators, and feedback information, within a unified hierarchical transformer fusion framework. Modality-specific encoders, cross-modal attention mechanisms, explainable attention visualization modules, and adaptive recommendation components were incorporated to improve both predictive performance and interpretability. Experimental evaluation was conducted using multiple baseline deep learning architectures, including CNN, LSTM, Transformer, and multimodal CNN-LSTM models. The proposed MST-SoftNet framework achieved superior performance across all evaluation metrics, attaining 93.67% accuracy, 92.11% F1-score, and 96.05% AUC, while simultaneously demonstrating reduced inference latency and improved computational efficiency. Attention visualization analysis further confirmed the capability of the framework to identify semantically meaningful multimodal behavioral patterns associated with communication, collaboration, leadership, creativity, emotional intelligence, and self-regulation competencies. Longitudinal adaptive learning experiments additionally demonstrated substantial improvement in student soft skill progression over time. The obtained results indicate that MST-SoftNet establishes a robust, interpretable, and scalable foundation for next-generation intelligent educational systems focused on personalized soft skill development and adaptive learning optimization. - Некоторые метрики заблокированы вашиминастройками согласия
Тип записи:Публикация, Fostering emotional intelligence and self-regulation in gifted high school students: experimental evidence from a soft skills development program(Frontiers Media, 2026-01-12)Цитирования (OpenAlex) 7