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, Digital technologies as an adaptive learning tool in higher education(EDP Sciences, 2023-01-01); ; Цитирования (OpenAlex) 9Цитирования (OpenCitations) 6 - Некоторые метрики заблокированы вашиминастройками согласия
Тип записи:Публикация, ЦИФРЛЫҚ БІЛІМ БЕРУ ЖАҒДАЙЫНДА БЕЙІМДЕЛГЕН ОҚЫТУДЫҢ ТИІМДІЛІГІ(2023-03-30); - Некоторые метрики заблокированы вашиминастройками согласия
Тип записи:Публикация, THE IMPACT OF GAMIFICATION ON VOCABULARY LEARNING THROUGH MOBILE APPLICATIONS AND ONLINE PLATFORMSThe effectiveness of mobile gamification in enhancing students’ memory and vocabulary acquisition has made it a significant research topic. Traditional vocabulary learning methods often rely on the rote memorization of words and drills, while gamified approaches employ interactive components such as leaderboards, quizzes, rewards and points to heighten the efficiency of learning. This research aims to examine advantages and disadvantages of mobile learning channels and evaluate how gamified vocabulary development techniques stack up against traditional methods of vocabulary learning, as well as identify the most impactful gamification elements.The study conducted a survey among 50 students using an online survey platform (Google Forms). Quantitative data collection techniques and comparison methods were used to investigate the effects of different platforms and game mechanics on vocabulary learning and motivation in students. To find out how various platforms and game mechanics impact vocabulary acquisition and student motivation, quantitative data collection and comparative analysis techniques were employed. A comparison of gamification platforms revealed that Quizlet works well because it uses competitive elements and repeated learning, Duolingo and Memrise use artificial intelligence to implement adaptive learning, Kahoot! and Word Wall are easy to use in the classroom, and LingQ, while focusing on contextual vocabulary learning, lacks gamification features.By demonstrating how game mechanics impact vocabulary acquisition, this study advances the fields of educational technologies and gamification. The study’s findings recommend the best approach, which balances interest and in-depth language comprehension, by combining gamification and structured learning. - 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.